Expose omni backbone extension contract

ARTIFACT_GUIDE.md

@@ -10,9 +10,10 @@ Xperience-native pretraining goal.
 
 | Artifact | Why to open it first |
 | --- | --- |
-| [`PROJECT_STATUS.md`](PROJECT_STATUS.md) | Gives the fastest current-state table: implemented, in staging, and outside current scope. |
+| [`PROJECT_STATUS.md`](PROJECT_STATUS.md) | Gives the fastest current-state table: implemented, being improved, and outside current scope. |
 | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md) | Shows the roadmap from public-sample task development to multi-episode data preparation, Qwen3-Omni LoRA, robustness runs, model branches, and the future native-pretraining goal. |
 | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md) | Explains which foundation backbones fit which Xperience-10M objective: Qwen3-Omni first, Cosmos 3 for world modeling, and VLA/policy models after action-target conversion. |
+| [`OMNI_MODEL_EXTENSION_CONTRACT.md`](OMNI_MODEL_EXTENSION_CONTRACT.md) | Defines the shared manifest, split, evaluation, packaging, and public-safety contract that future Qwen, Cosmos-style, and VLA/policy branches must satisfy. |
 | [`ADDITIONAL_DEVELOPMENT_DIRECTIONS.md`](ADDITIONAL_DEVELOPMENT_DIRECTIONS.md) | Records concrete non-backbone development tracks: taxonomy, benchmark protocol, representation learning, skill graphs, affordances, 3D/4D memory, QA, and policy transfer. |
 | [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) | Describes the future full-corpus Xperience Embodied Foundation Model goal, including modules, objectives, staged scale-up, hardware ranges, and evaluation. |
 | [`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md) | Defines the task unit, chronological split, metrics, leakage controls, and current limitations. |
@@ -105,10 +106,23 @@ research project.
 
 | Artifact | Current status |
 | --- | --- |
-| [`results/omni_finetune/DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md) | Summarizes the staging requirement before the held-out Qwen3-Omni pilot can report metrics. |
+| [`results/omni_finetune/DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md) | Summarizes the data-readiness checks required before a held-out Qwen3-Omni pilot can report metrics. |
 | [`results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`](results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md) | Documents the public multi-episode access path, selected 128-episode pilot plan, and data requirements. |
+| [`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json) | Compact verified summary for the first selected-episode Qwen3-Omni diagnostic pilot, including split counts, held-out metrics, and the quality-target caveat. |
+| [`results/omni_finetune/verified_public/`](results/omni_finetune/verified_public/) | Public-safe verified held-out result packages. These include metrics, predictions, reports, manifests, training metadata, validation summaries, and audit files, but not raw data or weights. |
 | [`scripts/omni/discover_xperience10m_sources.py`](scripts/omni/discover_xperience10m_sources.py) | Discovery gate for valid multi-episode Xperience-10M sources. |
 | [`scripts/omni/train_qwen3_omni_lora.py`](scripts/omni/train_qwen3_omni_lora.py) | Training entrypoint for the Qwen3-Omni LoRA pilot after the data gate passes. |
+| [`scripts/omni/run_128_fullsplit_parallel_export_8gpu.sh`](scripts/omni/run_128_fullsplit_parallel_export_8gpu.sh) | Full 96/16/16 launcher with parallel export, 8-process LoRA training, validation-sample monitoring, held-out test evaluation, and quality-target reporting. |
+| [`scripts/omni/merge_qwen3_omni_eval_shards.py`](scripts/omni/merge_qwen3_omni_eval_shards.py) | Recomputes held-out metrics from deterministic Qwen eval shards and checks missing or duplicate prediction ids. |
+| [`scripts/omni/package_verified_omni_result.py`](scripts/omni/package_verified_omni_result.py) | Creates a contract-driven public-safe package from validated held-out fine-tuning outputs without raw data, base weights, adapter/checkpoint weights, full checkpoints, or large archives. |
+| [`scripts/omni/audit_verified_omni_package.py`](scripts/omni/audit_verified_omni_package.py) | Audits a verified package before README, website, or Hugging Face updates by checking validation status, required files, primary metrics, held-out evidence, and forbidden file types. |
+| [`scripts/omni/watch_verified_omni_package.py`](scripts/omni/watch_verified_omni_package.py) | Waits for a passing held-out eval validation and then runs the verified public-safe packager automatically. |
+| [`OMNI_MODEL_EXTENSION_CONTRACT.md`](OMNI_MODEL_EXTENSION_CONTRACT.md) | Human-readable contract for adding new model families while preserving the same episode split, held-out evaluation, packaging gate, and public-safety boundary. |
+| [`configs/omni_backbones/`](configs/omni_backbones/) | Backbone registry for implemented Qwen3-Omni LoRA plus planned Cosmos-style world-model and VLA/policy branches. |
+| [`scripts/omni/backbone_registry.py`](scripts/omni/backbone_registry.py) | Validates each backbone contract, required metrics, required files, split policy, and forbidden public package categories. |
+| [`scripts/omni/export_model_neutral_window_index.py`](scripts/omni/export_model_neutral_window_index.py) | Converts Qwen JSONL records into a model-neutral window index that future Cosmos-style and policy/VLA exporters can consume. |
+| [`scripts/omni/smoke_test_backbone_packaging.py`](scripts/omni/smoke_test_backbone_packaging.py) | Runs synthetic package-contract checks for every configured backbone, including Qwen3-Omni, Cosmos-style world modeling, and VLA/policy branches. |
+| [`scripts/omni/scaffold_omni_backbone.py`](scripts/omni/scaffold_omni_backbone.py) | Creates a validated planned-backbone config from an existing contract template so new model branches inherit split, artifact, and publication rules. |
 | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md) | Adds the post-data-gate backbone selection plan: Qwen3-Omni first, Cosmos 3 for world modeling, and OpenVLA/openpi/GR00T for policy/action branches. |
 | [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json) | Machine-readable model-family registry with source links, entry conditions, and evaluation additions. |
 | [`ADDITIONAL_DEVELOPMENT_DIRECTIONS.md`](ADDITIONAL_DEVELOPMENT_DIRECTIONS.md) | Concise reader-facing plan for non-backbone tracks that can be built from Xperience-10M data. |

OMNI_MODEL_EXTENSION_CONTRACT.md

@@ -0,0 +1,246 @@
+# Omni Model Extension Contract
+
+This project uses one shared Xperience-10M data spine and separate backbone
+adapters. Qwen3-Omni is the first implemented fine-tuning path; future
+Cosmos-style world models and VLA/policy models should plug into the same
+manifest, split, artifact, and evaluation discipline.
+
+## Shared Pipeline
+
+Every trainable branch should keep these stages:
+
+1. **Episode selection:** choose complete Xperience-10M episodes before export.
+2. **Episode split:** split by episode/session, not by adjacent windows.
+3. **Manifest guard:** record every episode id, path, split, size, and missing
+   modality before training.
+4. **Backbone export:** convert raw windows into the model-specific sample
+   format.
+5. **Training:** save model config, adapter config, progress JSONL, and
+   checkpoint path.
+6. **Held-out evaluation:** evaluate on test episodes only after training.
+7. **Run report:** write metrics, predictions, confusion matrices or
+   task-specific scoring files, and skipped-episode reasons.
+8. **Long-run observability:** stream `progress.jsonl` and
+   `predictions.partial.jsonl` during evaluation so multi-hour held-out runs can
+   be monitored and resumed without changing the final metric definitions.
+
+The current 128-episode pilot uses a fixed `96/16/16` train/val/test split by
+episode.
+
+## Backbone Registry
+
+Backbone contracts live in:
+
+```text
+configs/omni_backbones/
+```
+
+Inspect them with:
+
+```bash
+python scripts/omni/backbone_registry.py --validate --json
+```
+
+Create a new planned backbone config 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
+```
+
+Current contracts:
+
+| Backbone | Status | Purpose |
+| --- | --- | --- |
+| `qwen3_omni_lora` | implemented | Structured episode-understanding JSON QA over video/audio/text plus sensor bridge features |
+| `cosmos_world_model` | planned adapter | Future-window and action-conditioned world modeling |
+| `policy_vla_branch` | planned adapter | Observation-to-action or motion-policy training after action-space conversion |
+
+## Model-Neutral Window Index
+
+The Qwen exporter produces model-ready JSONL records. To avoid tying future
+branches to Qwen chat-message formatting, convert those records into a
+backbone-neutral window index:
+
+```bash
+python scripts/omni/export_model_neutral_window_index.py \
+  --dataset-jsonl results/omni_finetune/<run_id>_dataset/dataset.jsonl
+```
+
+This writes:
+
+- `window_index.jsonl`
+- `window_index_manifest.json`
+
+Each neutral record keeps the same episode split and window boundaries, then
+separates:
+
+- media paths,
+- sensor feature pointers,
+- language context,
+- JSON supervision,
+- Qwen, Cosmos-style, and policy/VLA adapter views.
+
+Future exporters should consume this neutral index when possible, then add only
+the model-specific target conversion that they need.
+
+## Artifact Contract
+
+Every backbone config must declare an `artifact_contract` with:
+
+- `checkpoint_gate`: the model-specific checkpoint validation rule,
+- `required_training_files`: files that prove training state and configuration,
+- `required_eval_files`: files that prove held-out evaluation outputs,
+- `public_package_allowed`: small derived artifacts that may be published,
+- `public_package_forbidden`: raw data, weights, checkpoints, or large files
+  that must stay out of public packages.
+
+`scripts/omni/backbone_registry.py --validate --json` checks that the contract
+exists for Qwen, Cosmos-style, and policy/VLA branches. The validator and
+public-safe packager read `required_eval_files`, `primary_metrics`, and
+publication rules from the selected backbone config. Export, training, and
+evaluation code still remain model-specific, but the final validation and
+publication gate follows the same contract for every future branch.
+
+The registry validation also enforces the minimum held-out evidence surface:
+episode-level `train`/`val`/`test` split defaults, a leakage guard,
+`held_out_episode_count`, `metrics.json`, a JSONL prediction file,
+`RUN_REPORT.md`, training metadata, progress logs, and explicit forbidden
+artifact categories for raw data, model weights, checkpoints, and archives.
+
+## Qwen3-Omni Contract
+
+Qwen3-Omni consumes:
+
+- rendered multi-camera mosaic video,
+- extracted MP4 audio,
+- language prompt and label options,
+- optional sensor-bridge summaries/features.
+
+It predicts strict JSON:
+
+```json
+{
+  "action": "string",
+  "subtask": "string",
+  "objects": ["string"],
+  "contact": "string",
+  "transition": "string",
+  "next_action": "string",
+  "evidence_window": {"start_frame": 0, "end_frame": 0}
+}
+```
+
+Implemented entrypoints:
+
+- `scripts/omni/parallel_export_qwen3_omni_action_dataset.py`
+- `scripts/omni/train_qwen3_omni_lora.py`
+- `scripts/omni/eval_qwen3_omni_lora.py`
+- `scripts/omni/watch_omni_train_then_eval.py`
+- `scripts/omni/run_128_fullsplit_parallel_export_8gpu.sh`
+
+The watcher is the current post-training gate runner. For the Qwen3-Omni LoRA
+branch it waits for `progress.jsonl` to end in `complete`, checks the PEFT LoRA
+safetensors shapes, runs the training validator, runs a held-out eval smoke,
+then runs the full held-out test evaluation.
+
+The Qwen evaluator writes partial predictions during inference and finalizes the
+same `predictions.jsonl`, `predictions.csv`, `metrics.json`,
+`confusion_matrix.csv`, and `RUN_REPORT.md` files after all selected held-out
+windows finish. A restarted eval can resume from the partial prediction file.
+For faster held-out evaluation, the Qwen evaluator can also run deterministic
+sample shards via `--sample-offset` and `--sample-stride`. Sharded outputs must
+be merged with `scripts/omni/merge_qwen3_omni_eval_shards.py`, which recomputes
+the final metrics from combined predictions and checks missing or duplicate
+sample ids.
+
+Future model families can reuse the same wait/eval sequence only if their
+checkpoint artifact has a compatible gate. Otherwise they should provide a
+model-specific checkpoint check and evaluator, while keeping the same episode
+split and held-out reporting discipline.
+
+## Cosmos-Style World Model Contract
+
+Cosmos-style work should not reuse the JSON QA exporter as-is. It needs a
+future-window exporter with samples shaped like:
+
+```json
+{
+  "episode_id": "session__ep",
+  "split": "train",
+  "context_window": {"start_frame": 0, "end_frame": 119},
+  "target_window": {"start_frame": 120, "end_frame": 179},
+  "conditioning": {
+    "video": "path-or-latent",
+    "audio": "path-or-features",
+    "pose": "feature path",
+    "depth": "feature path",
+    "mocap": "feature path",
+    "imu": "feature path",
+    "language": "task context"
+  },
+  "target": {
+    "future_video": "path-or-latent",
+    "future_sensor_features": "path",
+    "transition": "label"
+  }
+}
+```
+
+Minimum evaluators:
+
+- future retrieval MRR / recall@5,
+- temporal consistency,
+- feature reconstruction error,
+- transition/contact prediction,
+- qualitative generated or retrieved examples.
+
+Cosmos-style checkpoints are not LoRA adapters by default. Their post-training
+gate should verify generated latent/video checkpoints, model config, scheduler
+state, and future-window evaluator outputs instead of using the Qwen LoRA
+safetensors check.
+
+## VLA / Policy Contract
+
+Policy branches need an explicit action target before training. A valid sample
+must state whether the target is an action class, next action, hand trajectory,
+contact event, retargeted humanoid action, or robot-compatible action token.
+
+The first policy exporter should save:
+
+- observation media/features,
+- language instruction or task context,
+- action target,
+- action normalization metadata fit on train episodes only,
+- target provenance from the original annotation/mocap/contact fields.
+
+Minimum evaluators:
+
+- action or next-action accuracy,
+- contact accuracy,
+- trajectory MPJPE when trajectories are used,
+- object-affordance F1,
+- held-out episode count and leakage check.
+
+Policy checkpoints should additionally save the action-space definition,
+normalization statistics, and retargeting/conversion metadata. These must be
+fit from train episodes only and validated before any held-out policy metrics
+are reported.
+
+## Non-Negotiable Invariants
+
+- Do not train on held-out test episodes.
+- Do not report model quality without predictions and metrics from held-out
+  episodes.
+- Do not redistribute raw gated MP4, HDF5, RRD, full checkpoint, or full model
+  weight files.
+- Do not treat a smoke run or one-episode overfit run as a real held-out model
+  result.
+- Record skipped episodes with reasons instead of silently dropping them.

PROJECT_STATUS.md

@@ -23,6 +23,7 @@ scale-up readiness; it is not presented as final full-dataset model quality.
 | Research takeaways | Verified | `RESEARCH_TAKEAWAYS.md`, `docs/data/research_takeaways.json`, `scripts/build_research_takeaways.py` | The main result interpretation is generated from committed metrics: chronological class shift, neural gains on dynamics/order/alignment, open retrieval/reconstruction problems, and the need for held-out episodes. |
 | Research roadmap | Current | `RESEARCH_ROADMAP.md`, `docs/data/research_roadmap.json` | The roadmap connects public-sample task development to the verified validation-aware Qwen3-Omni diagnostic baseline, structured-output improvement pass, robustness runs, world/policy branches, and the future Xperience-native pretraining goal. |
 | Foundation-model plan | Current | `FOUNDATION_MODEL_PLAN.md`, `docs/data/foundation_model_plan.json` | Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is added as the first world-model/action-generation branch; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit. |
+| Omni model extension contract | Current | `OMNI_MODEL_EXTENSION_CONTRACT.md`, `configs/omni_backbones/`, `scripts/omni/backbone_registry.py`, `scripts/omni/smoke_test_backbone_packaging.py` | Future model branches must keep the same episode split discipline, held-out metrics, validation gate, public-safe package contract, and explicit forbidden-artifact policy before reporting results. |
 | Xperience Embodied Foundation Model | Future goal | `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` | A future full-corpus pretraining plan describes target modules, objectives, staged scale-up, hardware ranges, and evaluation for a domain-specific embodied foundation model. |
 | Evaluation protocol | Verified | `EVALUATION_PROTOCOL.md`, `docs/data/evaluation_protocol.json`, `scripts/build_evaluation_protocol.py` | Windowing, chronological split, per-task metrics, leakage controls, and current limitations are generated from committed metric artifacts. |
 | Dataset context | Verified | `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`, official Xperience-10M and sample cards | The README and dashboard distinguish the public sample used here from the gated full dataset used for the selected multi-episode pilot. |
@@ -43,17 +44,20 @@ scale-up readiness; it is not presented as final full-dataset model quality.
    the path from public-sample task work to multi-episode modeling.
 5. Inspect `FOUNDATION_MODEL_PLAN.md` and
    `docs/data/foundation_model_plan.json` before choosing a backbone branch.
-6. Inspect `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` for the
+6. Inspect `OMNI_MODEL_EXTENSION_CONTRACT.md` and run
+   `python scripts/omni/backbone_registry.py --validate --json` before adding
+   a new Qwen, Cosmos-style, or VLA/policy branch.
+7. Inspect `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` for the
    long-term full-corpus pretraining goal.
-7. Inspect `docs/data/summary_metrics.json` and
+8. Inspect `docs/data/summary_metrics.json` and
    `results/episode_task_suite/neural_mlp/` to check the 12-task outputs.
-8. Inspect `results/audio_ablation/AUDIO_ABLATION_SUMMARY.md` before judging
+9. Inspect `results/audio_ablation/AUDIO_ABLATION_SUMMARY.md` before judging
    whether audio helps the current task suite.
-9. Inspect `EVALUATION_PROTOCOL.md` before judging task metrics or leakage
+10. Inspect `EVALUATION_PROTOCOL.md` before judging task metrics or leakage
    controls.
-10. Inspect `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md` only if you need the
+11. Inspect `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md` only if you need the
    detailed upstream dataset-card context.
-11. Inspect `docs/data/omni_finetune_verified_result.json` before judging the
+12. Inspect `docs/data/omni_finetune_verified_result.json` before judging the
    Qwen3-Omni diagnostic pilot.
 
