Datasets:

cy0307
/

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

@@ -5,6 +5,25 @@ research task lab for embodied AI. It is designed to answer a practical
 question: what can be built, measured, and extended from a richly synchronized
 egocentric episode before scaling to held-out multi-episode training?
 ## What Exists Now
 | Layer | Current artifact |

 question: what can be built, measured, and extended from a richly synchronized
 egocentric episode before scaling to held-out multi-episode training?
+## Research Intent
+The public sample is treated as a small but real research system. The project
+does not try to inflate one episode into a final benchmark. Instead, it shows
+the full path from data inspection to task design, baseline modeling,
+evaluation, artifact packaging, and a guarded scale-up plan. The intended
+reader should be able to audit one model input, understand each task, reproduce
+the public-sample results, and see exactly what remains before multi-episode
+model-quality claims.
+## Capability Map
+| Capability | Evidence in this project |
+| --- | --- |
+| Data understanding | `feature_manifest.json`, `available_modalities.json`, modality atlas, episode-window HF viewer |
+| Task design | 12 task contracts, task cards, case-study walkthroughs, and four research-direction extension probes |
+| Evaluation rigor | chronological split, per-task metrics, predictions, confusion matrices, leakage notes, and generated takeaways |
+| Scale-up planning | 128-episode selection/relay plan, Qwen3-Omni path, Cosmos 3 branch, and policy-model candidates after action-space conversion |
 ## What Exists Now
 | Layer | Current artifact |

PROJECT_README.md CHANGED Viewed

@@ -25,6 +25,25 @@ The central research questions are:
 - What baseline models and evaluation files should exist before scaling to
   Qwen3-Omni or other multimodal foundation-model fine-tuning?
 ## Start Here
 For a first pass, use [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md) or the

 - What baseline models and evaluation files should exist before scaling to
   Qwen3-Omni or other multimodal foundation-model fine-tuning?
+## Why This Project Exists
+This project is organized as a compact research artifact around Xperience-10M:
+start from a real public episode, make every modality and label path inspectable,
+turn the data into concrete embodied-AI tasks, and keep the evaluation boundary
+clear while preparing the next multi-episode experiments. The emphasis is on
+research judgment as much as implementation: what the sample can show, what it
+cannot show, and what evidence should exist before claiming model quality.
+The work is designed to demonstrate four capabilities that matter for
+embodied-AI research infrastructure:
+| Capability | What this project shows |
+| --- | --- |
+| Multimodal data understanding | Parses the public sample into synchronized windows across video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals |
+| Task design | Defines 12 human-readable tasks plus four direction-extension probes with inputs, outputs, process modules, metrics, and case-study walkthroughs |
+| Model and evaluation discipline | Runs minimal and compact neural baselines, records predictions/metrics, keeps chronological split boundaries explicit, and separates sample evidence from held-out claims |
+| Scale-up planning | Connects the public-sample pipeline to 32/128-episode held-out pilots, Qwen3-Omni LoRA, Cosmos-style world-model branches, and later policy-model branches |
 ## Start Here
 For a first pass, use [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md) or the

PROJECT_STATUS.md CHANGED Viewed

@@ -5,6 +5,15 @@ It summarizes what has already been implemented from the public
 Xperience-10M sample, what is being staged for multi-episode training, and
 which artifacts support the next development step.
 | Area | Current state | Evidence | Research readout |
 | --- | --- | --- | --- |
 | Public-sample pipeline | Verified | `results/episode_task_suite/summary_report.json`, `results/episode_task_suite/windows.csv`, `results/episode_task_suite/feature_manifest.json` | One public Xperience-10M sample episode is converted into 5,821 frames, 1,161 aligned 20-frame windows, and an 8,546-dimensional current feature contract. |