 ## Current Reading Notes
@@ -73,5 +77,8 @@ scale-up readiness; it is not presented as final full-dataset model quality.
 - Foundation-model selection is now explicit: Qwen3-Omni is the immediate
   trainable pilot, Cosmos 3 is the first world-model branch, and policy models
   such as OpenVLA/openpi/GR00T wait for action-target conversion.
+- Future model branches should be added through the backbone registry and
+  verified package contract, not by creating one-off result folders with
+  incompatible metrics or publication rules.
 - The Xperience Embodied Foundation Model is a future native-pretraining goal,
   not a completed model or current benchmark.

configs/omni_backbones/cosmos_world_model.json

@@ -0,0 +1,100 @@
+{
+  "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",
+  "split_policy": {
+    "unit": "episode",
+    "default_counts": {
+      "train": 96,
+      "val": 16,
+      "test": 16
+    },
+    "leakage_guard": "future windows must remain inside the same episode and test episodes must never condition training"
+  },
+  "modalities": {
+    "direct_inputs": [
+      "camera video streams or rendered mosaics",
+      "language task context"
+    ],
+    "conditioning_inputs": [
+      "pose and SLAM trajectory",
+      "depth and confidence",
+      "mocap or action labels",
+      "IMU acceleration and gyro",
+      "audio event cues"
+    ],
+    "targets": [
+      "future visual window",
+      "future latent state",
+      "future sensor-feature window",
+      "transition or contact event"
+    ],
+    "excluded_inputs": [
+      "visualization.rrd"
+    ]
+  },
+  "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": [
+    "future_retrieval_mrr",
+    "future_retrieval_recall_at_5",
+    "temporal_consistency",
+    "feature_reconstruction_error",
+    "transition_accuracy",
+    "contact_accuracy",
+    "held_out_episode_count"
+  ],
+  "artifact_contract": {
+    "checkpoint_gate": "world_model_checkpoint_and_generation_config",
+    "required_eval_files": [
+      "metrics.json",
+      "future_predictions.jsonl",
+      "retrieval_rankings.csv",
+      "temporal_consistency.csv",
+      "qualitative_examples.json",
+      "RUN_REPORT.md"
+    ],
+    "required_training_files": [
+      "training_metadata.json",
+      "progress.jsonl",
+      "model_config.json",
+      "checkpoint_manifest.json"
+    ],
+    "public_package_allowed": [
+      "metrics",
+      "future-window prediction summaries",
+      "retrieval rankings",
+      "temporal consistency tables",
+      "qualitative example metadata",
+      "episode and dataset manifests",
+      "validation summaries"
+    ],
+    "public_package_forbidden": [
+      "raw MP4",
+      "annotation HDF5",
+      "Rerun RRD",
+      "generated raw video unless explicitly licensed and size-bounded",
+      "base-model weights",
+      "full checkpoints",
+      "large archives"
+    ]
+  },
+  "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."
+  ]
+}

configs/omni_backbones/policy_vla_branch.json

@@ -0,0 +1,97 @@
+{
+  "id": "policy_vla_branch",
+  "display_name": "VLA / Policy Model Branch",
+  "status": "planned_adapter",
+  "model_family": "OpenVLA, openpi, GR00T, Octo, and related policy models",
+  "default_model_id": null,
+  "local_model_env": "POLICY_MODEL_DIR",
+  "dataset_contract": "xperience10m_observation_action_v0",
+  "training_objective": "observation_to_action_or_motion_policy",
+  "split_policy": {
+    "unit": "episode",
+    "default_counts": {
+      "train": 96,
+      "val": 16,
+      "test": 16
+    },
+    "leakage_guard": "action targets and normalization statistics must be fit on train episodes only"
+  },
+  "modalities": {
+    "observations": [
+      "egocentric video",
+      "language instruction or task context",
+      "optional depth/pose/mocap/IMU state"
+    ],
+    "candidate_targets": [
+      "action label",
+      "next action",
+      "hand trajectory chunk",
+      "contact state",
+      "retargeted body or humanoid action",
+      "robot-compatible action token"
+    ],
+    "excluded_inputs": [
+      "visualization.rrd"
+    ]
+  },
+  "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": [
+    "action_accuracy",
+    "next_action_accuracy",
+    "contact_accuracy",
+    "trajectory_mpjpe",
+    "object_affordance_f1",
+    "held_out_episode_count"
+  ],
+  "artifact_contract": {
+    "checkpoint_gate": "policy_checkpoint_action_space_and_normalizer",
+    "required_eval_files": [
+      "metrics.json",
+      "policy_predictions.jsonl",
+      "trajectory_metrics.csv",
+      "action_confusion_matrix.csv",
+      "retargeting_audit.json",
+      "RUN_REPORT.md"
+    ],
+    "required_training_files": [
+      "training_metadata.json",
+      "progress.jsonl",
+      "action_space.json",
+      "normalization_stats.json",
+      "checkpoint_manifest.json"
+    ],
+    "public_package_allowed": [
+      "metrics",
+      "policy prediction summaries",
+      "trajectory metric tables",
+      "action confusion matrices",
+      "action-space definitions",
+      "normalization metadata",
+      "retargeting audit summaries",
+      "validation summaries"
+    ],
+    "public_package_forbidden": [
+      "raw MP4",
+      "annotation HDF5",
+      "Rerun RRD",
+      "private retargeting source files",
+      "base-model weights",
+      "full checkpoints",
+      "large archives"
+    ]
+  },
+  "extension_requirements": [
+    "Define an explicit action space before policy fine-tuning.",
+    "Implement target conversion from human egocentric motion to policy-compatible action tokens or trajectories.",
+    "Fit action normalizers on train episodes only and save them with the run manifest.",
+    "Add policy evaluation that separates classification, trajectory, and retargeting metrics."
+  ]
+}

configs/omni_backbones/qwen3_omni_lora.json

@@ -0,0 +1,93 @@
+{
+  "id": "qwen3_omni_lora",
+  "display_name": "Qwen3-Omni LoRA",
+  "status": "implemented",
+  "model_family": "Qwen3-Omni",
+  "default_model_id": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+  "local_model_env": "MODEL_DIR",
+  "dataset_contract": "xperience10m_episode_json_qa_v1",
+  "training_objective": "structured_episode_understanding_json_qa",
+  "split_policy": {
+    "unit": "episode",
+    "default_counts": {
+      "train": 96,
+      "val": 16,
+      "test": 16
+    },
+    "leakage_guard": "episode_id and path-qualified session split checks"
+  },
+  "modalities": {
+    "direct_inputs": [
+      "multi-camera rendered mosaic video",
+      "AAC audio extracted from fisheye_cam0 MP4",
+      "language prompt and label options"
+    ],
+    "bridged_inputs": [
+      "depth and confidence",
+      "pose and SLAM trajectory",
+      "body and hand mocap",
+      "IMU acceleration and gyro",
+      "object/contact summary features"
+    ],
+    "excluded_inputs": [
+      "visualization.rrd"
+    ]
+  },
+  "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": "scripts/omni/train_qwen3_omni_lora.py",
+    "eval": "scripts/omni/eval_qwen3_omni_lora.py",
+    "watch": "scripts/omni/watch_omni_train_then_eval.py",
+    "launcher": "scripts/omni/run_128_fullsplit_parallel_export_8gpu.sh",
+    "upload": "scripts/omni/upload_qwen3_omni_lora_to_hf.py",
+    "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": "lora_safetensors_shape_check",
+    "required_eval_files": [
+      "metrics.json",
+      "predictions.jsonl",
+      "predictions.csv",
+      "per_class_metrics.csv",
+      "confusion_matrix.csv",
+      "RUN_REPORT.md"
+    ],
+    "required_training_files": [
+      "training_metadata.json",
+      "progress.jsonl",
+      "adapter_config.json",
+      "adapter_model.safetensors"
+    ],
+    "public_package_allowed": [
+      "metrics",
+      "predictions",
+      "confusion matrices",
+      "run reports",
+      "episode and dataset manifests",
+      "training metadata",
+      "validation summaries"
+    ],
+    "public_package_forbidden": [
+      "raw MP4",
+      "annotation HDF5",
+      "Rerun RRD",
+      "base-model weights",
+      "LoRA adapter weights",
+      "full checkpoints",
+      "large archives"
+    ]
+  },
+  "extension_requirements": []
+}

data/artifact_index.json

@@ -1,11 +1,12 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-04T21:22:15+00:00",
+  "generated_at_utc": "2026-06-06T14:35:42+00:00",
   "status": "pass",
-  "artifact_count": 77,
+  "artifact_count": 83,
   "missing": [],
   "by_kind": {
     "project_path": 14,
+    "scaleup_contract": 6,
     "project_scope": 1,
     "source_alignment": 5,
     "publication_workflow": 3,
@@ -40,8 +41,8 @@
       "surface": "repo_hf",
       "shows": "Gives first-pass readers a concise project shape before the detailed artifact trail.",
       "exists": true,
-      "bytes": 3594,
-      "sha256": "8df692f2d6b092097d5ef7d5039e7529abe2fd0a80ff3fb23fa14260fb5f9de2"
+      "bytes": 3829,
+      "sha256": "3d16e700c31aafe889b3d8e43d52250208766809c483a716a13cbd26961b3e72"
     },
     {
       "id": "project_brief_json",
@@ -51,8 +52,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable first-reader project brief for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 3637,
-      "sha256": "0e859cfd3fca3ac66c9dd67e04a404ab41757c248d8777ee9cf7da63ab11a2f7"
+      "bytes": 3752,
+      "sha256": "4bd2f3471d0141abf64a2327541e25031e9efa39940cc68a29c316cf36f7c884"
     },
     {
       "id": "project_status",
@@ -62,8 +63,8 @@
       "surface": "repo_hf",
       "shows": "Gives a compact current-state table for first-pass readers.",
       "exists": true,
-      "bytes": 7207,
-      "sha256": "7baaba976ccc254da1a03ee2653057d1e08f3fb0c0cad035886c362442828720"
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+      "sha256": "5eb48d489da7f005baab233a94c9d6b209eb1e9ffdb138c8e0e600ece9239a29"
     },
     {
       "id": "project_status_json",
@@ -73,8 +74,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable copy of the current project status for website and HF mirrors.",
       "exists": true,
-      "bytes": 9874,
-      "sha256": "600c95726eae3404127a8b2110f35468ff2ba02943cae0fbcd3ea43c66109d3e"
+      "bytes": 10977,
+      "sha256": "2bb0639c137dfd6eddd337eb909292543ae2e72753dee398f8240ff35f6a3984"
     },
     {
       "id": "research_roadmap",
@@ -84,8 +85,8 @@
       "surface": "repo_hf",
       "shows": "Defines the path from public-sample task development to multi-episode held-out evaluation and larger omni-model extensions.",
       "exists": true,
-      "bytes": 10259,
-      "sha256": "6e26acb051a45a5afe36fa0c8b3c60f58c383c2e58bee6e01bac28b7dd604f0a"
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+      "sha256": "4e3451a2248176e78b0587a8a15ead2df682068d0833a21e624ac955c5ae72a3"
     },
     {
       "id": "research_roadmap_json",
@@ -95,8 +96,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable research roadmap for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 7699,
-      "sha256": "69b777b4d01e2e587236c1bb2206a47e2b9a0e1a5f1c7c6e125b0dd3270ce0d5"
+      "bytes": 8981,
+      "sha256": "92f94edda5337aa0f49e136748320cb1c2c013b7a12b5fe36cf556f5b1bb0142"
     },
     {
       "id": "foundation_model_plan",
@@ -117,8 +118,74 @@
       "surface": "website_hf",
       "shows": "Machine-readable foundation-model selection matrix with source links, entry conditions, and evaluation additions.",
       "exists": true,
-      "bytes": 12981,
-      "sha256": "9cce52025a2e2f8afb4660e2af3353aea6ad0a1af380849218dd74c0acc271bb"
+      "bytes": 13112,
+      "sha256": "0850a9b74f2c62aba30b297089ad0a4a04424cc16d4a265f71e39538c6d22792"
+    },
+    {
+      "id": "omni_model_extension_contract",
+      "title": "Omni model extension contract",
+      "path": "OMNI_MODEL_EXTENSION_CONTRACT.md",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Defines the shared manifest, episode split, held-out evaluation, packaging, and public-safety rules for Qwen3-Omni, Cosmos-style, and VLA/policy model branches.",
+      "exists": true,
+      "bytes": 8900,
+      "sha256": "c4e51d0aa7536045c229418603a67c6b3c5f31c9d756ca7395cb0c9455f0ed6d"
+    },
+    {
+      "id": "omni_backbone_registry_configs",
+      "title": "Omni backbone registry configs",
+      "path": "configs/omni_backbones",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Stores the implemented Qwen3-Omni LoRA contract and planned Cosmos-style world-model and VLA/policy branch contracts.",
+      "exists": true,
+      "file_count": 3,
+      "bytes": 8904
+    },
+    {
+      "id": "omni_backbone_registry_validator",
+      "title": "Omni backbone registry validator",
+      "path": "scripts/omni/backbone_registry.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Validates backbone ids, split defaults, leakage guards, required metrics, required files, and forbidden public package categories.",
+      "exists": true,
+      "bytes": 8740,
+      "sha256": "3bc9eeb47471fb995f767d5420a556a95413c1cb2d8b3ea9d2805429df55beb8"
+    },
+    {
+      "id": "omni_model_neutral_window_index_exporter",
+      "title": "Model-neutral window index exporter",
+      "path": "scripts/omni/export_model_neutral_window_index.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Converts Qwen JSONL records into a model-neutral window index with Qwen, Cosmos-style, and policy/VLA adapter views.",
+      "exists": true,
+      "bytes": 8540,
+      "sha256": "ec6dd4fcdc1c9a90566a59ef118f7ed5b78ebf8843752873aea8ae8b5f71a34d"
+    },
+    {
+      "id": "omni_backbone_scaffolder",
+      "title": "Omni backbone scaffolder",
+      "path": "scripts/omni/scaffold_omni_backbone.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Creates a validated planned-backbone config from an existing contract template so new model families inherit the shared rules.",
+      "exists": true,
+      "bytes": 4809,
+      "sha256": "2cd34a7db1510392c6caba735fb903b2a6b78aeff8a748ebf20bcd372b0f8f2d"
+    },
+    {
+      "id": "omni_backbone_packaging_smoke",
+      "title": "Omni backbone packaging smoke test",
+      "path": "scripts/omni/smoke_test_backbone_packaging.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Builds synthetic verified packages for every configured backbone and audits them against the public-safe package contract.",
+      "exists": true,
+      "bytes": 6519,
+      "sha256": "a3773fc681e298325e2be80556d6be6e7e30b90ba22ee24b66633f07ff9c4ea4"
     },
     {
       "id": "additional_development_directions",
@@ -128,8 +195,8 @@
       "surface": "repo_hf",
       "shows": "Records concrete non-backbone Xperience-10M development tracks: taxonomy, benchmark protocol, representation learning, skill graphs, affordances, 3D/4D memory, QA, and policy transfer.",
       "exists": true,
-      "bytes": 3136,
-      "sha256": "9766e5537d7ec5a78548d52ea618443dae8e811bb68c4cb8361be0dba7136935"
+      "bytes": 3137,
+      "sha256": "cb4077bcd7e2b33efdd0306c7cb1f28dec547c71739fd52f7233218f90c54941"
     },
     {
       "id": "additional_development_directions_json",
@@ -139,8 +206,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable additional development directions for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 6120,
-      "sha256": "ec918f6849ca888e314a6481e10ddab68d88ba766cbe6a97265df6891058e1df"
+      "bytes": 6121,
+      "sha256": "4458c5b82062aed8c19bcc914f795ec217114422e9b6d54b624371c4b3c8681f"
     },
     {
       "id": "xperience_embodied_foundation_pretraining",
@@ -172,8 +239,8 @@
       "surface": "website_hf",
       "shows": "Gives a short project path with scope status and public surfaces.",
       "exists": true,
-      "bytes": 7648,
-      "sha256": "2a609a6d7bdfcddab225468d226fb5eed8d66bb90616850eae86aab9e9c3b945"
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+      "sha256": "40964c04cd769970e212288dc61a2a462c68ef5e4d962a7b9f50fe5a1fc84ce7"
     },
     {
       "id": "artifact_guide",
@@ -183,8 +250,8 @@
       "surface": "repo_hf",
       "shows": "Gives the human-readable map from project scope to data, tasks, platform mirrors, and scale-up status.",
       "exists": true,
-      "bytes": 12275,
-      "sha256": "7b4db6893ee0d13db32216f20a8287ffa442764cfd20475e1dc3b17c8e10a974"
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+      "sha256": "a9ad335b82c35a5ac102428663ffae1c8798e90e45cc5e795c3a499b4563b417"
     },
     {
       "id": "official_dataset_card_alignment",
@@ -249,8 +316,8 @@
       "surface": "repo_hf",
       "shows": "Publishes prepared Space, artifact dataset, and model bundles, including an explicit model-binary upload batch.",
       "exists": true,
-      "bytes": 15901,
-      "sha256": "60f27a4d32c48e6960445bfafe917ce30d6a43211153146a52ae068f051de4e8"
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+      "sha256": "7b3e515763ccce08f72b4fd12a903c21f14b469c6af144524196ecad945da2ab"
     },
     {
       "id": "github_package_dockerfile",
@@ -282,8 +349,8 @@
       "surface": "repo_hf",
       "shows": "Defines the window unit, chronological split, task metrics, leakage controls, and current limitations.",
       "exists": true,
-      "bytes": 5879,
-      "sha256": "a1eb4c2c43c47da84ec6225bddd6f3c43d0d30aef0781f6682102cf9a34b47e1"
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+      "sha256": "b0dce21ec27228e49693c1aefed1be120c73f80551d6d3609c48c473d792e709"
     },
     {
       "id": "evaluation_protocol_json",
@@ -293,8 +360,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable protocol generated from committed task metrics for website and HF mirrors.",
       "exists": true,
-      "bytes": 13606,
-      "sha256": "e1c729bfba5105db35035b09e3c3d2ae74375dc75dc6577d8bd79fd7801be0a6"
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     },
     {
       "id": "evaluation_protocol_builder",
@@ -304,8 +371,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the protocol from committed summary metrics and task artifacts.",
       "exists": true,
-      "bytes": 16148,
-      "sha256": "c7aa188da50c77ad85c4a6a8be2342111a5b8206c6c2d39e9737fb02e0016f3d"
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     {
       "id": "research_takeaways",
@@ -315,8 +382,8 @@
       "surface": "repo_hf",
       "shows": "Summarizes the main research lessons from committed metrics and identifies which experiments need held-out episodes.",
       "exists": true,
-      "bytes": 5006,
-      "sha256": "7eb264fe323a6a4bef9fed9e2104d0eb3977f38e9b152d1e5bbf5246ebb1aa8a"
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     {
       "id": "research_takeaways_json",
@@ -326,8 +393,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable result interpretation for the website, HF cards, and mirror checks.",
       "exists": true,
-      "bytes": 6938,
-      "sha256": "c839e4f163878028c6a250294161571ca18c86d5b45726d85a69420d26b2ebd6"
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@@ -337,8 +404,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the research takeaways from committed summary metrics and task result artifacts.",
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-      "bytes": 13289,
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@@ -606,8 +673,8 @@
       "surface": "repo_hf",
       "shows": "Defines public reproduction commands, expected outputs, and non-reproducible scale-up boundaries.",
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@@ -617,8 +684,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable reproduction steps with expected artifacts and public boundaries.",
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-      "bytes": 5265,
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@@ -628,8 +695,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
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-      "bytes": 28464,
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@@ -652,7 +719,7 @@
       "volatile": true,
       "shows": "Separates setup paths from completed held-out-episode results.",
       "exists": true,
-      "bytes": 18981,
+      "bytes": 20823,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -664,7 +731,7 @@
       "volatile": true,
       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
-      "bytes": 108621,
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       "hash_policy": "existence_and_size_only"
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@@ -676,7 +743,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
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       "hash_policy": "existence_and_size_only"
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@@ -687,8 +754,8 @@
       "surface": "website_hf",
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-      "bytes": 4643,
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@@ -709,8 +776,8 @@
       "surface": "website_hf",
       "shows": "Mirrors task metrics for the static dashboard.",
       "exists": true,
-      "bytes": 25301,
-      "sha256": "966042a203b6290ddb763ee827597752ed518c90791ffa61e55f519c810594a3"
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@@ -850,10 +917,10 @@
       "path": "results/omni_finetune/DATA_ACCESS_STATUS.md",
       "kind": "scaleup_status",
       "surface": "repo_hf",
-      "shows": "Summarizes the staging requirement before the held-out Qwen3-Omni pilot can report metrics.",
+      "shows": "Summarizes the data-readiness checks required before a held-out Qwen3-Omni pilot can report metrics.",
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-      "bytes": 3103,
-      "sha256": "8355831cecd3e10a56ca4a0ac0b00fcfc0773c89fbc11fedb25183954daa59da"
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@@ -863,8 +930,8 @@
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@@ -257,27 +257,27 @@
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@@ -344,33 +344,64 @@
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@@ -2999,27 +3030,27 @@
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@@ -3030,27 +3061,27 @@
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data/project_status.json

@@ -106,6 +106,17 @@
       ],
       "readout": "Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is added as the first world-model/action-generation branch; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit."
     },
+    {
+      "area": "Omni model extension contract",
+      "status": "current",
+      "evidence": [
+        "OMNI_MODEL_EXTENSION_CONTRACT.md",
+        "configs/omni_backbones/",
+        "scripts/omni/backbone_registry.py",
+        "scripts/omni/smoke_test_backbone_packaging.py"
+      ],
+      "readout": "Future Qwen, Cosmos-style, and VLA/policy branches must keep the same episode split discipline, held-out metrics, validation gate, public-safe package contract, and explicit forbidden-artifact policy before reporting results."
+    },
     {
       "area": "Xperience Embodied Foundation Model",
       "status": "future_goal",
@@ -190,6 +201,7 @@
     "Inspect RESEARCH_TAKEAWAYS.md and docs/data/research_takeaways.json before interpreting model scores.",
     "Inspect RESEARCH_ROADMAP.md and docs/data/research_roadmap.json for the path from public-sample task work to multi-episode modeling.",
     "Inspect FOUNDATION_MODEL_PLAN.md and docs/data/foundation_model_plan.json before choosing a backbone branch.",
+    "Inspect OMNI_MODEL_EXTENSION_CONTRACT.md and run python scripts/omni/backbone_registry.py --validate --json before adding a new Qwen, Cosmos-style, or VLA/policy branch.",
     "Inspect XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md for the long-term full-corpus pretraining goal.",
     "Inspect docs/data/summary_metrics.json and results/episode_task_suite/neural_mlp/ to check the 12-task outputs.",
     "Inspect results/audio_ablation/AUDIO_ABLATION_SUMMARY.md before judging whether audio helps the current task suite.",
@@ -205,6 +217,7 @@
     "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/.",
     "Foundation-model selection is explicit: Qwen3-Omni is the immediate trainable pilot, Cosmos 3 is the first world-model branch, and policy models such as OpenVLA/openpi/GR00T wait for action-target conversion.",
+    "Future model branches should be added through the backbone registry and verified package contract, not as one-off result folders with incompatible metrics or publication rules.",
     "The Xperience Embodied Foundation Model is a future native-pretraining goal, not a completed model or current benchmark."
   ]
 }

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T21:31:36+00:00",
+  "generated_at_utc": "2026-06-06T14:38:05+00:00",
   "checks": [
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       "name": "required_publication_assets_present",
@@ -182,8 +182,8 @@
     "github_repo": {
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       "exists": true,
-      "file_count": 401,
-      "text_file_count": 335,
+      "file_count": 442,
+      "text_file_count": 372,
       "largest_file": {
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         "bytes": 55702978
@@ -193,8 +193,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 319,
-      "text_file_count": 253,
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@@ -204,8 +204,8 @@
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       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 420,
-      "text_file_count": 332,
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+      "text_file_count": 420,
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@@ -215,8 +215,8 @@
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       "exists": true,
-      "file_count": 648,
-      "text_file_count": 523,
+      "file_count": 701,
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         "bytes": 93495480

data/scope_claims_audit.json

@@ -1,75 +1,69 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T16:48:58+00:00",
+  "generated_at_utc": "2026-06-06T14:35:59+00:00",
   "summary": {
-    "qwen3_omni_32_episode_claim": false,
-    "dataset_manifest_num_episodes": 1,
-    "dataset_manifest_num_samples": 128,
-    "training_metadata_num_train_samples": 128,
-    "source_discovery_ready_for_32_episode_pilot": false,
-    "historical_identifier_count": 159,
-    "public_32_episode_status_file_count": 3,
+    "qwen3_omni_verified_diagnostic_pilot": true,
+    "dataset_manifest_num_episodes": 119,
+    "dataset_manifest_num_samples": 3808,
+    "training_metadata_num_train_samples": 2848,
+    "eval_num_samples": 448,
+    "eval_json_validity_rate": 0.875,
+    "quality_target_met": false,
+    "historical_identifier_count": 132,
+    "public_32_episode_status_file_count": 1,
     "failure_count": 0
   },
   "checks": [
     {
-      "name": "project_manifest_records_pending_32_episode_qwen_result",
+      "name": "project_packet_records_verified_diagnostic_status",
       "status": "pass",
-      "detail": "project_manifest scope_boundary.qwen3_omni_32_episode_claim=False",
-      "evidence": [
-        "docs/data/project_manifest.json"
-      ]
-    },
-    {
-      "name": "project_packet_records_pending_32_episode_qwen_result",
-      "status": "pass",
-      "detail": "project_packet scope_status.qwen3_omni_32_episode_claim=False",
+      "detail": "project packet describes the verified diagnostic pilot and quality boundary",
       "evidence": [
         "docs/data/project_packet.json"
       ]
     },
     {
-      "name": "project_packet_describes_32_episode_setup_status",
+      "name": "summary_metrics_preserves_verified_diagnostic_status",
       "status": "pass",
-      "detail": "project packet describes the setup-stage Qwen3-Omni run separately from the planned 32-episode fine-tune",
+      "detail": "The selected-episode Qwen3-Omni validation-aware diagnostic pilot is verified, but held-out quality is still weak and JSON validity remains below the 98% target.",
       "evidence": [
-        "docs/data/project_packet.json"
+        "docs/data/summary_metrics.json"
       ]
     },
     {
-      "name": "summary_metrics_preserves_omni_scale_up_status",
+      "name": "verified_package_dataset_has_expected_windows",
       "status": "pass",
-      "detail": "The selected-episode Qwen3-Omni fine-tune requires completed data preparation and held-out evaluation; the 32-episode Qwen3-Omni fine-tune requires gated data preparation before any real held-out metric is reported.",
+      "detail": "episodes=119, samples=3808, split_counts={'train': 2848, 'val': 512, 'test': 448}",
       "evidence": [
-        "docs/data/summary_metrics.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/dataset_manifest.json"
       ]
     },
     {
-      "name": "omni_dataset_manifest_is_setup_stage",
+      "name": "verified_package_training_records_8_processes",
       "status": "pass",
-      "detail": "episodes=1, samples=128, split_counts={'train': 128}",
+      "detail": "train=2848, val=512, processes=8",
       "evidence": [
-        "results/omni_finetune/dataset_manifest.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/training/training_metadata.json"
       ]
     },
     {
-      "name": "omni_training_metadata_is_setup_stage",
+      "name": "verified_package_eval_records_real_held_out_metrics",
       "status": "pass",
-      "detail": "train=128, val=0, processes=8",
+      "detail": "samples=448, split=test, held_out=14, json_validity=0.875",
       "evidence": [
-        "results/omni_finetune/training_metadata.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/eval/metrics.json"
       ]
     },
     {
-      "name": "source_discovery_gate_is_closed",
+      "name": "verified_package_audit_passes",
       "status": "pass",
-      "detail": "ready_for_32_episode_pilot=False, local_valid=1",
+      "detail": "audit_status=pass, issues=0",
       "evidence": [
-        "results/omni_finetune/source_discovery.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/package_audit.json"
       ]
     },
     {
-      "name": "public_presentation_has_no_historical_32ep_identifiers",
+      "name": "public_presentation_has_no_misleading_32ep_identifiers",
       "status": "pass",
       "detail": "public presentation scan failures=0",
       "evidence": [
@@ -90,7 +84,7 @@
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
-      "detail": "historical identifiers found in result provenance files=159",
+      "detail": "historical identifiers found in result provenance files=132",
       "evidence": [
         "results/omni_finetune/"
       ]
@@ -100,14 +94,6 @@
     {
       "path": "docs/data/artifact_index.json",
       "contains_32_episode_status_text": true
-    },
-    {
-      "path": "docs/data/project_packet.json",
-      "contains_32_episode_status_text": true
-    },
-    {
-      "path": "docs/data/summary_metrics.json",
-      "contains_32_episode_status_text": true
     }
   ],
   "historical_identifiers": [
@@ -123,313 +109,320 @@
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT.md",
-      "line": 4,
+      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
+      "line": 42,
       "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`"
+      "example": "export RELAY_WORKDIR=/path/to/ropedia-episode-task-suite"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT.md",
-      "line": 5,
+      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
+      "line": 45,
       "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_eval.md",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 1,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:0\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 0, \"end_frame\": 19, \"num_frames\": 20}, \"media\": {\"video_paths\": [{"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_eval.md",
-      "line": 5,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 2,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:1\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 20, \"end_frame\": 39, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_lora.md",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 3,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:2\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 40, \"end_frame\": 59, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
-      "line": 42,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 4,
       "patterns": [
+        "qwen3_omni_32ep",
+        "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "export RELAY_WORKDIR=/path/to/ropedia-episode-task-suite"
+      "example": "{\"id\": \"xperience-10m-sample:qa:3\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 60, \"end_frame\": 79, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
-      "line": 45,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 5,
       "patterns": [
+        "qwen3_omni_32ep",
+        "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
+      "example": "{\"id\": \"xperience-10m-sample:qa:4\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 80, \"end_frame\": 99, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 1,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 6,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "run_id: xperience10m_qwen3_omni_32ep_lora"
+      "example": "{\"id\": \"xperience-10m-sample:qa:5\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 100, \"end_frame\": 119, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 7,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "dataset_jsonl: results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl"
+      "example": "{\"id\": \"xperience-10m-sample:qa:6\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 120, \"end_frame\": 139, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 5,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 8,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "checkpoint_dir: /path/to/ropedia_workspace/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora"
+      "example": "{\"id\": \"xperience-10m-sample:qa:7\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 140, \"end_frame\": 159, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 1,
+      "line": 9,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:0\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 0, \"end_frame\": 19, \"num_frames\": 20}, \"media\": {\"video_paths\": [{"
+      "example": "{\"id\": \"xperience-10m-sample:qa:8\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 160, \"end_frame\": 179, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 2,
+      "line": 10,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:1\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 20, \"end_frame\": 39, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:9\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 180, \"end_frame\": 199, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 3,
+      "line": 11,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:2\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 40, \"end_frame\": 59, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:10\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 200, \"end_frame\": 219, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 4,
+      "line": 12,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:3\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 60, \"end_frame\": 79, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:11\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 220, \"end_frame\": 239, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 5,
+      "line": 13,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:4\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 80, \"end_frame\": 99, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:12\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 240, \"end_frame\": 259, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 6,
+      "line": 14,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:5\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 100, \"end_frame\": 119, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:13\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 260, \"end_frame\": 279, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 7,
+      "line": 15,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:6\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 120, \"end_frame\": 139, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:14\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 280, \"end_frame\": 299, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 8,
+      "line": 16,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:7\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 140, \"end_frame\": 159, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:15\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 300, \"end_frame\": 319, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 9,
+      "line": 17,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:8\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 160, \"end_frame\": 179, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:40\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 800, \"end_frame\": 819, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 10,
+      "line": 18,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:9\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 180, \"end_frame\": 199, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:41\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 820, \"end_frame\": 839, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 11,
+      "line": 19,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:10\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 200, \"end_frame\": 219, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:42\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 840, \"end_frame\": 859, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 12,
+      "line": 20,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:11\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 220, \"end_frame\": 239, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:43\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 860, \"end_frame\": 879, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 13,
+      "line": 21,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:12\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 240, \"end_frame\": 259, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:44\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 880, \"end_frame\": 899, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 14,
+      "line": 22,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:13\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 260, \"end_frame\": 279, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:45\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 900, \"end_frame\": 919, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 15,
+      "line": 23,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:14\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 280, \"end_frame\": 299, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:46\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 920, \"end_frame\": 939, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 16,
+      "line": 24,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:15\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 300, \"end_frame\": 319, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:47\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 940, \"end_frame\": 959, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 17,
+      "line": 25,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:40\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 800, \"end_frame\": 819, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:50\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1000, \"end_frame\": 1019, \"num_frames\": 20}, \"media\": {\"video_path"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 18,
+      "line": 26,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:41\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 820, \"end_frame\": 839, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:51\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1020, \"end_frame\": 1039, \"num_frames\": 20}, \"media\": {\"video_path"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 19,
+      "line": 27,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:42\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 840, \"end_frame\": 859, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:52\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1040, \"end_frame\": 1059, \"num_frames\": 20}, \"media\": {\"video_path"
     }
   ],
-  "historical_identifier_total_count": 159,
+  "historical_identifier_total_count": 132,
   "failures": []
 }

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-03T20:36:23+00:00",
+  "generated_at_utc": "2026-06-06T14:35:59+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,
@@ -64,45 +64,45 @@
       "observed": "timeline_action"
     },
     {
-      "name": "timeline_action: public_field_output_short_is_human_readable",
+      "name": "timeline_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current action class",
+      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_plain_goal_is_human_readable",
+      "name": "timeline_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Look at one short multimodal window and name what action is happening now.",
+      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_process_short_is_human_readable",
+      "name": "timeline_action: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "window features -> action label builder -> classifier",
+      "value": "Action Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_research_name_is_human_readable",
+      "name": "timeline_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Egocentric Action Recognition",
+      "value": "current action class",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_card_blurb_is_human_readable",
+      "name": "timeline_action: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
+      "value": "Egocentric Action Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_display_name_is_human_readable",
+      "name": "timeline_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Action Recognition",
+      "value": "Look at one short multimodal window and name what action is happening now.",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_input_short_is_human_readable",
+      "name": "timeline_action: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
+      "value": "window features -> action label builder -> classifier",
       "raw_hits": []
     },
     {
@@ -184,45 +184,45 @@
       "observed": "timeline_subtask"
     },
     {
-      "name": "timeline_subtask: public_field_output_short_is_human_readable",
+      "name": "timeline_subtask: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current procedure step",
+      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
+      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict the higher-level task stage for the current window.",
+      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_process_short_is_human_readable",
+      "name": "timeline_subtask: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "window features -> subtask label builder -> classifier",
+      "value": "Procedure Step Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_research_name_is_human_readable",
+      "name": "timeline_subtask: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Subtask Recognition",
+      "value": "current procedure step",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
+      "name": "timeline_subtask: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
+      "value": "Temporal Subtask Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_display_name_is_human_readable",
+      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Procedure Step Recognition",
+      "value": "Predict the higher-level task stage for the current window.",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_input_short_is_human_readable",
+      "name": "timeline_subtask: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
+      "value": "window features -> subtask label builder -> classifier",
       "raw_hits": []
     },
     {
@@ -304,45 +304,45 @@
       "observed": "transition_detection"
     },
     {
-      "name": "transition_detection: public_field_output_short_is_human_readable",
+      "name": "transition_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "boundary or steady",
+      "value": "current window with boundary target",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_plain_goal_is_human_readable",
+      "name": "transition_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Detect whether the current window is near a boundary between actions.",
+      "value": "Detect the local moment where the episode changes from one action segment to the next.",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_process_short_is_human_readable",
+      "name": "transition_detection: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "action changes -> boundary labels -> binary classifier",
+      "value": "Action Boundary Detection",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_research_name_is_human_readable",
+      "name": "transition_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Action Segmentation",
+      "value": "boundary or steady",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_card_blurb_is_human_readable",
+      "name": "transition_detection: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Detect the local moment where the episode changes from one action segment to the next.",
+      "value": "Temporal Action Segmentation",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_display_name_is_human_readable",
+      "name": "transition_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Action Boundary Detection",
+      "value": "Detect whether the current window is near a boundary between actions.",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_input_short_is_human_readable",
+      "name": "transition_detection: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current window with boundary target",
+      "value": "action changes -> boundary labels -> binary classifier",
       "raw_hits": []
     },
     {
@@ -422,45 +422,45 @@
       "observed": "next_action"
     },
     {
-      "name": "next_action: public_field_output_short_is_human_readable",
+      "name": "next_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "action at t+20 frames",
+      "value": "current window at time t",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_plain_goal_is_human_readable",
+      "name": "next_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Use the current window to guess the action that will happen shortly after it.",
+      "value": "Forecast the near-future action from the current observations only.",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_process_short_is_human_readable",
+      "name": "next_action: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "current features -> future label shift -> classifier",
+      "value": "Next-Action Prediction",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_research_name_is_human_readable",
+      "name": "next_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Short-Horizon Intention Prediction",
+      "value": "action at t+20 frames",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_card_blurb_is_human_readable",
+      "name": "next_action: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Forecast the near-future action from the current observations only.",
+      "value": "Short-Horizon Intention Prediction",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_display_name_is_human_readable",
+      "name": "next_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Next-Action Prediction",
+      "value": "Use the current window to guess the action that will happen shortly after it.",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_input_short_is_human_readable",
+      "name": "next_action: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current window at time t",
+      "value": "current features -> future label shift -> classifier",
       "raw_hits": []
     },
     {
@@ -540,45 +540,45 @@
       "observed": "hand_trajectory_forecast"
     },
     {
-      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "future hand-joint trajectory",
+      "value": "current multimodal window",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict where the hands will move over the next few frames.",
+      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_process_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "current features -> future mocap target -> regression head",
+      "value": "Hand Trajectory Forecasting",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "3D Hand Motion Forecasting",
+      "value": "future hand-joint trajectory",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
+      "value": "3D Hand Motion Forecasting",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_display_name_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Hand Trajectory Forecasting",
+      "value": "Predict where the hands will move over the next few frames.",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current multimodal window",
+      "value": "current features -> future mocap target -> regression head",
       "raw_hits": []
     },
     {
@@ -658,45 +658,45 @@
       "observed": "contact_prediction"
     },
     {
-      "name": "contact_prediction: public_field_output_short_is_human_readable",
+      "name": "contact_prediction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "contact or no contact",
+      "value": "non-contact, non-caption features",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
+      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict whether the body or hand is in contact with something.",
+      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_process_short_is_human_readable",
+      "name": "contact_prediction: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "feature filter -> contact target -> binary classifier",
+      "value": "Contact State Prediction",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_research_name_is_human_readable",
+      "name": "contact_prediction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Human-Object Contact Prediction",
+      "value": "contact or no contact",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
+      "name": "contact_prediction: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
+      "value": "Human-Object Contact Prediction",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_display_name_is_human_readable",
+      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Contact State Prediction",
+      "value": "Predict whether the body or hand is in contact with something.",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_input_short_is_human_readable",
+      "name": "contact_prediction: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "non-contact, non-caption features",
+      "value": "feature filter -> contact target -> binary classifier",
       "raw_hits": []
     },
     {
@@ -774,45 +774,45 @@
       "observed": "object_relevance"
     },
     {
-      "name": "object_relevance: public_field_output_short_is_human_readable",
+      "name": "object_relevance: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "relevant object set",
+      "value": "non-caption multimodal features",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_plain_goal_is_human_readable",
+      "name": "object_relevance: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict which objects matter in the current window.",
+      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_process_short_is_human_readable",
+      "name": "object_relevance: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "object vocabulary -> multi-hot labels -> sigmoid heads",
+      "value": "Object Relevance Prediction",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_research_name_is_human_readable",
+      "name": "object_relevance: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Object-Centric Interaction Recognition",
+      "value": "relevant object set",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_card_blurb_is_human_readable",
+      "name": "object_relevance: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
+      "value": "Object-Centric Interaction Recognition",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_display_name_is_human_readable",
+      "name": "object_relevance: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Object Relevance Prediction",
+      "value": "Predict which objects matter in the current window.",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_input_short_is_human_readable",
+      "name": "object_relevance: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "non-caption multimodal features",
+      "value": "object vocabulary -> multi-hot labels -> sigmoid heads",
       "raw_hits": []
     },
     {
@@ -892,45 +892,45 @@
       "observed": "caption_grounding"
     },
     {
-      "name": "caption_grounding: public_field_output_short_is_human_readable",
+      "name": "caption_grounding: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "ranked matching moments",
+      "value": "text-like query and candidate windows",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
+      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Given a text-like query from annotation, find the matching time window.",
+      "value": "Retrieve the matching time window for an annotation-derived text query.",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_process_short_is_human_readable",
+      "name": "caption_grounding: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "query features -> candidate index -> cosine ranker",
+      "value": "Language Grounding",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_research_name_is_human_readable",
+      "name": "caption_grounding: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Language-to-Moment Grounding",
+      "value": "ranked matching moments",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
+      "name": "caption_grounding: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Retrieve the matching time window for an annotation-derived text query.",
+      "value": "Language-to-Moment Grounding",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_display_name_is_human_readable",
+      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Language Grounding",
+      "value": "Given a text-like query from annotation, find the matching time window.",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_input_short_is_human_readable",
+      "name": "caption_grounding: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "text-like query and candidate windows",
+      "value": "query features -> candidate index -> cosine ranker",
       "raw_hits": []
     },
     {
@@ -1008,45 +1008,45 @@
       "observed": "cross_modal_retrieval"
     },
     {
-      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "ranked visual windows",
+      "value": "motion/IMU/pose query; depth/video candidates",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Use one group of modalities to retrieve the matching window from another group.",
+      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_process_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "modality split -> projection -> nearest-neighbor ranker",
+      "value": "Cross-Modal Retrieval",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Multimodal Representation Retrieval",
+      "value": "ranked visual windows",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
+      "value": "Multimodal Representation Retrieval",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_display_name_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Retrieval",
+      "value": "Use one group of modalities to retrieve the matching window from another group.",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion/IMU/pose query; depth/video candidates",
+      "value": "modality split -> projection -> nearest-neighbor ranker",
       "raw_hits": []
     },
     {
@@ -1126,45 +1126,45 @@
       "observed": "modality_reconstruction"
     },
     {
-      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "reconstructed depth/video vector",
+      "value": "motion, IMU, and camera/pose features",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
+      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict one modality feature block from other modality blocks.",
+      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_process_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "source-target split -> scaler -> regression head",
+      "value": "Cross-Modal Reconstruction",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
+      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Modality Feature Reconstruction",
+      "value": "reconstructed depth/video vector",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
+      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
+      "value": "Modality Feature Reconstruction",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_display_name_is_human_readable",
+      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Reconstruction",
+      "value": "Predict one modality feature block from other modality blocks.",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion, IMU, and camera/pose features",
+      "value": "source-target split -> scaler -> regression head",
       "raw_hits": []
     },
     {
@@ -1244,45 +1244,45 @@
       "observed": "temporal_order"
     },
     {
-      "name": "temporal_order: public_field_output_short_is_human_readable",
+      "name": "temporal_order: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "correct or reversed",
+      "value": "two adjacent windows plus difference vector",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_plain_goal_is_human_readable",
+      "name": "temporal_order: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two nearby windows are in the correct time order.",
+      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_process_short_is_human_readable",
+      "name": "temporal_order: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "pair builder -> feature combiner -> binary classifier",
+      "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_research_name_is_human_readable",
+      "name": "temporal_order: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Order Verification",
+      "value": "correct or reversed",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_card_blurb_is_human_readable",
+      "name": "temporal_order: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
+      "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_display_name_is_human_readable",
+      "name": "temporal_order: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Temporal Order Verification",
+      "value": "Tell whether two nearby windows are in the correct time order.",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_input_short_is_human_readable",
+      "name": "temporal_order: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "two adjacent windows plus difference vector",
+      "value": "pair builder -> feature combiner -> binary classifier",
       "raw_hits": []
     },
     {
@@ -1360,45 +1360,45 @@
       "observed": "misalignment_detection"
     },
     {
-      "name": "misalignment_detection: public_field_output_short_is_human_readable",
+      "name": "misalignment_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "aligned or shifted",
+      "value": "motion-side and visual/depth-side feature groups",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
+      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Detect when modalities that should match are shifted out of sync.",
+      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_process_short_is_human_readable",
+      "name": "misalignment_detection: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "aligned/shifted pairs -> feature combiner -> binary classifier",
+      "value": "Multimodal Synchronization Detection",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_research_name_is_human_readable",
+      "name": "misalignment_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Misalignment Detection",
+      "value": "aligned or shifted",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
+      "name": "misalignment_detection: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
+      "value": "Cross-Modal Misalignment Detection",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_display_name_is_human_readable",
+      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Multimodal Synchronization Detection",
+      "value": "Detect when modalities that should match are shifted out of sync.",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_input_short_is_human_readable",
+      "name": "misalignment_detection: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion-side and visual/depth-side feature groups",
+      "value": "aligned/shifted pairs -> feature combiner -> binary classifier",
       "raw_hits": []
     },
     {