 Xperience-10M sample, what is being staged for multi-episode training, and
 which artifacts support the next development step.
+## Research Positioning
+The project is a research-engineering study of Xperience-10M rather than a
+single demo result. It makes the public sample episode inspectable, defines
+embodied-AI tasks over synchronized modalities, records baseline behavior, and
+keeps the next multi-episode modeling stage explicit. The current evidence is
+useful for judging data understanding, task design, evaluation discipline, and
+scale-up readiness; it is not presented as final full-dataset model quality.
 | Area | Current state | Evidence | Research readout |
 | --- | --- | --- | --- |
 | Public-sample pipeline | Verified | `results/episode_task_suite/summary_report.json`, `results/episode_task_suite/windows.csv`, `results/episode_task_suite/feature_manifest.json` | One public Xperience-10M sample episode is converted into 5,821 frames, 1,161 aligned 20-frame windows, and an 8,546-dimensional current feature contract. |

docs/data/artifact_index.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-03T18:11:35+00:00",
   "status": "pass",
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   "missing": [],
@@ -40,8 +40,8 @@
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 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
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@@ -1,7 +1,7 @@
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 {
   "title": "Ropedia Xperience-10M Project Brief",
   "summary": "A concise first-reader brief for the public-sample embodied-AI task lab and its multi-episode scale-up path.",
+  "research_intent": "Treat the public Xperience-10M sample as a small but real research system: inspect synchronized modalities, define embodied-AI task contracts, run bounded baselines, package evidence, and prepare held-out multi-episode scale-up without presenting one episode as final model quality.",
+  "capability_map": [
+    {
+      "capability": "Data understanding",
+      "evidence": "feature_manifest.json, available_modalities.json, modality atlas, and the episode-window HF viewer"
+    },
+    {
+      "capability": "Task design",
+      "evidence": "12 task contracts, task cards, case-study walkthroughs, and four research-direction extension probes"
+    },
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+      "capability": "Evaluation rigor",
+      "evidence": "chronological split, per-task metrics, predictions, confusion matrices, leakage notes, and generated takeaways"
+    },
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+      "capability": "Scale-up planning",
+      "evidence": "128-episode selection/relay plan, Qwen3-Omni path, Cosmos 3 branch, and policy-model candidates after action-space conversion"
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docs/data/project_packet.json CHANGED Viewed

@@ -142,7 +142,7 @@
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     "Cross-environment generalization is evaluated in the later multi-episode stage.",
-    "The Qwen3-Omni setup run is separate from the planned held-out fine-tune.",
     "Feature-vector reconstruction is separate from pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
     "Raw Xperience-10M data is not redistributed in this repo."
   ]

   },
   "current_reading_notes": [
     "Cross-environment generalization is evaluated in the later multi-episode stage.",
+    "The Qwen3-Omni setup run is separate from the planned held-out 32-episode fine-tune and still requires gated data staging before any real 32-episode result is reported.",
     "Feature-vector reconstruction is separate from pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
     "Raw Xperience-10M data is not redistributed in this repo."
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   "version": "2026-06-01",
   "decision": "public_sample_pipeline_verified_multi_episode_omni_data_staging",
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   "title": "Ropedia Xperience-10M Task Suite Project Status",
   "version": "2026-06-01",
   "decision": "public_sample_pipeline_verified_multi_episode_omni_data_staging",
+  "research_positioning": "A research-engineering study that makes one public Xperience-10M sample episode inspectable, defines embodied-AI tasks over synchronized modalities, records baseline behavior, and keeps later multi-episode model-quality claims separate from current single-episode evidence.",
   "scope_boundary": {
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docs/data/public_surface_qa.json CHANGED Viewed

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
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   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
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@@ -28,27 +28,27 @@
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@@ -100,10 +100,10 @@
       "status": "pass",
       "reason": "Public copy should consistently present the project as Ropedia Xperience-10M, with the Qwen3-Omni scale-up status.",
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-        "Xperience-10M": 128,
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         "128-episode relay": 6
       }
     },

 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
+  "generated_at_utc": "2026-06-03T20:37:22+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
         "website_integrity": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-03T20:35:48+00:00"
         },
         "rendered_site_check": {
           "exists": true,
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-03T20:36:23+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-03T20:36:23+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-03T20:37:04+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-03T20:08:35+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
+          "generated_at_utc": "2026-06-03T20:09:03+00:00"
         },
         "live_publication": {
           "exists": true,
       "status": "pass",
       "reason": "Public copy should consistently present the project as Ropedia Xperience-10M, with the Qwen3-Omni scale-up status.",
       "marker_counts": {
+        "Ropedia Xperience-10M Task Suite": 15,
+        "Xperience-10M": 131,
         "12-task": 33,
+        "Qwen3-Omni": 83,
         "128-episode relay": 6
       }
     },

docs/data/publication_audit.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-03T19:29:55+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -204,7 +204,7 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 410,
       "text_file_count": 325,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",