data/website_integrity.json

@@ -1,13 +1,13 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T21:31:33+00:00",
+  "generated_at_utc": "2026-06-06T14:36:10+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 128,
-    "external_reference_count": 112,
-    "json_files": 33,
+    "local_references": 133,
+    "external_reference_count": 107,
+    "json_files": 34,
     "image_assets_referenced": 22,
     "failure_count": 0
   },
@@ -75,7 +75,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 67412,
-      "evidence_index": 88788
+      "evidence_index": 89661
     },
     {
       "name": "project_status_links_json",
@@ -119,13 +119,13 @@
       "name": "reader_resources_link_qwen_status",
       "status": "pass",
       "reason": "The website should expose the Qwen3-Omni scale-up status.",
-      "marker_count": 9
+      "marker_count": 5
     },
     {
       "name": "roadmap_html_matches_json_phases",
       "status": "pass",
       "reason": "The roadmap section should show every stage defined in research_roadmap.json.",
-      "phase_count": 7,
+      "phase_count": 8,
       "missing_phase_names": [],
       "roadmap_json_error": null
     },
@@ -133,11 +133,12 @@
       "name": "roadmap_status_chips_match_json",
       "status": "pass",
       "reason": "The roadmap status chips should match the phase statuses in research_roadmap.json.",
-      "phase_count": 7,
+      "phase_count": 8,
       "statuses": [
         "implemented",
-        "active",
-        "next",
+        "implemented_for_first_pilot",
+        "verified_baseline",
+        "active_next_step",
         "next",
         "planned",
         "planned",
@@ -151,8 +152,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 67412,
-      "protocol_index": 85478,
-      "evidence_index": 88788
+      "protocol_index": 86399,
+      "evidence_index": 89661
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -226,7 +227,7 @@
     {
       "path": "index.html",
       "id_count": 77,
-      "reference_count": 105,
+      "reference_count": 110,
       "image_count": 24
     },
     {
@@ -245,12 +246,12 @@
   "json_files": [
     {
       "path": "data/additional_development_directions.json",
-      "bytes": 6120,
+      "bytes": 6121,
       "top_level_type": "dict"
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     {
       "path": "data/artifact_index.json",
-      "bytes": 34764,
+      "bytes": 37736,
       "top_level_type": "dict"
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     {
@@ -265,12 +266,12 @@
     },
     {
       "path": "data/evaluation_protocol.json",
-      "bytes": 13606,
+      "bytes": 13788,
       "top_level_type": "dict"
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     {
       "path": "data/evidence_contract.json",
-      "bytes": 12147,
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     {
@@ -280,7 +281,7 @@
     },
     {
       "path": "data/foundation_model_plan.json",
-      "bytes": 12981,
+      "bytes": 13112,
       "top_level_type": "dict"
     },
     {
@@ -290,7 +291,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 110838,
+      "bytes": 111950,
       "top_level_type": "dict"
     },
     {
@@ -298,24 +299,29 @@
       "bytes": 3797,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/omni_finetune_verified_result.json",
+      "bytes": 3145,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/project_brief.json",
-      "bytes": 3637,
+      "bytes": 3752,
       "top_level_type": "dict"
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-      "bytes": 4643,
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-      "bytes": 7648,
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-      "bytes": 9874,
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     {
@@ -340,7 +346,7 @@
     },
     {
       "path": "data/reproducibility_matrix.json",
-      "bytes": 5265,
+      "bytes": 5223,
       "top_level_type": "dict"
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     {
@@ -355,22 +361,22 @@
     },
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       "path": "data/research_roadmap.json",
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+      "bytes": 8981,
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       "path": "data/research_roadmap_interactive.json",
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     {
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     {
       "path": "data/scope_claims_audit.json",
-      "bytes": 18981,
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@@ -385,7 +391,7 @@
     },
     {
       "path": "data/summary_metrics.json",
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+      "bytes": 26028,
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     {
@@ -400,7 +406,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 15982,
+      "bytes": 15221,
       "top_level_type": "dict"
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docs/data/artifact_index.json

@@ -1,11 +1,12 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-04T21:22:15+00:00",
+  "generated_at_utc": "2026-06-06T14:35:42+00:00",
   "status": "pass",
-  "artifact_count": 77,
+  "artifact_count": 83,
   "missing": [],
   "by_kind": {
     "project_path": 14,
+    "scaleup_contract": 6,
     "project_scope": 1,
     "source_alignment": 5,
     "publication_workflow": 3,
@@ -40,8 +41,8 @@
       "surface": "repo_hf",
       "shows": "Gives first-pass readers a concise project shape before the detailed artifact trail.",
       "exists": true,
-      "bytes": 3594,
-      "sha256": "8df692f2d6b092097d5ef7d5039e7529abe2fd0a80ff3fb23fa14260fb5f9de2"
+      "bytes": 3829,
+      "sha256": "3d16e700c31aafe889b3d8e43d52250208766809c483a716a13cbd26961b3e72"
     },
     {
       "id": "project_brief_json",
@@ -51,8 +52,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable first-reader project brief for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 3637,
-      "sha256": "0e859cfd3fca3ac66c9dd67e04a404ab41757c248d8777ee9cf7da63ab11a2f7"
+      "bytes": 3752,
+      "sha256": "4bd2f3471d0141abf64a2327541e25031e9efa39940cc68a29c316cf36f7c884"
     },
     {
       "id": "project_status",
@@ -62,8 +63,8 @@
       "surface": "repo_hf",
       "shows": "Gives a compact current-state table for first-pass readers.",
       "exists": true,
-      "bytes": 7207,
-      "sha256": "7baaba976ccc254da1a03ee2653057d1e08f3fb0c0cad035886c362442828720"
+      "bytes": 8534,
+      "sha256": "5eb48d489da7f005baab233a94c9d6b209eb1e9ffdb138c8e0e600ece9239a29"
     },
     {
       "id": "project_status_json",
@@ -73,8 +74,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable copy of the current project status for website and HF mirrors.",
       "exists": true,
-      "bytes": 9874,
-      "sha256": "600c95726eae3404127a8b2110f35468ff2ba02943cae0fbcd3ea43c66109d3e"
+      "bytes": 10977,
+      "sha256": "2bb0639c137dfd6eddd337eb909292543ae2e72753dee398f8240ff35f6a3984"
     },
     {
       "id": "research_roadmap",
@@ -84,8 +85,8 @@
       "surface": "repo_hf",
       "shows": "Defines the path from public-sample task development to multi-episode held-out evaluation and larger omni-model extensions.",
       "exists": true,
-      "bytes": 10259,
-      "sha256": "6e26acb051a45a5afe36fa0c8b3c60f58c383c2e58bee6e01bac28b7dd604f0a"
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+      "sha256": "4e3451a2248176e78b0587a8a15ead2df682068d0833a21e624ac955c5ae72a3"
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       "id": "research_roadmap_json",
@@ -95,8 +96,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable research roadmap for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 7699,
-      "sha256": "69b777b4d01e2e587236c1bb2206a47e2b9a0e1a5f1c7c6e125b0dd3270ce0d5"
+      "bytes": 8981,
+      "sha256": "92f94edda5337aa0f49e136748320cb1c2c013b7a12b5fe36cf556f5b1bb0142"
     },
     {
       "id": "foundation_model_plan",
@@ -117,8 +118,74 @@
       "surface": "website_hf",
       "shows": "Machine-readable foundation-model selection matrix with source links, entry conditions, and evaluation additions.",
       "exists": true,
-      "bytes": 12981,
-      "sha256": "9cce52025a2e2f8afb4660e2af3353aea6ad0a1af380849218dd74c0acc271bb"
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+      "sha256": "0850a9b74f2c62aba30b297089ad0a4a04424cc16d4a265f71e39538c6d22792"
+    },
+    {
+      "id": "omni_model_extension_contract",
+      "title": "Omni model extension contract",
+      "path": "OMNI_MODEL_EXTENSION_CONTRACT.md",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Defines the shared manifest, episode split, held-out evaluation, packaging, and public-safety rules for Qwen3-Omni, Cosmos-style, and VLA/policy model branches.",
+      "exists": true,
+      "bytes": 8900,
+      "sha256": "c4e51d0aa7536045c229418603a67c6b3c5f31c9d756ca7395cb0c9455f0ed6d"
+    },
+    {
+      "id": "omni_backbone_registry_configs",
+      "title": "Omni backbone registry configs",
+      "path": "configs/omni_backbones",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Stores the implemented Qwen3-Omni LoRA contract and planned Cosmos-style world-model and VLA/policy branch contracts.",
+      "exists": true,
+      "file_count": 3,
+      "bytes": 8904
+    },
+    {
+      "id": "omni_backbone_registry_validator",
+      "title": "Omni backbone registry validator",
+      "path": "scripts/omni/backbone_registry.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Validates backbone ids, split defaults, leakage guards, required metrics, required files, and forbidden public package categories.",
+      "exists": true,
+      "bytes": 8740,
+      "sha256": "3bc9eeb47471fb995f767d5420a556a95413c1cb2d8b3ea9d2805429df55beb8"
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+    {
+      "id": "omni_model_neutral_window_index_exporter",
+      "title": "Model-neutral window index exporter",
+      "path": "scripts/omni/export_model_neutral_window_index.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Converts Qwen JSONL records into a model-neutral window index with Qwen, Cosmos-style, and policy/VLA adapter views.",
+      "exists": true,
+      "bytes": 8540,
+      "sha256": "ec6dd4fcdc1c9a90566a59ef118f7ed5b78ebf8843752873aea8ae8b5f71a34d"
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+    {
+      "id": "omni_backbone_scaffolder",
+      "title": "Omni backbone scaffolder",
+      "path": "scripts/omni/scaffold_omni_backbone.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Creates a validated planned-backbone config from an existing contract template so new model families inherit the shared rules.",
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+      "bytes": 4809,
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+    },
+    {
+      "id": "omni_backbone_packaging_smoke",
+      "title": "Omni backbone packaging smoke test",
+      "path": "scripts/omni/smoke_test_backbone_packaging.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Builds synthetic verified packages for every configured backbone and audits them against the public-safe package contract.",
+      "exists": true,
+      "bytes": 6519,
+      "sha256": "a3773fc681e298325e2be80556d6be6e7e30b90ba22ee24b66633f07ff9c4ea4"
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@@ -128,8 +195,8 @@
       "surface": "repo_hf",
       "shows": "Records concrete non-backbone Xperience-10M development tracks: taxonomy, benchmark protocol, representation learning, skill graphs, affordances, 3D/4D memory, QA, and policy transfer.",
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@@ -139,8 +206,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable additional development directions for the website and Hugging Face mirrors.",
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-      "bytes": 6120,
-      "sha256": "ec918f6849ca888e314a6481e10ddab68d88ba766cbe6a97265df6891058e1df"
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@@ -172,8 +239,8 @@
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@@ -183,8 +250,8 @@
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@@ -249,8 +316,8 @@
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@@ -282,8 +349,8 @@
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@@ -293,8 +360,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable protocol generated from committed task metrics for website and HF mirrors.",
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-      "bytes": 13606,
-      "sha256": "e1c729bfba5105db35035b09e3c3d2ae74375dc75dc6577d8bd79fd7801be0a6"
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@@ -304,8 +371,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the protocol from committed summary metrics and task artifacts.",
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-      "bytes": 16148,
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@@ -315,8 +382,8 @@
       "surface": "repo_hf",
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-      "sha256": "7eb264fe323a6a4bef9fed9e2104d0eb3977f38e9b152d1e5bbf5246ebb1aa8a"
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@@ -326,8 +393,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable result interpretation for the website, HF cards, and mirror checks.",
       "exists": true,
-      "bytes": 6938,
-      "sha256": "c839e4f163878028c6a250294161571ca18c86d5b45726d85a69420d26b2ebd6"
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@@ -337,8 +404,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the research takeaways from committed summary metrics and task result artifacts.",
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-      "bytes": 13289,
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@@ -606,8 +673,8 @@
       "surface": "repo_hf",
       "shows": "Defines public reproduction commands, expected outputs, and non-reproducible scale-up boundaries.",
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@@ -617,8 +684,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable reproduction steps with expected artifacts and public boundaries.",
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-      "bytes": 5265,
-      "sha256": "28feab95f4c080a658d0db49d817cb4564557638a7d177a806ed704508dca4dc"
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@@ -628,8 +695,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
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-      "bytes": 28464,
-      "sha256": "a74480209e3185b00aaa50dbf93313010f49ce3dd39e1c0d856536302abf9398"
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@@ -652,7 +719,7 @@
       "volatile": true,
       "shows": "Separates setup paths from completed held-out-episode results.",
       "exists": true,
-      "bytes": 18981,
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       "hash_policy": "existence_and_size_only"
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@@ -664,7 +731,7 @@
       "volatile": true,
       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
-      "bytes": 108621,
+      "bytes": 111950,
       "hash_policy": "existence_and_size_only"
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@@ -676,7 +743,7 @@
       "volatile": true,
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-      "bytes": 14891,
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       "hash_policy": "existence_and_size_only"
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@@ -687,8 +754,8 @@
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-      "bytes": 4643,
-      "sha256": "b02ca0064bb6eea07abdcf4ff4aacde06a291f94679b79ca599052848a269b49"
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@@ -709,8 +776,8 @@
       "surface": "website_hf",
       "shows": "Mirrors task metrics for the static dashboard.",
       "exists": true,
-      "bytes": 25301,
-      "sha256": "966042a203b6290ddb763ee827597752ed518c90791ffa61e55f519c810594a3"
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@@ -850,10 +917,10 @@
       "path": "results/omni_finetune/DATA_ACCESS_STATUS.md",
       "kind": "scaleup_status",
       "surface": "repo_hf",
-      "shows": "Summarizes the staging requirement before the held-out Qwen3-Omni pilot can report metrics.",
+      "shows": "Summarizes the data-readiness checks required before a held-out Qwen3-Omni pilot can report metrics.",
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-      "bytes": 3103,
-      "sha256": "8355831cecd3e10a56ca4a0ac0b00fcfc0773c89fbc11fedb25183954daa59da"
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@@ -863,8 +930,8 @@
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docs/data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T21:31:33+00:00",
+  "generated_at_utc": "2026-06-06T14:37:36+00:00",
   "hf_root": "hf_publish",
   "summary": {
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+    "group_count": 104,
     "failure_count": 0,
     "failures_by_surface": {}
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@@ -40,27 +40,27 @@
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-        "bytes": 6120,
-        "sha256": "ec918f6849ca888e314a6481e10ddab68d88ba766cbe6a97265df6891058e1df"
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-          "sha256": "ec918f6849ca888e314a6481e10ddab68d88ba766cbe6a97265df6891058e1df"
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-          "bytes": 6120,
-          "sha256": "ec918f6849ca888e314a6481e10ddab68d88ba766cbe6a97265df6891058e1df"
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-          "bytes": 6120,
-          "sha256": "ec918f6849ca888e314a6481e10ddab68d88ba766cbe6a97265df6891058e1df"
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@@ -102,27 +102,27 @@
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-        "sha256": "01559066191f8b0f6c150bffe33106230ebebd564d00acfd6066606486500f8e"
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-          "bytes": 34764,
-          "sha256": "01559066191f8b0f6c150bffe33106230ebebd564d00acfd6066606486500f8e"
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-          "bytes": 34764,
-          "sha256": "01559066191f8b0f6c150bffe33106230ebebd564d00acfd6066606486500f8e"
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-          "bytes": 34764,
-          "sha256": "01559066191f8b0f6c150bffe33106230ebebd564d00acfd6066606486500f8e"
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@@ -164,27 +164,27 @@
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-          "sha256": "d027ab3ee0b147957b1196211261949ff607fc7a2bb43ff3b92ee46e5a902fa0"
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-          "sha256": "d027ab3ee0b147957b1196211261949ff607fc7a2bb43ff3b92ee46e5a902fa0"
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@@ -195,27 +195,27 @@
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-          "sha256": "e1c729bfba5105db35035b09e3c3d2ae74375dc75dc6577d8bd79fd7801be0a6"
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-          "sha256": "e1c729bfba5105db35035b09e3c3d2ae74375dc75dc6577d8bd79fd7801be0a6"
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@@ -257,27 +257,27 @@
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-        "bytes": 12981,
-        "sha256": "9cce52025a2e2f8afb4660e2af3353aea6ad0a1af380849218dd74c0acc271bb"
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-          "sha256": "9cce52025a2e2f8afb4660e2af3353aea6ad0a1af380849218dd74c0acc271bb"
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       "failures": []
@@ -344,33 +344,64 @@
       },
       "failures": []
     },
+    {
+      "name": "data/omni_finetune_verified_result.json",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/data/omni_finetune_verified_result.json",
+        "exists": true,
+        "bytes": 3145,
+        "sha256": "37b001a24201ba56b327fa89f19792d64ebcdabc1faffa7e7bb4fd6b8323731a"
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+          "bytes": 3145,
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+          "bytes": 3145,
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+          "bytes": 5101,
+          "sha256": "29856af67d4c2e4ea8f339e5a9bde362da08bbd17b1a3c681ee9b4aa579559f0"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:RESEARCH_TAKEAWAYS.md",
           "exists": true,
-          "bytes": 5006,
-          "sha256": "7eb264fe323a6a4bef9fed9e2104d0eb3977f38e9b152d1e5bbf5246ebb1aa8a"
+          "bytes": 5101,
+          "sha256": "29856af67d4c2e4ea8f339e5a9bde362da08bbd17b1a3c681ee9b4aa579559f0"
         },
         "hf_model": {
           "path": "hf_model:RESEARCH_TAKEAWAYS.md",
           "exists": true,
-          "bytes": 5006,
-          "sha256": "7eb264fe323a6a4bef9fed9e2104d0eb3977f38e9b152d1e5bbf5246ebb1aa8a"
+          "bytes": 5101,
+          "sha256": "29856af67d4c2e4ea8f339e5a9bde362da08bbd17b1a3c681ee9b4aa579559f0"
         }
       },
       "failures": []