 {
   "status": "pass",
+  "generated_at_utc": "2026-06-03T20:44:32+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
+      "file_count": 411,
       "text_file_count": 325,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",

docs/data/quality_gates.json CHANGED Viewed

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-03T18:27:05+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
+  "generated_at_utc": "2026-06-03T20:42:40+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

docs/data/scope_claims_audit.json CHANGED Viewed

@@ -1,14 +1,14 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-03T14:43:40+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": 157,
-    "public_32_episode_status_file_count": 10,
     "failure_count": 0
   },
   "checks": [
@@ -39,7 +39,7 @@
     {
       "name": "summary_metrics_preserves_omni_scale_up_status",
       "status": "pass",
-      "detail": "The 32-episode Qwen3-Omni fine-tune requires gated data staging and held-out evaluation.",
       "evidence": [
         "docs/data/summary_metrics.json"
       ]
@@ -90,45 +90,17 @@
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
-      "detail": "historical identifiers found in result provenance files=157",
       "evidence": [
         "results/omni_finetune/"
       ]
     }
   ],
   "public_status_observations": [
-    {
-      "path": "README.md",
-      "contains_32_episode_status_text": true
-    },
-    {
-      "path": "ARTIFACT_GUIDE.md",
-      "contains_32_episode_status_text": true
-    },
-    {
-      "path": "EVIDENCE_CONTRACT.md",
-      "contains_32_episode_status_text": true
-    },
-    {
-      "path": "REPRODUCIBILITY.md",
-      "contains_32_episode_status_text": true
-    },
-    {
-      "path": "docs/index.html",
-      "contains_32_episode_status_text": true
-    },
     {
       "path": "docs/data/artifact_index.json",
       "contains_32_episode_status_text": true
     },
-    {
-      "path": "docs/data/evidence_contract.json",
-      "contains_32_episode_status_text": true
-    },
-    {
-      "path": "docs/data/reproducibility_matrix.json",
-      "contains_32_episode_status_text": true
-    },
     {
       "path": "docs/data/project_packet.json",
       "contains_32_episode_status_text": true
@@ -199,6 +171,24 @@
       ],
       "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
     },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/config.yaml",
@@ -438,30 +428,8 @@
         "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\""
-    },
-    {
-      "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/dataset.jsonl",
-      "line": 20,
-      "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep",
-        "ropedia-episode-task-suite"
-      ],
-      "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": 21,
-      "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep",
-        "ropedia-episode-task-suite"
-      ],
-      "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\""
     }
   ],
-  "historical_identifier_total_count": 157,
   "failures": []
 }

 {
   "status": "pass",
+  "generated_at_utc": "2026-06-03T20:37:04+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,
     "failure_count": 0
   },
   "checks": [
     {
       "name": "summary_metrics_preserves_omni_scale_up_status",
       "status": "pass",
+      "detail": "The selected-episode Qwen3-Omni fine-tune requires completed data staging and held-out evaluation; the 32-episode Qwen3-Omni fine-tune requires gated data staging before any real held-out metric is reported.",
       "evidence": [
         "docs/data/summary_metrics.json"
       ]
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
+      "detail": "historical identifiers found in result provenance files=159",
       "evidence": [
         "results/omni_finetune/"
       ]
     }
   ],
   "public_status_observations": [
     {
       "path": "docs/data/artifact_index.json",
       "contains_32_episode_status_text": true
     },
     {
       "path": "docs/data/project_packet.json",
       "contains_32_episode_status_text": true
       ],
       "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
     },
+    {
+      "classification": "historical_identifier_in_readiness_artifact",
+      "path": "results/omni_finetune/XPERIENCE10M_128_RELAY_AND_FINETUNE_PLAN.md",
+      "line": 42,
+      "patterns": [
+        "ropedia-episode-task-suite"
+      ],
+      "example": "export RELAY_WORKDIR=/path/to/ropedia-episode-task-suite"
+    },
+    {
+      "classification": "historical_identifier_in_readiness_artifact",
+      "path": "results/omni_finetune/XPERIENCE10M_128_RELAY_AND_FINETUNE_PLAN.md",
+      "line": 45,
+      "patterns": [
+        "ropedia-episode-task-suite"
+      ],
+      "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
+    },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/config.yaml",
         "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\""
     }
   ],
+  "historical_identifier_total_count": 159,
   "failures": []
 }

docs/data/source_alignment_audit.json CHANGED Viewed

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-03T17:02:07+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
+  "generated_at_utc": "2026-06-03T20:36:23+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

docs/data/summary_metrics.json CHANGED Viewed

@@ -14,7 +14,7 @@
       "visualization.rrd"
     ],
     "access_status": "Full-dataset access is granted; selected multi-episode relay is in progress with chunked parallel transfer and overlapping batch prefetch.",
-    "current_scope": "The selected-episode Qwen3-Omni fine-tune requires completed data staging and held-out evaluation."
   },
   "models": {
     "motion_action": {

       "visualization.rrd"
     ],
     "access_status": "Full-dataset access is granted; selected multi-episode relay is in progress with chunked parallel transfer and overlapping batch prefetch.",
+    "current_scope": "The selected-episode Qwen3-Omni fine-tune requires completed data staging and held-out evaluation; the 32-episode Qwen3-Omni fine-tune requires gated data staging before any real held-out metric is reported."
   },
   "models": {
     "motion_action": {

docs/data/task_surface_integrity.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-03T14:43:40+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,
@@ -64,21 +64,15 @@
       "observed": "timeline_action"
     },
     {
-      "name": "timeline_action: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Egocentric Action Recognition",
-      "raw_hits": []
-    },
-    {
-      "name": "timeline_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
       "raw_hits": []
     },
     {
@@ -88,15 +82,15 @@
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "current action class",
       "raw_hits": []
     },
     {
-      "name": "timeline_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Look at one short multimodal window and name what action is happening now.",
       "raw_hits": []
     },
     {
@@ -105,6 +99,12 @@
       "value": "Action Recognition",
       "raw_hits": []
     },
     {
       "name": "timeline_action: known_task_family",
       "status": "pass",
@@ -184,21 +184,15 @@
       "observed": "timeline_subtask"
     },
     {
-      "name": "timeline_subtask: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Temporal Subtask Recognition",
-      "raw_hits": []
-    },
-    {
-      "name": "timeline_subtask: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "20-frame multimodal window",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
       "raw_hits": []
     },
     {
@@ -208,15 +202,15 @@
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "current procedure step",
       "raw_hits": []
     },
     {
-      "name": "timeline_subtask: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Predict the higher-level task stage for the current window.",
       "raw_hits": []
     },
     {
@@ -225,6 +219,12 @@
       "value": "Procedure Step Recognition",
       "raw_hits": []
     },
     {
       "name": "timeline_subtask: known_task_family",
       "status": "pass",
@@ -304,21 +304,15 @@
       "observed": "transition_detection"
     },
     {
-      "name": "transition_detection: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Temporal Action Segmentation",
-      "raw_hits": []
-    },
-    {
-      "name": "transition_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current window with boundary target",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Detect the local moment where the episode changes from one action segment to the next.",
       "raw_hits": []
     },
     {
@@ -328,15 +322,15 @@
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "boundary or steady",
       "raw_hits": []
     },
     {
-      "name": "transition_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Detect whether the current window is near a boundary between actions.",
       "raw_hits": []
     },
     {
@@ -345,6 +339,12 @@
       "value": "Action Boundary Detection",
       "raw_hits": []
     },
     {
       "name": "transition_detection: known_task_family",
       "status": "pass",
@@ -422,21 +422,15 @@
       "observed": "next_action"
     },
     {
-      "name": "next_action: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Short-Horizon Intention Prediction",
-      "raw_hits": []
-    },
-    {
-      "name": "next_action: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current window at time t",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Forecast the near-future action from the current observations only.",
       "raw_hits": []
     },
     {
@@ -446,15 +440,15 @@
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "action at t+20 frames",
       "raw_hits": []
     },
     {
-      "name": "next_action: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Use the current window to guess the action that will happen shortly after it.",