docs/data/project_status.json

@@ -106,6 +106,17 @@
       ],
       "readout": "Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is added as the first world-model/action-generation branch; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit."
     },
+    {
+      "area": "Omni model extension contract",
+      "status": "current",
+      "evidence": [
+        "OMNI_MODEL_EXTENSION_CONTRACT.md",
+        "configs/omni_backbones/",
+        "scripts/omni/backbone_registry.py",
+        "scripts/omni/smoke_test_backbone_packaging.py"
+      ],
+      "readout": "Future Qwen, Cosmos-style, and VLA/policy branches must keep the same episode split discipline, held-out metrics, validation gate, public-safe package contract, and explicit forbidden-artifact policy before reporting results."
+    },
     {
       "area": "Xperience Embodied Foundation Model",
       "status": "future_goal",
@@ -190,6 +201,7 @@
     "Inspect RESEARCH_TAKEAWAYS.md and docs/data/research_takeaways.json before interpreting model scores.",
     "Inspect RESEARCH_ROADMAP.md and docs/data/research_roadmap.json for the path from public-sample task work to multi-episode modeling.",
     "Inspect FOUNDATION_MODEL_PLAN.md and docs/data/foundation_model_plan.json before choosing a backbone branch.",
+    "Inspect OMNI_MODEL_EXTENSION_CONTRACT.md and run python scripts/omni/backbone_registry.py --validate --json before adding a new Qwen, Cosmos-style, or VLA/policy branch.",
     "Inspect XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md for the long-term full-corpus pretraining goal.",
     "Inspect docs/data/summary_metrics.json and results/episode_task_suite/neural_mlp/ to check the 12-task outputs.",
     "Inspect results/audio_ablation/AUDIO_ABLATION_SUMMARY.md before judging whether audio helps the current task suite.",
@@ -205,6 +217,7 @@
     "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/.",
     "Foundation-model selection is explicit: Qwen3-Omni is the immediate trainable pilot, Cosmos 3 is the first world-model branch, and policy models such as OpenVLA/openpi/GR00T wait for action-target conversion.",
+    "Future model branches should be added through the backbone registry and verified package contract, not as one-off result folders with incompatible metrics or publication rules.",
     "The Xperience Embodied Foundation Model is a future native-pretraining goal, not a completed model or current benchmark."
   ]
 }

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T21:31:36+00:00",
+  "generated_at_utc": "2026-06-06T14:38:05+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -182,8 +182,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 401,
-      "text_file_count": 335,
+      "file_count": 442,
+      "text_file_count": 372,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -193,8 +193,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 319,
-      "text_file_count": 253,
+      "file_count": 356,
+      "text_file_count": 286,
       "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": 420,
-      "text_file_count": 332,
+      "file_count": 514,
+      "text_file_count": 420,
       "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": 648,
-      "text_file_count": 523,
+      "file_count": 701,
+      "text_file_count": 572,
       "largest_file": {
         "path": "pytorch_model.bin",
         "bytes": 93495480

docs/data/scope_claims_audit.json

@@ -1,75 +1,69 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T16:48:58+00:00",
+  "generated_at_utc": "2026-06-06T14:35:59+00:00",
   "summary": {
-    "qwen3_omni_32_episode_claim": false,
-    "dataset_manifest_num_episodes": 1,
-    "dataset_manifest_num_samples": 128,
-    "training_metadata_num_train_samples": 128,
-    "source_discovery_ready_for_32_episode_pilot": false,
-    "historical_identifier_count": 159,
-    "public_32_episode_status_file_count": 3,
+    "qwen3_omni_verified_diagnostic_pilot": true,
+    "dataset_manifest_num_episodes": 119,
+    "dataset_manifest_num_samples": 3808,
+    "training_metadata_num_train_samples": 2848,
+    "eval_num_samples": 448,
+    "eval_json_validity_rate": 0.875,
+    "quality_target_met": false,
+    "historical_identifier_count": 132,
+    "public_32_episode_status_file_count": 1,
     "failure_count": 0
   },
   "checks": [
     {
-      "name": "project_manifest_records_pending_32_episode_qwen_result",
+      "name": "project_packet_records_verified_diagnostic_status",
       "status": "pass",
-      "detail": "project_manifest scope_boundary.qwen3_omni_32_episode_claim=False",
-      "evidence": [
-        "docs/data/project_manifest.json"
-      ]
-    },
-    {
-      "name": "project_packet_records_pending_32_episode_qwen_result",
-      "status": "pass",
-      "detail": "project_packet scope_status.qwen3_omni_32_episode_claim=False",
+      "detail": "project packet describes the verified diagnostic pilot and quality boundary",
       "evidence": [
         "docs/data/project_packet.json"
       ]
     },
     {
-      "name": "project_packet_describes_32_episode_setup_status",
+      "name": "summary_metrics_preserves_verified_diagnostic_status",
       "status": "pass",
-      "detail": "project packet describes the setup-stage Qwen3-Omni run separately from the planned 32-episode fine-tune",
+      "detail": "The selected-episode Qwen3-Omni validation-aware diagnostic pilot is verified, but held-out quality is still weak and JSON validity remains below the 98% target.",
       "evidence": [
-        "docs/data/project_packet.json"
+        "docs/data/summary_metrics.json"
       ]
     },
     {
-      "name": "summary_metrics_preserves_omni_scale_up_status",
+      "name": "verified_package_dataset_has_expected_windows",
       "status": "pass",
-      "detail": "The selected-episode Qwen3-Omni fine-tune requires completed data preparation and held-out evaluation; the 32-episode Qwen3-Omni fine-tune requires gated data preparation before any real held-out metric is reported.",
+      "detail": "episodes=119, samples=3808, split_counts={'train': 2848, 'val': 512, 'test': 448}",
       "evidence": [
-        "docs/data/summary_metrics.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/dataset_manifest.json"
       ]
     },
     {
-      "name": "omni_dataset_manifest_is_setup_stage",
+      "name": "verified_package_training_records_8_processes",
       "status": "pass",
-      "detail": "episodes=1, samples=128, split_counts={'train': 128}",
+      "detail": "train=2848, val=512, processes=8",
       "evidence": [
-        "results/omni_finetune/dataset_manifest.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/training/training_metadata.json"
       ]
     },
     {
-      "name": "omni_training_metadata_is_setup_stage",
+      "name": "verified_package_eval_records_real_held_out_metrics",
       "status": "pass",
-      "detail": "train=128, val=0, processes=8",
+      "detail": "samples=448, split=test, held_out=14, json_validity=0.875",
       "evidence": [
-        "results/omni_finetune/training_metadata.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/eval/metrics.json"
       ]
     },
     {
-      "name": "source_discovery_gate_is_closed",
+      "name": "verified_package_audit_passes",
       "status": "pass",
-      "detail": "ready_for_32_episode_pilot=False, local_valid=1",
+      "detail": "audit_status=pass, issues=0",
       "evidence": [
-        "results/omni_finetune/source_discovery.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/package_audit.json"
       ]
     },
     {
-      "name": "public_presentation_has_no_historical_32ep_identifiers",
+      "name": "public_presentation_has_no_misleading_32ep_identifiers",
       "status": "pass",
       "detail": "public presentation scan failures=0",
       "evidence": [
@@ -90,7 +84,7 @@
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
-      "detail": "historical identifiers found in result provenance files=159",
+      "detail": "historical identifiers found in result provenance files=132",
       "evidence": [
         "results/omni_finetune/"
       ]
@@ -100,14 +94,6 @@
     {
       "path": "docs/data/artifact_index.json",
       "contains_32_episode_status_text": true
-    },
-    {
-      "path": "docs/data/project_packet.json",
-      "contains_32_episode_status_text": true
-    },
-    {
-      "path": "docs/data/summary_metrics.json",
-      "contains_32_episode_status_text": true
     }
   ],
   "historical_identifiers": [
@@ -123,313 +109,320 @@
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT.md",
-      "line": 4,
+      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
+      "line": 42,
       "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`"
+      "example": "export RELAY_WORKDIR=/path/to/ropedia-episode-task-suite"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT.md",
-      "line": 5,
+      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
+      "line": 45,
       "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_eval.md",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 1,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:0\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 0, \"end_frame\": 19, \"num_frames\": 20}, \"media\": {\"video_paths\": [{"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_eval.md",
-      "line": 5,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 2,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:1\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 20, \"end_frame\": 39, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_lora.md",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 3,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:2\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 40, \"end_frame\": 59, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
-      "line": 42,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 4,
       "patterns": [
+        "qwen3_omni_32ep",
+        "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "export RELAY_WORKDIR=/path/to/ropedia-episode-task-suite"
+      "example": "{\"id\": \"xperience-10m-sample:qa:3\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 60, \"end_frame\": 79, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
-      "line": 45,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 5,
       "patterns": [
+        "qwen3_omni_32ep",
+        "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
+      "example": "{\"id\": \"xperience-10m-sample:qa:4\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 80, \"end_frame\": 99, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 1,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 6,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "run_id: xperience10m_qwen3_omni_32ep_lora"
+      "example": "{\"id\": \"xperience-10m-sample:qa:5\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 100, \"end_frame\": 119, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 7,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "dataset_jsonl: results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl"
+      "example": "{\"id\": \"xperience-10m-sample:qa:6\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 120, \"end_frame\": 139, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 5,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 8,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "checkpoint_dir: /path/to/ropedia_workspace/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora"
+      "example": "{\"id\": \"xperience-10m-sample:qa:7\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 140, \"end_frame\": 159, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 1,
+      "line": 9,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:0\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 0, \"end_frame\": 19, \"num_frames\": 20}, \"media\": {\"video_paths\": [{"
+      "example": "{\"id\": \"xperience-10m-sample:qa:8\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 160, \"end_frame\": 179, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 2,
+      "line": 10,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:1\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 20, \"end_frame\": 39, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:9\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 180, \"end_frame\": 199, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 3,
+      "line": 11,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:2\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 40, \"end_frame\": 59, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:10\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 200, \"end_frame\": 219, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 4,
+      "line": 12,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:3\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 60, \"end_frame\": 79, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:11\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 220, \"end_frame\": 239, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 5,
+      "line": 13,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:4\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 80, \"end_frame\": 99, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:12\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 240, \"end_frame\": 259, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 6,
+      "line": 14,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:5\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 100, \"end_frame\": 119, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:13\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 260, \"end_frame\": 279, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 7,
+      "line": 15,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:6\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 120, \"end_frame\": 139, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:14\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 280, \"end_frame\": 299, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 8,
+      "line": 16,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:7\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 140, \"end_frame\": 159, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:15\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 300, \"end_frame\": 319, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 9,
+      "line": 17,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:8\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 160, \"end_frame\": 179, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:40\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 800, \"end_frame\": 819, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 10,
+      "line": 18,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:9\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 180, \"end_frame\": 199, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:41\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 820, \"end_frame\": 839, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 11,
+      "line": 19,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:10\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 200, \"end_frame\": 219, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:42\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 840, \"end_frame\": 859, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 12,
+      "line": 20,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:11\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 220, \"end_frame\": 239, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:43\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 860, \"end_frame\": 879, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 13,
+      "line": 21,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:12\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 240, \"end_frame\": 259, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:44\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 880, \"end_frame\": 899, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 14,
+      "line": 22,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:13\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 260, \"end_frame\": 279, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:45\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 900, \"end_frame\": 919, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 15,
+      "line": 23,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:14\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 280, \"end_frame\": 299, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:46\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 920, \"end_frame\": 939, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 16,
+      "line": 24,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:15\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 300, \"end_frame\": 319, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:47\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 940, \"end_frame\": 959, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 17,
+      "line": 25,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:40\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 800, \"end_frame\": 819, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:50\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1000, \"end_frame\": 1019, \"num_frames\": 20}, \"media\": {\"video_path"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 18,
+      "line": 26,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:41\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 820, \"end_frame\": 839, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:51\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1020, \"end_frame\": 1039, \"num_frames\": 20}, \"media\": {\"video_path"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 19,
+      "line": 27,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:42\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 840, \"end_frame\": 859, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:52\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1040, \"end_frame\": 1059, \"num_frames\": 20}, \"media\": {\"video_path"
     }
   ],
-  "historical_identifier_total_count": 159,
+  "historical_identifier_total_count": 132,
   "failures": []
 }

docs/data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-03T20:36:23+00:00",
+  "generated_at_utc": "2026-06-06T14:35:59+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,
@@ -64,45 +64,45 @@
       "observed": "timeline_action"
     },
     {
-      "name": "timeline_action: public_field_output_short_is_human_readable",
+      "name": "timeline_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current action class",
+      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_plain_goal_is_human_readable",
+      "name": "timeline_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Look at one short multimodal window and name what action is happening now.",
+      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_process_short_is_human_readable",
+      "name": "timeline_action: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "window features -> action label builder -> classifier",
+      "value": "Action Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_research_name_is_human_readable",
+      "name": "timeline_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Egocentric Action Recognition",
+      "value": "current action class",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_card_blurb_is_human_readable",
+      "name": "timeline_action: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
+      "value": "Egocentric Action Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_display_name_is_human_readable",
+      "name": "timeline_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Action Recognition",
+      "value": "Look at one short multimodal window and name what action is happening now.",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_input_short_is_human_readable",
+      "name": "timeline_action: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
+      "value": "window features -> action label builder -> classifier",
       "raw_hits": []
     },
     {
@@ -184,45 +184,45 @@
       "observed": "timeline_subtask"
     },
     {
-      "name": "timeline_subtask: public_field_output_short_is_human_readable",
+      "name": "timeline_subtask: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current procedure step",
+      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
+      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict the higher-level task stage for the current window.",
+      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_process_short_is_human_readable",
+      "name": "timeline_subtask: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "window features -> subtask label builder -> classifier",
+      "value": "Procedure Step Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_research_name_is_human_readable",
+      "name": "timeline_subtask: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Subtask Recognition",
+      "value": "current procedure step",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
+      "name": "timeline_subtask: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
+      "value": "Temporal Subtask Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_display_name_is_human_readable",
+      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Procedure Step Recognition",
+      "value": "Predict the higher-level task stage for the current window.",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_input_short_is_human_readable",
+      "name": "timeline_subtask: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
+      "value": "window features -> subtask label builder -> classifier",
       "raw_hits": []
     },
     {
@@ -304,45 +304,45 @@
       "observed": "transition_detection"
     },
     {
-      "name": "transition_detection: public_field_output_short_is_human_readable",
+      "name": "transition_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "boundary or steady",
+      "value": "current window with boundary target",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_plain_goal_is_human_readable",
+      "name": "transition_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Detect whether the current window is near a boundary between actions.",
+      "value": "Detect the local moment where the episode changes from one action segment to the next.",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_process_short_is_human_readable",
+      "name": "transition_detection: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "action changes -> boundary labels -> binary classifier",
+      "value": "Action Boundary Detection",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_research_name_is_human_readable",
+      "name": "transition_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Action Segmentation",
+      "value": "boundary or steady",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_card_blurb_is_human_readable",
+      "name": "transition_detection: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Detect the local moment where the episode changes from one action segment to the next.",
+      "value": "Temporal Action Segmentation",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_display_name_is_human_readable",
+      "name": "transition_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Action Boundary Detection",
+      "value": "Detect whether the current window is near a boundary between actions.",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_input_short_is_human_readable",
+      "name": "transition_detection: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current window with boundary target",
+      "value": "action changes -> boundary labels -> binary classifier",
       "raw_hits": []
     },
     {
@@ -422,45 +422,45 @@
       "observed": "next_action"
     },
     {
-      "name": "next_action: public_field_output_short_is_human_readable",
+      "name": "next_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "action at t+20 frames",
+      "value": "current window at time t",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_plain_goal_is_human_readable",
+      "name": "next_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Use the current window to guess the action that will happen shortly after it.",
+      "value": "Forecast the near-future action from the current observations only.",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_process_short_is_human_readable",
+      "name": "next_action: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "current features -> future label shift -> classifier",
+      "value": "Next-Action Prediction",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_research_name_is_human_readable",
+      "name": "next_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Short-Horizon Intention Prediction",
+      "value": "action at t+20 frames",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_card_blurb_is_human_readable",
+      "name": "next_action: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Forecast the near-future action from the current observations only.",
+      "value": "Short-Horizon Intention Prediction",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_display_name_is_human_readable",
+      "name": "next_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Next-Action Prediction",
+      "value": "Use the current window to guess the action that will happen shortly after it.",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_input_short_is_human_readable",
+      "name": "next_action: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current window at time t",
+      "value": "current features -> future label shift -> classifier",
       "raw_hits": []
     },
     {
@@ -540,45 +540,45 @@
       "observed": "hand_trajectory_forecast"
     },
     {
-      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "future hand-joint trajectory",
+      "value": "current multimodal window",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict where the hands will move over the next few frames.",
+      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_process_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "current features -> future mocap target -> regression head",
+      "value": "Hand Trajectory Forecasting",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "3D Hand Motion Forecasting",
+      "value": "future hand-joint trajectory",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
+      "value": "3D Hand Motion Forecasting",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_display_name_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Hand Trajectory Forecasting",
+      "value": "Predict where the hands will move over the next few frames.",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current multimodal window",
+      "value": "current features -> future mocap target -> regression head",
       "raw_hits": []
     },
     {
@@ -658,45 +658,45 @@
       "observed": "contact_prediction"
     },
     {
-      "name": "contact_prediction: public_field_output_short_is_human_readable",
+      "name": "contact_prediction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "contact or no contact",
+      "value": "non-contact, non-caption features",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
+      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict whether the body or hand is in contact with something.",
+      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_process_short_is_human_readable",
+      "name": "contact_prediction: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "feature filter -> contact target -> binary classifier",
+      "value": "Contact State Prediction",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_research_name_is_human_readable",
+      "name": "contact_prediction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Human-Object Contact Prediction",
+      "value": "contact or no contact",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
+      "name": "contact_prediction: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
+      "value": "Human-Object Contact Prediction",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_display_name_is_human_readable",
+      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Contact State Prediction",
+      "value": "Predict whether the body or hand is in contact with something.",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_input_short_is_human_readable",
+      "name": "contact_prediction: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "non-contact, non-caption features",
+      "value": "feature filter -> contact target -> binary classifier",
       "raw_hits": []
     },
     {
@@ -774,45 +774,45 @@
       "observed": "object_relevance"
     },
     {
-      "name": "object_relevance: public_field_output_short_is_human_readable",
+      "name": "object_relevance: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "relevant object set",
+      "value": "non-caption multimodal features",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_plain_goal_is_human_readable",
+      "name": "object_relevance: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict which objects matter in the current window.",
+      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_process_short_is_human_readable",
+      "name": "object_relevance: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "object vocabulary -> multi-hot labels -> sigmoid heads",
+      "value": "Object Relevance Prediction",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_research_name_is_human_readable",
+      "name": "object_relevance: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Object-Centric Interaction Recognition",
+      "value": "relevant object set",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_card_blurb_is_human_readable",
+      "name": "object_relevance: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
+      "value": "Object-Centric Interaction Recognition",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_display_name_is_human_readable",
+      "name": "object_relevance: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Object Relevance Prediction",
+      "value": "Predict which objects matter in the current window.",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_input_short_is_human_readable",
+      "name": "object_relevance: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "non-caption multimodal features",
+      "value": "object vocabulary -> multi-hot labels -> sigmoid heads",
       "raw_hits": []
     },
     {
@@ -892,45 +892,45 @@
       "observed": "caption_grounding"
     },
     {
-      "name": "caption_grounding: public_field_output_short_is_human_readable",
+      "name": "caption_grounding: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "ranked matching moments",
+      "value": "text-like query and candidate windows",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
+      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Given a text-like query from annotation, find the matching time window.",
+      "value": "Retrieve the matching time window for an annotation-derived text query.",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_process_short_is_human_readable",
+      "name": "caption_grounding: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "query features -> candidate index -> cosine ranker",
+      "value": "Language Grounding",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_research_name_is_human_readable",
+      "name": "caption_grounding: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Language-to-Moment Grounding",
+      "value": "ranked matching moments",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
+      "name": "caption_grounding: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Retrieve the matching time window for an annotation-derived text query.",
+      "value": "Language-to-Moment Grounding",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_display_name_is_human_readable",
+      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Language Grounding",
+      "value": "Given a text-like query from annotation, find the matching time window.",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_input_short_is_human_readable",
+      "name": "caption_grounding: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "text-like query and candidate windows",
+      "value": "query features -> candidate index -> cosine ranker",
       "raw_hits": []
     },
     {
@@ -1008,45 +1008,45 @@
       "observed": "cross_modal_retrieval"
     },
     {
-      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "ranked visual windows",
+      "value": "motion/IMU/pose query; depth/video candidates",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Use one group of modalities to retrieve the matching window from another group.",
+      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_process_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "modality split -> projection -> nearest-neighbor ranker",
+      "value": "Cross-Modal Retrieval",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Multimodal Representation Retrieval",
+      "value": "ranked visual windows",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
+      "value": "Multimodal Representation Retrieval",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_display_name_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Retrieval",
+      "value": "Use one group of modalities to retrieve the matching window from another group.",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion/IMU/pose query; depth/video candidates",
+      "value": "modality split -> projection -> nearest-neighbor ranker",
       "raw_hits": []
     },
     {
@@ -1126,45 +1126,45 @@
       "observed": "modality_reconstruction"
     },
     {
-      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "reconstructed depth/video vector",
+      "value": "motion, IMU, and camera/pose features",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
+      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict one modality feature block from other modality blocks.",
+      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_process_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "source-target split -> scaler -> regression head",
+      "value": "Cross-Modal Reconstruction",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
+      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Modality Feature Reconstruction",
+      "value": "reconstructed depth/video vector",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
+      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
+      "value": "Modality Feature Reconstruction",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_display_name_is_human_readable",
+      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Reconstruction",
+      "value": "Predict one modality feature block from other modality blocks.",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion, IMU, and camera/pose features",
+      "value": "source-target split -> scaler -> regression head",
       "raw_hits": []
     },
     {
@@ -1244,45 +1244,45 @@
       "observed": "temporal_order"
     },
     {
-      "name": "temporal_order: public_field_output_short_is_human_readable",
+      "name": "temporal_order: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "correct or reversed",
+      "value": "two adjacent windows plus difference vector",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_plain_goal_is_human_readable",
+      "name": "temporal_order: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two nearby windows are in the correct time order.",
+      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_process_short_is_human_readable",
+      "name": "temporal_order: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "pair builder -> feature combiner -> binary classifier",
+      "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_research_name_is_human_readable",
+      "name": "temporal_order: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Order Verification",
+      "value": "correct or reversed",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_card_blurb_is_human_readable",
+      "name": "temporal_order: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
+      "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_display_name_is_human_readable",
+      "name": "temporal_order: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Temporal Order Verification",
+      "value": "Tell whether two nearby windows are in the correct time order.",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_input_short_is_human_readable",
+      "name": "temporal_order: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "two adjacent windows plus difference vector",
+      "value": "pair builder -> feature combiner -> binary classifier",
       "raw_hits": []
     },
     {
@@ -1360,45 +1360,45 @@
       "observed": "misalignment_detection"
     },
     {
-      "name": "misalignment_detection: public_field_output_short_is_human_readable",
+      "name": "misalignment_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "aligned or shifted",
+      "value": "motion-side and visual/depth-side feature groups",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
+      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Detect when modalities that should match are shifted out of sync.",
+      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_process_short_is_human_readable",
+      "name": "misalignment_detection: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "aligned/shifted pairs -> feature combiner -> binary classifier",
+      "value": "Multimodal Synchronization Detection",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_research_name_is_human_readable",
+      "name": "misalignment_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Misalignment Detection",
+      "value": "aligned or shifted",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
+      "name": "misalignment_detection: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
+      "value": "Cross-Modal Misalignment Detection",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_display_name_is_human_readable",
+      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Multimodal Synchronization Detection",
+      "value": "Detect when modalities that should match are shifted out of sync.",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_input_short_is_human_readable",
+      "name": "misalignment_detection: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion-side and visual/depth-side feature groups",
+      "value": "aligned/shifted pairs -> feature combiner -> binary classifier",
       "raw_hits": []
     },
     {

docs/data/website_integrity.json

@@ -1,13 +1,13 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T21:31:33+00:00",
+  "generated_at_utc": "2026-06-06T14:36:10+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 128,
-    "external_reference_count": 112,
-    "json_files": 33,
+    "local_references": 133,
+    "external_reference_count": 107,
+    "json_files": 34,
     "image_assets_referenced": 22,
     "failure_count": 0
   },
@@ -75,7 +75,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 67412,
-      "evidence_index": 88788
+      "evidence_index": 89661
     },
     {
       "name": "project_status_links_json",
@@ -119,13 +119,13 @@
       "name": "reader_resources_link_qwen_status",
       "status": "pass",
       "reason": "The website should expose the Qwen3-Omni scale-up status.",
-      "marker_count": 9
+      "marker_count": 5
     },
     {
       "name": "roadmap_html_matches_json_phases",
       "status": "pass",
       "reason": "The roadmap section should show every stage defined in research_roadmap.json.",
-      "phase_count": 7,
+      "phase_count": 8,
       "missing_phase_names": [],
       "roadmap_json_error": null
     },
@@ -133,11 +133,12 @@
       "name": "roadmap_status_chips_match_json",
       "status": "pass",
       "reason": "The roadmap status chips should match the phase statuses in research_roadmap.json.",
-      "phase_count": 7,
+      "phase_count": 8,
       "statuses": [
         "implemented",
-        "active",
-        "next",
+        "implemented_for_first_pilot",
+        "verified_baseline",
+        "active_next_step",
         "next",
         "planned",
         "planned",
@@ -151,8 +152,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 67412,
-      "protocol_index": 85478,
-      "evidence_index": 88788
+      "protocol_index": 86399,
+      "evidence_index": 89661
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -226,7 +227,7 @@
     {
       "path": "index.html",
       "id_count": 77,
-      "reference_count": 105,
+      "reference_count": 110,
       "image_count": 24
     },
     {
@@ -245,12 +246,12 @@
   "json_files": [
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       "path": "data/additional_development_directions.json",
-      "bytes": 6120,
+      "bytes": 6121,
       "top_level_type": "dict"
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     {
       "path": "data/artifact_index.json",
-      "bytes": 34764,
+      "bytes": 37736,
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@@ -265,12 +266,12 @@
     },
     {
       "path": "data/evaluation_protocol.json",
-      "bytes": 13606,
+      "bytes": 13788,
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     {
       "path": "data/evidence_contract.json",
-      "bytes": 12147,
+      "bytes": 12219,
       "top_level_type": "dict"
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     {
@@ -280,7 +281,7 @@
     },
     {
       "path": "data/foundation_model_plan.json",
-      "bytes": 12981,
+      "bytes": 13112,
       "top_level_type": "dict"
     },
     {
@@ -290,7 +291,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 110838,
+      "bytes": 111950,
       "top_level_type": "dict"
     },
     {
@@ -298,24 +299,29 @@
       "bytes": 3797,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/omni_finetune_verified_result.json",
+      "bytes": 3145,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/project_brief.json",
-      "bytes": 3637,
+      "bytes": 3752,
       "top_level_type": "dict"
     },
     {
       "path": "data/project_manifest.json",
-      "bytes": 4643,
+      "bytes": 4927,
       "top_level_type": "dict"
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     {
       "path": "data/project_packet.json",
-      "bytes": 7648,
+      "bytes": 7802,
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     },
     {
       "path": "data/project_status.json",
-      "bytes": 9874,
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     {
@@ -340,7 +346,7 @@
     },
     {
       "path": "data/reproducibility_matrix.json",
-      "bytes": 5265,
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       "top_level_type": "dict"
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     {
@@ -355,22 +361,22 @@
     },
     {
       "path": "data/research_roadmap.json",
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       "path": "data/research_roadmap_interactive.json",
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     {
       "path": "data/scope_claims_audit.json",
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+      "bytes": 20823,
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@@ -385,7 +391,7 @@
     },
     {
       "path": "data/summary_metrics.json",
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+      "bytes": 26028,
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@@ -400,7 +406,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 15982,
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metrics/artifact_index.json

@@ -1,11 +1,12 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-04T21:22:15+00:00",
+  "generated_at_utc": "2026-06-06T14:35:42+00:00",
   "status": "pass",
-  "artifact_count": 77,
+  "artifact_count": 83,
   "missing": [],
   "by_kind": {
     "project_path": 14,
+    "scaleup_contract": 6,
     "project_scope": 1,
     "source_alignment": 5,
     "publication_workflow": 3,
@@ -40,8 +41,8 @@
       "surface": "repo_hf",
       "shows": "Gives first-pass readers a concise project shape before the detailed artifact trail.",
       "exists": true,
-      "bytes": 3594,
-      "sha256": "8df692f2d6b092097d5ef7d5039e7529abe2fd0a80ff3fb23fa14260fb5f9de2"
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+      "sha256": "3d16e700c31aafe889b3d8e43d52250208766809c483a716a13cbd26961b3e72"
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     {
       "id": "project_brief_json",
@@ -51,8 +52,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable first-reader project brief for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 3637,
-      "sha256": "0e859cfd3fca3ac66c9dd67e04a404ab41757c248d8777ee9cf7da63ab11a2f7"
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+      "sha256": "4bd2f3471d0141abf64a2327541e25031e9efa39940cc68a29c316cf36f7c884"
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     {
       "id": "project_status",
@@ -62,8 +63,8 @@
       "surface": "repo_hf",
       "shows": "Gives a compact current-state table for first-pass readers.",
       "exists": true,
-      "bytes": 7207,
-      "sha256": "7baaba976ccc254da1a03ee2653057d1e08f3fb0c0cad035886c362442828720"
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+      "sha256": "5eb48d489da7f005baab233a94c9d6b209eb1e9ffdb138c8e0e600ece9239a29"
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     {
       "id": "project_status_json",
@@ -73,8 +74,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable copy of the current project status for website and HF mirrors.",
       "exists": true,
-      "bytes": 9874,
-      "sha256": "600c95726eae3404127a8b2110f35468ff2ba02943cae0fbcd3ea43c66109d3e"
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+      "sha256": "2bb0639c137dfd6eddd337eb909292543ae2e72753dee398f8240ff35f6a3984"
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     {
       "id": "research_roadmap",
@@ -84,8 +85,8 @@
       "surface": "repo_hf",
       "shows": "Defines the path from public-sample task development to multi-episode held-out evaluation and larger omni-model extensions.",
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-      "bytes": 10259,
-      "sha256": "6e26acb051a45a5afe36fa0c8b3c60f58c383c2e58bee6e01bac28b7dd604f0a"
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+      "sha256": "4e3451a2248176e78b0587a8a15ead2df682068d0833a21e624ac955c5ae72a3"
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     {
       "id": "research_roadmap_json",
@@ -95,8 +96,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable research roadmap for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 7699,
-      "sha256": "69b777b4d01e2e587236c1bb2206a47e2b9a0e1a5f1c7c6e125b0dd3270ce0d5"
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+      "sha256": "92f94edda5337aa0f49e136748320cb1c2c013b7a12b5fe36cf556f5b1bb0142"
     },
     {
       "id": "foundation_model_plan",
@@ -117,8 +118,74 @@
       "surface": "website_hf",
       "shows": "Machine-readable foundation-model selection matrix with source links, entry conditions, and evaluation additions.",
       "exists": true,
-      "bytes": 12981,
-      "sha256": "9cce52025a2e2f8afb4660e2af3353aea6ad0a1af380849218dd74c0acc271bb"
+      "bytes": 13112,
+      "sha256": "0850a9b74f2c62aba30b297089ad0a4a04424cc16d4a265f71e39538c6d22792"
+    },
+    {
+      "id": "omni_model_extension_contract",
+      "title": "Omni model extension contract",
+      "path": "OMNI_MODEL_EXTENSION_CONTRACT.md",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Defines the shared manifest, episode split, held-out evaluation, packaging, and public-safety rules for Qwen3-Omni, Cosmos-style, and VLA/policy model branches.",
+      "exists": true,
+      "bytes": 8900,
+      "sha256": "c4e51d0aa7536045c229418603a67c6b3c5f31c9d756ca7395cb0c9455f0ed6d"
+    },
+    {
+      "id": "omni_backbone_registry_configs",
+      "title": "Omni backbone registry configs",
+      "path": "configs/omni_backbones",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Stores the implemented Qwen3-Omni LoRA contract and planned Cosmos-style world-model and VLA/policy branch contracts.",
+      "exists": true,
+      "file_count": 3,
+      "bytes": 8904
+    },
+    {
+      "id": "omni_backbone_registry_validator",
+      "title": "Omni backbone registry validator",
+      "path": "scripts/omni/backbone_registry.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Validates backbone ids, split defaults, leakage guards, required metrics, required files, and forbidden public package categories.",
+      "exists": true,
+      "bytes": 8740,
+      "sha256": "3bc9eeb47471fb995f767d5420a556a95413c1cb2d8b3ea9d2805429df55beb8"
+    },
+    {
+      "id": "omni_model_neutral_window_index_exporter",
+      "title": "Model-neutral window index exporter",
+      "path": "scripts/omni/export_model_neutral_window_index.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Converts Qwen JSONL records into a model-neutral window index with Qwen, Cosmos-style, and policy/VLA adapter views.",
+      "exists": true,
+      "bytes": 8540,
+      "sha256": "ec6dd4fcdc1c9a90566a59ef118f7ed5b78ebf8843752873aea8ae8b5f71a34d"
+    },
+    {
+      "id": "omni_backbone_scaffolder",
+      "title": "Omni backbone scaffolder",
+      "path": "scripts/omni/scaffold_omni_backbone.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Creates a validated planned-backbone config from an existing contract template so new model families inherit the shared rules.",
+      "exists": true,
+      "bytes": 4809,
+      "sha256": "2cd34a7db1510392c6caba735fb903b2a6b78aeff8a748ebf20bcd372b0f8f2d"
+    },
+    {
+      "id": "omni_backbone_packaging_smoke",
+      "title": "Omni backbone packaging smoke test",
+      "path": "scripts/omni/smoke_test_backbone_packaging.py",
+      "kind": "scaleup_contract",
+      "surface": "repo_hf",
+      "shows": "Builds synthetic verified packages for every configured backbone and audits them against the public-safe package contract.",
+      "exists": true,
+      "bytes": 6519,
+      "sha256": "a3773fc681e298325e2be80556d6be6e7e30b90ba22ee24b66633f07ff9c4ea4"
     },
     {
       "id": "additional_development_directions",
@@ -128,8 +195,8 @@
       "surface": "repo_hf",
       "shows": "Records concrete non-backbone Xperience-10M development tracks: taxonomy, benchmark protocol, representation learning, skill graphs, affordances, 3D/4D memory, QA, and policy transfer.",
       "exists": true,
-      "bytes": 3136,
-      "sha256": "9766e5537d7ec5a78548d52ea618443dae8e811bb68c4cb8361be0dba7136935"
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     {
       "id": "additional_development_directions_json",
@@ -139,8 +206,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable additional development directions for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 6120,
-      "sha256": "ec918f6849ca888e314a6481e10ddab68d88ba766cbe6a97265df6891058e1df"
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     },
     {
       "id": "xperience_embodied_foundation_pretraining",
@@ -172,8 +239,8 @@
       "surface": "website_hf",
       "shows": "Gives a short project path with scope status and public surfaces.",
       "exists": true,
-      "bytes": 7648,
-      "sha256": "2a609a6d7bdfcddab225468d226fb5eed8d66bb90616850eae86aab9e9c3b945"
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     },
     {
       "id": "artifact_guide",
@@ -183,8 +250,8 @@
       "surface": "repo_hf",
       "shows": "Gives the human-readable map from project scope to data, tasks, platform mirrors, and scale-up status.",
       "exists": true,
-      "bytes": 12275,
-      "sha256": "7b4db6893ee0d13db32216f20a8287ffa442764cfd20475e1dc3b17c8e10a974"
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     {
       "id": "official_dataset_card_alignment",
@@ -249,8 +316,8 @@
       "surface": "repo_hf",
       "shows": "Publishes prepared Space, artifact dataset, and model bundles, including an explicit model-binary upload batch.",
       "exists": true,
-      "bytes": 15901,
-      "sha256": "60f27a4d32c48e6960445bfafe917ce30d6a43211153146a52ae068f051de4e8"
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     },
     {
       "id": "github_package_dockerfile",
@@ -282,8 +349,8 @@
       "surface": "repo_hf",
       "shows": "Defines the window unit, chronological split, task metrics, leakage controls, and current limitations.",
       "exists": true,
-      "bytes": 5879,
-      "sha256": "a1eb4c2c43c47da84ec6225bddd6f3c43d0d30aef0781f6682102cf9a34b47e1"
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+      "sha256": "b0dce21ec27228e49693c1aefed1be120c73f80551d6d3609c48c473d792e709"
     },
     {
       "id": "evaluation_protocol_json",
@@ -293,8 +360,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable protocol generated from committed task metrics for website and HF mirrors.",
       "exists": true,
-      "bytes": 13606,
-      "sha256": "e1c729bfba5105db35035b09e3c3d2ae74375dc75dc6577d8bd79fd7801be0a6"
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     },
     {
       "id": "evaluation_protocol_builder",
@@ -304,8 +371,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the protocol from committed summary metrics and task artifacts.",
       "exists": true,
-      "bytes": 16148,
-      "sha256": "c7aa188da50c77ad85c4a6a8be2342111a5b8206c6c2d39e9737fb02e0016f3d"
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+      "sha256": "0e404d53826ea893ed1a9d6f07b3e98cdf16b64b37088480a1b8ddb957997164"
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     {
       "id": "research_takeaways",
@@ -315,8 +382,8 @@
       "surface": "repo_hf",
       "shows": "Summarizes the main research lessons from committed metrics and identifies which experiments need held-out episodes.",
       "exists": true,
-      "bytes": 5006,
-      "sha256": "7eb264fe323a6a4bef9fed9e2104d0eb3977f38e9b152d1e5bbf5246ebb1aa8a"
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     {
       "id": "research_takeaways_json",
@@ -326,8 +393,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable result interpretation for the website, HF cards, and mirror checks.",
       "exists": true,
-      "bytes": 6938,
-      "sha256": "c839e4f163878028c6a250294161571ca18c86d5b45726d85a69420d26b2ebd6"
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     {
       "id": "research_takeaways_builder",
@@ -337,8 +404,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the research takeaways from committed summary metrics and task result artifacts.",
       "exists": true,
-      "bytes": 13289,
-      "sha256": "3dbc93f1cae8479f64ad25a5abef14cd35c023e122e3934c6f709c4058382791"
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       "id": "audio_ablation_script",
@@ -606,8 +673,8 @@
       "surface": "repo_hf",
       "shows": "Defines public reproduction commands, expected outputs, and non-reproducible scale-up boundaries.",
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-      "sha256": "19ca27c3771d22843ee8257aa1cd8ae66e17609a84f6d78e70c55deac2a6ce5b"
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       "id": "reproducibility_matrix",
@@ -617,8 +684,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable reproduction steps with expected artifacts and public boundaries.",
       "exists": true,
-      "bytes": 5265,
-      "sha256": "28feab95f4c080a658d0db49d817cb4564557638a7d177a806ed704508dca4dc"
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     {
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@@ -628,8 +695,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 28464,
-      "sha256": "a74480209e3185b00aaa50dbf93313010f49ce3dd39e1c0d856536302abf9398"
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     {
       "id": "publication_audit",
@@ -652,7 +719,7 @@
       "volatile": true,
       "shows": "Separates setup paths from completed held-out-episode results.",
       "exists": true,
-      "bytes": 18981,
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       "hash_policy": "existence_and_size_only"
     },
     {
@@ -664,7 +731,7 @@
       "volatile": true,
       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
-      "bytes": 108621,
+      "bytes": 111950,
       "hash_policy": "existence_and_size_only"
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     {
@@ -676,7 +743,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
       "exists": true,
-      "bytes": 14891,
+      "bytes": 15221,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -687,8 +754,8 @@
       "surface": "website_hf",
       "shows": "Lists public URLs, upstream sources, and machine-readable project metadata.",
       "exists": true,
-      "bytes": 4643,
-      "sha256": "b02ca0064bb6eea07abdcf4ff4aacde06a291f94679b79ca599052848a269b49"
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     {
       "id": "task_summary",
@@ -709,8 +776,8 @@
       "surface": "website_hf",
       "shows": "Mirrors task metrics for the static dashboard.",
       "exists": true,
-      "bytes": 25301,
-      "sha256": "966042a203b6290ddb763ee827597752ed518c90791ffa61e55f519c810594a3"
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+      "sha256": "5259cf5373cb07ce6fad2bed69c35bfa77550515949588326f792d5d6043c082"
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     {
       "id": "feature_manifest",
@@ -850,10 +917,10 @@
       "path": "results/omni_finetune/DATA_ACCESS_STATUS.md",
       "kind": "scaleup_status",
       "surface": "repo_hf",
-      "shows": "Summarizes the staging requirement before the held-out Qwen3-Omni pilot can report metrics.",
+      "shows": "Summarizes the data-readiness checks required before a held-out Qwen3-Omni pilot can report metrics.",
       "exists": true,
-      "bytes": 3103,
-      "sha256": "8355831cecd3e10a56ca4a0ac0b00fcfc0773c89fbc11fedb25183954daa59da"
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     {
       "id": "multi_episode_access_status",
@@ -863,8 +930,8 @@
       "surface": "repo_hf",
       "shows": "Documents the public multi-episode access status and 32-episode pilot selection.",
       "exists": true,
-      "bytes": 2339,
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metrics/mirror_parity.json