       "raw_hits": []
     },
     {
@@ -463,6 +457,12 @@
       "value": "Next-Action Prediction",
       "raw_hits": []
     },
     {
       "name": "next_action: known_task_family",
       "status": "pass",
@@ -540,21 +540,15 @@
       "observed": "hand_trajectory_forecast"
     },
     {
-      "name": "hand_trajectory_forecast: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "3D Hand Motion Forecasting",
-      "raw_hits": []
-    },
-    {
-      "name": "hand_trajectory_forecast: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "current multimodal window",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict the future 3D left/right hand path from the current multimodal state.",
       "raw_hits": []
     },
     {
@@ -564,15 +558,15 @@
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "future hand-joint trajectory",
       "raw_hits": []
     },
     {
-      "name": "hand_trajectory_forecast: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Predict where the hands will move over the next few frames.",
       "raw_hits": []
     },
     {
@@ -581,6 +575,12 @@
       "value": "Hand Trajectory Forecasting",
       "raw_hits": []
     },
     {
       "name": "hand_trajectory_forecast: known_task_family",
       "status": "pass",
@@ -658,21 +658,15 @@
       "observed": "contact_prediction"
     },
     {
-      "name": "contact_prediction: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Human-Object Contact Prediction",
-      "raw_hits": []
-    },
-    {
-      "name": "contact_prediction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "non-contact, non-caption features",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
       "raw_hits": []
     },
     {
@@ -682,15 +676,15 @@
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "contact or no contact",
       "raw_hits": []
     },
     {
-      "name": "contact_prediction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Predict whether the body or hand is in contact with something.",
       "raw_hits": []
     },
     {
@@ -699,6 +693,12 @@
       "value": "Contact State Prediction",
       "raw_hits": []
     },
     {
       "name": "contact_prediction: known_task_family",
       "status": "pass",
@@ -774,21 +774,15 @@
       "observed": "object_relevance"
     },
     {
-      "name": "object_relevance: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Object-Centric Interaction Recognition",
-      "raw_hits": []
-    },
-    {
-      "name": "object_relevance: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "non-caption multimodal features",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Infer which objects are relevant to the current manipulation window from non-caption features.",
       "raw_hits": []
     },
     {
@@ -798,15 +792,15 @@
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "relevant object set",
       "raw_hits": []
     },
     {
-      "name": "object_relevance: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Predict which objects matter in the current window.",
       "raw_hits": []
     },
     {
@@ -815,6 +809,12 @@
       "value": "Object Relevance Prediction",
       "raw_hits": []
     },
     {
       "name": "object_relevance: known_task_family",
       "status": "pass",
@@ -892,21 +892,15 @@
       "observed": "caption_grounding"
     },
     {
-      "name": "caption_grounding: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Language-to-Moment Grounding",
-      "raw_hits": []
-    },
-    {
-      "name": "caption_grounding: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "text-like query and candidate windows",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Retrieve the matching time window for an annotation-derived text query.",
       "raw_hits": []
     },
     {
@@ -916,15 +910,15 @@
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "ranked matching moments",
       "raw_hits": []
     },
     {
-      "name": "caption_grounding: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Given a text-like query from annotation, find the matching time window.",
       "raw_hits": []
     },
     {
@@ -933,6 +927,12 @@
       "value": "Language Grounding",
       "raw_hits": []
     },
     {
       "name": "caption_grounding: known_task_family",
       "status": "pass",
@@ -1008,21 +1008,15 @@
       "observed": "cross_modal_retrieval"
     },
     {
-      "name": "cross_modal_retrieval: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Multimodal Representation Retrieval",
-      "raw_hits": []
-    },
-    {
-      "name": "cross_modal_retrieval: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "motion/IMU/pose query; depth/video candidates",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
       "raw_hits": []
     },
     {
@@ -1032,15 +1026,15 @@
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "ranked visual windows",