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@@ -257,27 +257,27 @@
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@@ -344,33 +344,64 @@
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@@ -3030,27 +3061,27 @@
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metrics/project_status.json

@@ -106,6 +106,17 @@
       ],
       "readout": "Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is added as the first world-model/action-generation branch; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit."
     },
+    {
+      "area": "Omni model extension contract",
+      "status": "current",
+      "evidence": [
+        "OMNI_MODEL_EXTENSION_CONTRACT.md",
+        "configs/omni_backbones/",
+        "scripts/omni/backbone_registry.py",
+        "scripts/omni/smoke_test_backbone_packaging.py"
+      ],
+      "readout": "Future Qwen, Cosmos-style, and VLA/policy branches must keep the same episode split discipline, held-out metrics, validation gate, public-safe package contract, and explicit forbidden-artifact policy before reporting results."
+    },
     {
       "area": "Xperience Embodied Foundation Model",
       "status": "future_goal",
@@ -190,6 +201,7 @@
     "Inspect RESEARCH_TAKEAWAYS.md and docs/data/research_takeaways.json before interpreting model scores.",
     "Inspect RESEARCH_ROADMAP.md and docs/data/research_roadmap.json for the path from public-sample task work to multi-episode modeling.",
     "Inspect FOUNDATION_MODEL_PLAN.md and docs/data/foundation_model_plan.json before choosing a backbone branch.",
+    "Inspect OMNI_MODEL_EXTENSION_CONTRACT.md and run python scripts/omni/backbone_registry.py --validate --json before adding a new Qwen, Cosmos-style, or VLA/policy branch.",
     "Inspect XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md for the long-term full-corpus pretraining goal.",
     "Inspect docs/data/summary_metrics.json and results/episode_task_suite/neural_mlp/ to check the 12-task outputs.",
     "Inspect results/audio_ablation/AUDIO_ABLATION_SUMMARY.md before judging whether audio helps the current task suite.",
@@ -205,6 +217,7 @@
     "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/.",
     "Foundation-model selection is explicit: Qwen3-Omni is the immediate trainable pilot, Cosmos 3 is the first world-model branch, and policy models such as OpenVLA/openpi/GR00T wait for action-target conversion.",
+    "Future model branches should be added through the backbone registry and verified package contract, not as one-off result folders with incompatible metrics or publication rules.",
     "The Xperience Embodied Foundation Model is a future native-pretraining goal, not a completed model or current benchmark."
   ]
 }

metrics/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T21:31:36+00:00",
+  "generated_at_utc": "2026-06-06T14:38:05+00:00",
   "checks": [
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@@ -182,8 +182,8 @@
     "github_repo": {
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       "exists": true,
-      "file_count": 401,
-      "text_file_count": 335,
+      "file_count": 442,
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       "largest_file": {
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         "bytes": 55702978
@@ -193,8 +193,8 @@
     "hf_space_bundle": {
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-      "file_count": 319,
-      "text_file_count": 253,
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@@ -204,8 +204,8 @@
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       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 420,
-      "text_file_count": 332,
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+      "text_file_count": 420,
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@@ -215,8 +215,8 @@
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       "exists": true,
-      "file_count": 648,
-      "text_file_count": 523,
+      "file_count": 701,
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         "bytes": 93495480

metrics/scope_claims_audit.json

@@ -1,75 +1,69 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T16:48:58+00:00",
+  "generated_at_utc": "2026-06-06T14:35:59+00:00",
   "summary": {
-    "qwen3_omni_32_episode_claim": false,
-    "dataset_manifest_num_episodes": 1,
-    "dataset_manifest_num_samples": 128,
-    "training_metadata_num_train_samples": 128,
-    "source_discovery_ready_for_32_episode_pilot": false,
-    "historical_identifier_count": 159,
-    "public_32_episode_status_file_count": 3,
+    "qwen3_omni_verified_diagnostic_pilot": true,
+    "dataset_manifest_num_episodes": 119,
+    "dataset_manifest_num_samples": 3808,
+    "training_metadata_num_train_samples": 2848,
+    "eval_num_samples": 448,
+    "eval_json_validity_rate": 0.875,
+    "quality_target_met": false,
+    "historical_identifier_count": 132,
+    "public_32_episode_status_file_count": 1,
     "failure_count": 0
   },
   "checks": [
     {
-      "name": "project_manifest_records_pending_32_episode_qwen_result",
+      "name": "project_packet_records_verified_diagnostic_status",
       "status": "pass",
-      "detail": "project_manifest scope_boundary.qwen3_omni_32_episode_claim=False",
-      "evidence": [
-        "docs/data/project_manifest.json"
-      ]
-    },
-    {
-      "name": "project_packet_records_pending_32_episode_qwen_result",
-      "status": "pass",
-      "detail": "project_packet scope_status.qwen3_omni_32_episode_claim=False",
+      "detail": "project packet describes the verified diagnostic pilot and quality boundary",
       "evidence": [
         "docs/data/project_packet.json"
       ]
     },
     {
-      "name": "project_packet_describes_32_episode_setup_status",
+      "name": "summary_metrics_preserves_verified_diagnostic_status",
       "status": "pass",
-      "detail": "project packet describes the setup-stage Qwen3-Omni run separately from the planned 32-episode fine-tune",
+      "detail": "The selected-episode Qwen3-Omni validation-aware diagnostic pilot is verified, but held-out quality is still weak and JSON validity remains below the 98% target.",
       "evidence": [
-        "docs/data/project_packet.json"
+        "docs/data/summary_metrics.json"
       ]
     },
     {
-      "name": "summary_metrics_preserves_omni_scale_up_status",
+      "name": "verified_package_dataset_has_expected_windows",
       "status": "pass",
-      "detail": "The selected-episode Qwen3-Omni fine-tune requires completed data preparation and held-out evaluation; the 32-episode Qwen3-Omni fine-tune requires gated data preparation before any real held-out metric is reported.",
+      "detail": "episodes=119, samples=3808, split_counts={'train': 2848, 'val': 512, 'test': 448}",
       "evidence": [
-        "docs/data/summary_metrics.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/dataset/dataset_manifest.json"
       ]
     },
     {
-      "name": "omni_dataset_manifest_is_setup_stage",
+      "name": "verified_package_training_records_8_processes",
       "status": "pass",
-      "detail": "episodes=1, samples=128, split_counts={'train': 128}",
+      "detail": "train=2848, val=512, processes=8",
       "evidence": [
-        "results/omni_finetune/dataset_manifest.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/training/training_metadata.json"
       ]
     },
     {
-      "name": "omni_training_metadata_is_setup_stage",
+      "name": "verified_package_eval_records_real_held_out_metrics",
       "status": "pass",
-      "detail": "train=128, val=0, processes=8",
+      "detail": "samples=448, split=test, held_out=14, json_validity=0.875",
       "evidence": [
-        "results/omni_finetune/training_metadata.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/eval/metrics.json"
       ]
     },
     {
-      "name": "source_discovery_gate_is_closed",
+      "name": "verified_package_audit_passes",
       "status": "pass",
-      "detail": "ready_for_32_episode_pilot=False, local_valid=1",
+      "detail": "audit_status=pass, issues=0",
       "evidence": [
-        "results/omni_finetune/source_discovery.json"
+        "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/package_audit.json"
       ]
     },
     {
-      "name": "public_presentation_has_no_historical_32ep_identifiers",
+      "name": "public_presentation_has_no_misleading_32ep_identifiers",
       "status": "pass",
       "detail": "public presentation scan failures=0",
       "evidence": [
@@ -90,7 +84,7 @@
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
-      "detail": "historical identifiers found in result provenance files=159",
+      "detail": "historical identifiers found in result provenance files=132",
       "evidence": [
         "results/omni_finetune/"
       ]
@@ -100,14 +94,6 @@
     {
       "path": "docs/data/artifact_index.json",
       "contains_32_episode_status_text": true
-    },
-    {
-      "path": "docs/data/project_packet.json",
-      "contains_32_episode_status_text": true
-    },
-    {
-      "path": "docs/data/summary_metrics.json",
-      "contains_32_episode_status_text": true
     }
   ],
   "historical_identifiers": [
@@ -123,313 +109,320 @@
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT.md",
-      "line": 4,
+      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
+      "line": 42,
       "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`"
+      "example": "export RELAY_WORKDIR=/path/to/ropedia-episode-task-suite"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT.md",
-      "line": 5,
+      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
+      "line": 45,
       "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_eval.md",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 1,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:0\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 0, \"end_frame\": 19, \"num_frames\": 20}, \"media\": {\"video_paths\": [{"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_eval.md",
-      "line": 5,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 2,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:1\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 20, \"end_frame\": 39, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/RUN_REPORT_lora.md",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 3,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
+      "example": "{\"id\": \"xperience-10m-sample:qa:2\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 40, \"end_frame\": 59, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
-      "line": 42,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 4,
       "patterns": [
+        "qwen3_omni_32ep",
+        "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "export RELAY_WORKDIR=/path/to/ropedia-episode-task-suite"
+      "example": "{\"id\": \"xperience-10m-sample:qa:3\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 60, \"end_frame\": 79, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/XPERIENCE10M_128_DATA_PREPARATION_AND_FINETUNE_PLAN.md",
-      "line": 45,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 5,
       "patterns": [
+        "qwen3_omni_32ep",
+        "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
+      "example": "{\"id\": \"xperience-10m-sample:qa:4\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 80, \"end_frame\": 99, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 1,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 6,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "run_id: xperience10m_qwen3_omni_32ep_lora"
+      "example": "{\"id\": \"xperience-10m-sample:qa:5\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 100, \"end_frame\": 119, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 4,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 7,
       "patterns": [
         "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep"
+        "xperience10m_qwen3_omni_32ep",
+        "ropedia-episode-task-suite"
       ],
-      "example": "dataset_jsonl: results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl"
+      "example": "{\"id\": \"xperience-10m-sample:qa:6\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 120, \"end_frame\": 139, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/config.yaml",
-      "line": 5,
+      "path": "results/omni_finetune/dataset.jsonl",
+      "line": 8,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "checkpoint_dir: /path/to/ropedia_workspace/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora"
+      "example": "{\"id\": \"xperience-10m-sample:qa:7\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 140, \"end_frame\": 159, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 1,
+      "line": 9,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:0\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 0, \"end_frame\": 19, \"num_frames\": 20}, \"media\": {\"video_paths\": [{"
+      "example": "{\"id\": \"xperience-10m-sample:qa:8\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 160, \"end_frame\": 179, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 2,
+      "line": 10,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:1\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 20, \"end_frame\": 39, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:9\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 180, \"end_frame\": 199, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 3,
+      "line": 11,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:2\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 40, \"end_frame\": 59, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:10\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 200, \"end_frame\": 219, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 4,
+      "line": 12,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:3\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 60, \"end_frame\": 79, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:11\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 220, \"end_frame\": 239, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 5,
+      "line": 13,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:4\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 80, \"end_frame\": 99, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
+      "example": "{\"id\": \"xperience-10m-sample:qa:12\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 240, \"end_frame\": 259, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 6,
+      "line": 14,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:5\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 100, \"end_frame\": 119, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:13\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 260, \"end_frame\": 279, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 7,
+      "line": 15,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:6\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 120, \"end_frame\": 139, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:14\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 280, \"end_frame\": 299, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 8,
+      "line": 16,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:7\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 140, \"end_frame\": 159, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:15\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 300, \"end_frame\": 319, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 9,
+      "line": 17,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:8\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 160, \"end_frame\": 179, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:40\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 800, \"end_frame\": 819, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 10,
+      "line": 18,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:9\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 180, \"end_frame\": 199, \"num_frames\": 20}, \"media\": {\"video_paths\":"
+      "example": "{\"id\": \"xperience-10m-sample:qa:41\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 820, \"end_frame\": 839, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 11,
+      "line": 19,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:10\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 200, \"end_frame\": 219, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:42\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 840, \"end_frame\": 859, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 12,
+      "line": 20,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:11\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 220, \"end_frame\": 239, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:43\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 860, \"end_frame\": 879, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 13,
+      "line": 21,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:12\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 240, \"end_frame\": 259, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:44\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 880, \"end_frame\": 899, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 14,
+      "line": 22,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:13\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 260, \"end_frame\": 279, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:45\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 900, \"end_frame\": 919, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 15,
+      "line": 23,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:14\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 280, \"end_frame\": 299, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:46\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 920, \"end_frame\": 939, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 16,
+      "line": 24,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:15\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 300, \"end_frame\": 319, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:47\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 940, \"end_frame\": 959, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 17,
+      "line": 25,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:40\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 800, \"end_frame\": 819, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:50\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1000, \"end_frame\": 1019, \"num_frames\": 20}, \"media\": {\"video_path"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 18,
+      "line": 26,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:41\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 820, \"end_frame\": 839, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:51\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1020, \"end_frame\": 1039, \"num_frames\": 20}, \"media\": {\"video_path"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
-      "line": 19,
+      "line": 27,
       "patterns": [
         "qwen3_omni_32ep",
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:42\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 840, \"end_frame\": 859, \"num_frames\": 20}, \"media\": {\"video_paths\""
+      "example": "{\"id\": \"xperience-10m-sample:qa:52\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1040, \"end_frame\": 1059, \"num_frames\": 20}, \"media\": {\"video_path"
     }
   ],
-  "historical_identifier_total_count": 159,
+  "historical_identifier_total_count": 132,
   "failures": []
 }

metrics/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-03T20:36:23+00:00",
+  "generated_at_utc": "2026-06-06T14:35:59+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,
@@ -64,45 +64,45 @@
       "observed": "timeline_action"
     },
     {
-      "name": "timeline_action: public_field_output_short_is_human_readable",
+      "name": "timeline_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current action class",
+      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_plain_goal_is_human_readable",
+      "name": "timeline_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Look at one short multimodal window and name what action is happening now.",
+      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_process_short_is_human_readable",
+      "name": "timeline_action: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "window features -> action label builder -> classifier",
+      "value": "Action Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_research_name_is_human_readable",
+      "name": "timeline_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Egocentric Action Recognition",
+      "value": "current action class",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_card_blurb_is_human_readable",
+      "name": "timeline_action: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
+      "value": "Egocentric Action Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_display_name_is_human_readable",
+      "name": "timeline_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Action Recognition",
+      "value": "Look at one short multimodal window and name what action is happening now.",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_input_short_is_human_readable",
+      "name": "timeline_action: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
+      "value": "window features -> action label builder -> classifier",
       "raw_hits": []
     },
     {
@@ -184,45 +184,45 @@
       "observed": "timeline_subtask"
     },
     {
-      "name": "timeline_subtask: public_field_output_short_is_human_readable",
+      "name": "timeline_subtask: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current procedure step",
+      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
+      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict the higher-level task stage for the current window.",
+      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_process_short_is_human_readable",
+      "name": "timeline_subtask: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "window features -> subtask label builder -> classifier",
+      "value": "Procedure Step Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_research_name_is_human_readable",
+      "name": "timeline_subtask: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Subtask Recognition",
+      "value": "current procedure step",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
+      "name": "timeline_subtask: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
+      "value": "Temporal Subtask Recognition",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_display_name_is_human_readable",
+      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Procedure Step Recognition",
+      "value": "Predict the higher-level task stage for the current window.",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_input_short_is_human_readable",
+      "name": "timeline_subtask: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
+      "value": "window features -> subtask label builder -> classifier",
       "raw_hits": []
     },
     {
@@ -304,45 +304,45 @@
       "observed": "transition_detection"
     },
     {
-      "name": "transition_detection: public_field_output_short_is_human_readable",
+      "name": "transition_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "boundary or steady",
+      "value": "current window with boundary target",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_plain_goal_is_human_readable",
+      "name": "transition_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Detect whether the current window is near a boundary between actions.",
+      "value": "Detect the local moment where the episode changes from one action segment to the next.",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_process_short_is_human_readable",
+      "name": "transition_detection: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "action changes -> boundary labels -> binary classifier",
+      "value": "Action Boundary Detection",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_research_name_is_human_readable",
+      "name": "transition_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Action Segmentation",
+      "value": "boundary or steady",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_card_blurb_is_human_readable",
+      "name": "transition_detection: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Detect the local moment where the episode changes from one action segment to the next.",
+      "value": "Temporal Action Segmentation",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_display_name_is_human_readable",
+      "name": "transition_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Action Boundary Detection",
+      "value": "Detect whether the current window is near a boundary between actions.",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_input_short_is_human_readable",
+      "name": "transition_detection: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current window with boundary target",
+      "value": "action changes -> boundary labels -> binary classifier",
       "raw_hits": []
     },
     {
@@ -422,45 +422,45 @@
       "observed": "next_action"
     },
     {
-      "name": "next_action: public_field_output_short_is_human_readable",
+      "name": "next_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "action at t+20 frames",
+      "value": "current window at time t",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_plain_goal_is_human_readable",
+      "name": "next_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Use the current window to guess the action that will happen shortly after it.",
+      "value": "Forecast the near-future action from the current observations only.",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_process_short_is_human_readable",
+      "name": "next_action: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "current features -> future label shift -> classifier",
+      "value": "Next-Action Prediction",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_research_name_is_human_readable",
+      "name": "next_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Short-Horizon Intention Prediction",
+      "value": "action at t+20 frames",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_card_blurb_is_human_readable",
+      "name": "next_action: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Forecast the near-future action from the current observations only.",
+      "value": "Short-Horizon Intention Prediction",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_display_name_is_human_readable",
+      "name": "next_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Next-Action Prediction",
+      "value": "Use the current window to guess the action that will happen shortly after it.",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_input_short_is_human_readable",
+      "name": "next_action: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current window at time t",
+      "value": "current features -> future label shift -> classifier",
       "raw_hits": []
     },
     {
@@ -540,45 +540,45 @@
       "observed": "hand_trajectory_forecast"
     },
     {
-      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "future hand-joint trajectory",
+      "value": "current multimodal window",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict where the hands will move over the next few frames.",
+      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_process_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "current features -> future mocap target -> regression head",
+      "value": "Hand Trajectory Forecasting",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "3D Hand Motion Forecasting",
+      "value": "future hand-joint trajectory",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
+      "value": "3D Hand Motion Forecasting",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_display_name_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Hand Trajectory Forecasting",
+      "value": "Predict where the hands will move over the next few frames.",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
+      "name": "hand_trajectory_forecast: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "current multimodal window",
+      "value": "current features -> future mocap target -> regression head",
       "raw_hits": []
     },
     {
@@ -658,45 +658,45 @@
       "observed": "contact_prediction"
     },
     {
-      "name": "contact_prediction: public_field_output_short_is_human_readable",
+      "name": "contact_prediction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "contact or no contact",
+      "value": "non-contact, non-caption features",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
+      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict whether the body or hand is in contact with something.",
+      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_process_short_is_human_readable",
+      "name": "contact_prediction: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "feature filter -> contact target -> binary classifier",
+      "value": "Contact State Prediction",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_research_name_is_human_readable",
+      "name": "contact_prediction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Human-Object Contact Prediction",
+      "value": "contact or no contact",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
+      "name": "contact_prediction: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
+      "value": "Human-Object Contact Prediction",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_display_name_is_human_readable",
+      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Contact State Prediction",
+      "value": "Predict whether the body or hand is in contact with something.",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_input_short_is_human_readable",
+      "name": "contact_prediction: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "non-contact, non-caption features",
+      "value": "feature filter -> contact target -> binary classifier",
       "raw_hits": []
     },
     {
@@ -774,45 +774,45 @@
       "observed": "object_relevance"
     },
     {
-      "name": "object_relevance: public_field_output_short_is_human_readable",
+      "name": "object_relevance: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "relevant object set",
+      "value": "non-caption multimodal features",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_plain_goal_is_human_readable",
+      "name": "object_relevance: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict which objects matter in the current window.",
+      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_process_short_is_human_readable",
+      "name": "object_relevance: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "object vocabulary -> multi-hot labels -> sigmoid heads",
+      "value": "Object Relevance Prediction",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_research_name_is_human_readable",
+      "name": "object_relevance: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Object-Centric Interaction Recognition",
+      "value": "relevant object set",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_card_blurb_is_human_readable",
+      "name": "object_relevance: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
+      "value": "Object-Centric Interaction Recognition",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_display_name_is_human_readable",
+      "name": "object_relevance: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Object Relevance Prediction",
+      "value": "Predict which objects matter in the current window.",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_input_short_is_human_readable",
+      "name": "object_relevance: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "non-caption multimodal features",
+      "value": "object vocabulary -> multi-hot labels -> sigmoid heads",
       "raw_hits": []
     },
     {
@@ -892,45 +892,45 @@
       "observed": "caption_grounding"
     },
     {
-      "name": "caption_grounding: public_field_output_short_is_human_readable",
+      "name": "caption_grounding: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "ranked matching moments",
+      "value": "text-like query and candidate windows",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
+      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Given a text-like query from annotation, find the matching time window.",
+      "value": "Retrieve the matching time window for an annotation-derived text query.",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_process_short_is_human_readable",
+      "name": "caption_grounding: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "query features -> candidate index -> cosine ranker",
+      "value": "Language Grounding",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_research_name_is_human_readable",
+      "name": "caption_grounding: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Language-to-Moment Grounding",
+      "value": "ranked matching moments",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
+      "name": "caption_grounding: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Retrieve the matching time window for an annotation-derived text query.",
+      "value": "Language-to-Moment Grounding",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_display_name_is_human_readable",
+      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Language Grounding",
+      "value": "Given a text-like query from annotation, find the matching time window.",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_input_short_is_human_readable",
+      "name": "caption_grounding: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "text-like query and candidate windows",
+      "value": "query features -> candidate index -> cosine ranker",
       "raw_hits": []
     },
     {
@@ -1008,45 +1008,45 @@
       "observed": "cross_modal_retrieval"
     },
     {
-      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "ranked visual windows",
+      "value": "motion/IMU/pose query; depth/video candidates",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Use one group of modalities to retrieve the matching window from another group.",
+      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_process_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "modality split -> projection -> nearest-neighbor ranker",
+      "value": "Cross-Modal Retrieval",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Multimodal Representation Retrieval",
+      "value": "ranked visual windows",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
+      "value": "Multimodal Representation Retrieval",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_display_name_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Retrieval",
+      "value": "Use one group of modalities to retrieve the matching window from another group.",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
+      "name": "cross_modal_retrieval: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion/IMU/pose query; depth/video candidates",
+      "value": "modality split -> projection -> nearest-neighbor ranker",
       "raw_hits": []
     },
     {
@@ -1126,45 +1126,45 @@
       "observed": "modality_reconstruction"
     },
     {
-      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "reconstructed depth/video vector",
+      "value": "motion, IMU, and camera/pose features",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
+      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict one modality feature block from other modality blocks.",
+      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_process_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "source-target split -> scaler -> regression head",
+      "value": "Cross-Modal Reconstruction",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
+      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Modality Feature Reconstruction",
+      "value": "reconstructed depth/video vector",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
+      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
+      "value": "Modality Feature Reconstruction",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_display_name_is_human_readable",
+      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Reconstruction",
+      "value": "Predict one modality feature block from other modality blocks.",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
+      "name": "modality_reconstruction: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion, IMU, and camera/pose features",
+      "value": "source-target split -> scaler -> regression head",
       "raw_hits": []
     },
     {
@@ -1244,45 +1244,45 @@
       "observed": "temporal_order"
     },
     {
-      "name": "temporal_order: public_field_output_short_is_human_readable",
+      "name": "temporal_order: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "correct or reversed",
+      "value": "two adjacent windows plus difference vector",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_plain_goal_is_human_readable",
+      "name": "temporal_order: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two nearby windows are in the correct time order.",
+      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_process_short_is_human_readable",
+      "name": "temporal_order: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "pair builder -> feature combiner -> binary classifier",
+      "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_research_name_is_human_readable",
+      "name": "temporal_order: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Temporal Order Verification",
+      "value": "correct or reversed",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_card_blurb_is_human_readable",
+      "name": "temporal_order: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
+      "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_display_name_is_human_readable",
+      "name": "temporal_order: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Temporal Order Verification",
+      "value": "Tell whether two nearby windows are in the correct time order.",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_input_short_is_human_readable",
+      "name": "temporal_order: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "two adjacent windows plus difference vector",
+      "value": "pair builder -> feature combiner -> binary classifier",
       "raw_hits": []
     },
     {
@@ -1360,45 +1360,45 @@
       "observed": "misalignment_detection"
     },
     {
-      "name": "misalignment_detection: public_field_output_short_is_human_readable",
+      "name": "misalignment_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "aligned or shifted",
+      "value": "motion-side and visual/depth-side feature groups",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
+      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Detect when modalities that should match are shifted out of sync.",
+      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_process_short_is_human_readable",
+      "name": "misalignment_detection: public_field_display_name_is_human_readable",
       "status": "pass",
-      "value": "aligned/shifted pairs -> feature combiner -> binary classifier",
+      "value": "Multimodal Synchronization Detection",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_research_name_is_human_readable",
+      "name": "misalignment_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "Cross-Modal Misalignment Detection",
+      "value": "aligned or shifted",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
+      "name": "misalignment_detection: public_field_research_name_is_human_readable",
       "status": "pass",
-      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
+      "value": "Cross-Modal Misalignment Detection",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_display_name_is_human_readable",
+      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Multimodal Synchronization Detection",
+      "value": "Detect when modalities that should match are shifted out of sync.",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_input_short_is_human_readable",
+      "name": "misalignment_detection: public_field_process_short_is_human_readable",
       "status": "pass",
-      "value": "motion-side and visual/depth-side feature groups",
+      "value": "aligned/shifted pairs -> feature combiner -> binary classifier",
       "raw_hits": []
     },
     {

metrics/website_integrity.json

@@ -1,13 +1,13 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-04T21:31:33+00:00",
+  "generated_at_utc": "2026-06-06T14:36:10+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 128,
-    "external_reference_count": 112,
-    "json_files": 33,
+    "local_references": 133,
+    "external_reference_count": 107,
+    "json_files": 34,
     "image_assets_referenced": 22,
     "failure_count": 0
   },
@@ -75,7 +75,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 67412,
-      "evidence_index": 88788
+      "evidence_index": 89661
     },
     {
       "name": "project_status_links_json",
@@ -119,13 +119,13 @@
       "name": "reader_resources_link_qwen_status",
       "status": "pass",
       "reason": "The website should expose the Qwen3-Omni scale-up status.",
-      "marker_count": 9
+      "marker_count": 5
     },
     {
       "name": "roadmap_html_matches_json_phases",
       "status": "pass",
       "reason": "The roadmap section should show every stage defined in research_roadmap.json.",
-      "phase_count": 7,
+      "phase_count": 8,
       "missing_phase_names": [],
       "roadmap_json_error": null
     },
@@ -133,11 +133,12 @@
       "name": "roadmap_status_chips_match_json",
       "status": "pass",
       "reason": "The roadmap status chips should match the phase statuses in research_roadmap.json.",
-      "phase_count": 7,
+      "phase_count": 8,
       "statuses": [
         "implemented",
-        "active",
-        "next",
+        "implemented_for_first_pilot",
+        "verified_baseline",
+        "active_next_step",
         "next",
         "planned",
         "planned",
@@ -151,8 +152,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 67412,
-      "protocol_index": 85478,
-      "evidence_index": 88788
+      "protocol_index": 86399,
+      "evidence_index": 89661
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -226,7 +227,7 @@
     {
       "path": "index.html",
       "id_count": 77,
-      "reference_count": 105,
+      "reference_count": 110,
       "image_count": 24
     },
     {
@@ -245,12 +246,12 @@
   "json_files": [
     {
       "path": "data/additional_development_directions.json",
-      "bytes": 6120,
+      "bytes": 6121,
       "top_level_type": "dict"
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 34764,
+      "bytes": 37736,
       "top_level_type": "dict"
     },
     {
@@ -265,12 +266,12 @@
     },
     {
       "path": "data/evaluation_protocol.json",
-      "bytes": 13606,
+      "bytes": 13788,
       "top_level_type": "dict"
     },
     {
       "path": "data/evidence_contract.json",
-      "bytes": 12147,
+      "bytes": 12219,
       "top_level_type": "dict"
     },
     {
@@ -280,7 +281,7 @@
     },
     {
       "path": "data/foundation_model_plan.json",
-      "bytes": 12981,
+      "bytes": 13112,
       "top_level_type": "dict"
     },
     {
@@ -290,7 +291,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 110838,
+      "bytes": 111950,
       "top_level_type": "dict"
     },
     {
@@ -298,24 +299,29 @@
       "bytes": 3797,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/omni_finetune_verified_result.json",
+      "bytes": 3145,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/project_brief.json",
-      "bytes": 3637,
+      "bytes": 3752,
       "top_level_type": "dict"
     },
     {
       "path": "data/project_manifest.json",
-      "bytes": 4643,
+      "bytes": 4927,
       "top_level_type": "dict"
     },
     {
       "path": "data/project_packet.json",
-      "bytes": 7648,
+      "bytes": 7802,
       "top_level_type": "dict"
     },
     {
       "path": "data/project_status.json",
-      "bytes": 9874,
+      "bytes": 10977,
       "top_level_type": "dict"
     },
     {
@@ -340,7 +346,7 @@
     },
     {
       "path": "data/reproducibility_matrix.json",
-      "bytes": 5265,
+      "bytes": 5223,
       "top_level_type": "dict"
     },
     {
@@ -355,22 +361,22 @@
     },
     {
       "path": "data/research_roadmap.json",
-      "bytes": 7699,
+      "bytes": 8981,
       "top_level_type": "dict"
     },
     {
       "path": "data/research_roadmap_interactive.json",
-      "bytes": 141224,
+      "bytes": 142418,
       "top_level_type": "dict"
     },
     {
       "path": "data/research_takeaways.json",
-      "bytes": 6938,
+      "bytes": 7102,
       "top_level_type": "dict"
     },
     {
       "path": "data/scope_claims_audit.json",
-      "bytes": 18981,
+      "bytes": 20823,
       "top_level_type": "dict"
     },
     {
@@ -385,7 +391,7 @@
     },
     {
       "path": "data/summary_metrics.json",
-      "bytes": 25301,
+      "bytes": 26028,
       "top_level_type": "dict"
     },
     {
@@ -400,7 +406,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 15982,
+      "bytes": 15221,
       "top_level_type": "dict"
     },
     {

scripts/build_artifact_index.py

@@ -81,6 +81,54 @@ ARTIFACTS = [
         "surface": "website_hf",
         "shows": "Machine-readable foundation-model selection matrix with source links, entry conditions, and evaluation additions.",
     },
+    {
+        "id": "omni_model_extension_contract",
+        "title": "Omni model extension contract",
+        "path": "OMNI_MODEL_EXTENSION_CONTRACT.md",
+        "kind": "scaleup_contract",
+        "surface": "repo_hf",
+        "shows": "Defines the shared manifest, episode split, held-out evaluation, packaging, and public-safety rules for Qwen3-Omni, Cosmos-style, and VLA/policy model branches.",
+    },
+    {
+        "id": "omni_backbone_registry_configs",
+        "title": "Omni backbone registry configs",
+        "path": "configs/omni_backbones",
+        "kind": "scaleup_contract",
+        "surface": "repo_hf",
+        "shows": "Stores the implemented Qwen3-Omni LoRA contract and planned Cosmos-style world-model and VLA/policy branch contracts.",
+    },
+    {
+        "id": "omni_backbone_registry_validator",
+        "title": "Omni backbone registry validator",
+        "path": "scripts/omni/backbone_registry.py",
+        "kind": "scaleup_contract",
+        "surface": "repo_hf",
+        "shows": "Validates backbone ids, split defaults, leakage guards, required metrics, required files, and forbidden public package categories.",
+    },
+    {
+        "id": "omni_model_neutral_window_index_exporter",
+        "title": "Model-neutral window index exporter",
+        "path": "scripts/omni/export_model_neutral_window_index.py",
+        "kind": "scaleup_contract",
+        "surface": "repo_hf",
+        "shows": "Converts Qwen JSONL records into a model-neutral window index with Qwen, Cosmos-style, and policy/VLA adapter views.",
+    },
+    {
+        "id": "omni_backbone_scaffolder",
+        "title": "Omni backbone scaffolder",
+        "path": "scripts/omni/scaffold_omni_backbone.py",
+        "kind": "scaleup_contract",
+        "surface": "repo_hf",
+        "shows": "Creates a validated planned-backbone config from an existing contract template so new model families inherit the shared rules.",
+    },
+    {
+        "id": "omni_backbone_packaging_smoke",
+        "title": "Omni backbone packaging smoke test",
+        "path": "scripts/omni/smoke_test_backbone_packaging.py",
+        "kind": "scaleup_contract",
+        "surface": "repo_hf",
+        "shows": "Builds synthetic verified packages for every configured backbone and audits them against the public-safe package contract.",
+    },
     {
         "id": "additional_development_directions",
         "title": "Additional development directions",
@@ -616,7 +664,7 @@ ARTIFACTS = [
         "path": "results/omni_finetune/DATA_ACCESS_STATUS.md",
         "kind": "scaleup_status",
         "surface": "repo_hf",
-        "shows": "Summarizes the staging requirement before the held-out Qwen3-Omni pilot can report metrics.",
+        "shows": "Summarizes the data-readiness checks required before a held-out Qwen3-Omni pilot can report metrics.",
     },
     {
         "id": "multi_episode_access_status",