       "raw_hits": []
     },
     {
-      "name": "cross_modal_retrieval: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Use one group of modalities to retrieve the matching window from another group.",
       "raw_hits": []
     },
     {
@@ -1049,6 +1043,12 @@
       "value": "Cross-Modal Retrieval",
       "raw_hits": []
     },
     {
       "name": "cross_modal_retrieval: known_task_family",
       "status": "pass",
@@ -1126,21 +1126,15 @@
       "observed": "modality_reconstruction"
     },
     {
-      "name": "modality_reconstruction: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Modality Feature Reconstruction",
-      "raw_hits": []
-    },
-    {
-      "name": "modality_reconstruction: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "motion, IMU, and camera/pose features",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
       "raw_hits": []
     },
     {
@@ -1150,15 +1144,15 @@
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "reconstructed depth/video vector",
       "raw_hits": []
     },
     {
-      "name": "modality_reconstruction: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Predict one modality feature block from other modality blocks.",
       "raw_hits": []
     },
     {
@@ -1167,6 +1161,12 @@
       "value": "Cross-Modal Reconstruction",
       "raw_hits": []
     },
     {
       "name": "modality_reconstruction: known_task_family",
       "status": "pass",
@@ -1244,21 +1244,15 @@
       "observed": "temporal_order"
     },
     {
-      "name": "temporal_order: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Temporal Order Verification",
-      "raw_hits": []
-    },
-    {
-      "name": "temporal_order: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "two adjacent windows plus difference vector",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two neighboring windows are in chronological order or reversed.",
       "raw_hits": []
     },
     {
@@ -1268,15 +1262,15 @@
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "correct or reversed",
       "raw_hits": []
     },
     {
-      "name": "temporal_order: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Tell whether two nearby windows are in the correct time order.",
       "raw_hits": []
     },
     {
@@ -1285,6 +1279,12 @@
       "value": "Temporal Order Verification",
       "raw_hits": []
     },
     {
       "name": "temporal_order: known_task_family",
       "status": "pass",
@@ -1360,21 +1360,15 @@
       "observed": "misalignment_detection"
     },
     {
-      "name": "misalignment_detection: public_field_research_name_is_human_readable",
-      "status": "pass",
-      "value": "Cross-Modal Misalignment Detection",
-      "raw_hits": []
-    },
-    {
-      "name": "misalignment_detection: public_field_input_short_is_human_readable",
       "status": "pass",
-      "value": "motion-side and visual/depth-side feature groups",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_card_blurb_is_human_readable",
       "status": "pass",
-      "value": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
       "raw_hits": []
     },
     {
@@ -1384,15 +1378,15 @@
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_output_short_is_human_readable",
       "status": "pass",
-      "value": "aligned or shifted",
       "raw_hits": []
     },
     {
-      "name": "misalignment_detection: public_field_plain_goal_is_human_readable",
       "status": "pass",
-      "value": "Detect when modalities that should match are shifted out of sync.",
       "raw_hits": []
     },
     {
@@ -1401,6 +1395,12 @@
       "value": "Multimodal Synchronization Detection",
       "raw_hits": []
     },
     {
       "name": "misalignment_detection: known_task_family",
       "status": "pass",

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

docs/data/website_integrity.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-03T18:25:52+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -75,7 +75,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 66059,
-      "evidence_index": 81858
     },
     {
       "name": "project_status_links_json",
@@ -150,8 +150,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 66059,
-      "protocol_index": 78722,
-      "evidence_index": 81858
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -259,7 +259,7 @@
     },
     {
       "path": "data/evaluation_protocol.json",
-      "bytes": 13625,
       "top_level_type": "dict"
     },
     {
@@ -284,7 +284,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 108606,
       "top_level_type": "dict"
     },
     {
@@ -294,7 +294,7 @@
     },
     {
       "path": "data/project_brief.json",
-      "bytes": 2585,
       "top_level_type": "dict"
     },
     {
@@ -304,12 +304,12 @@
     },
     {
       "path": "data/project_packet.json",
-      "bytes": 7553,
       "top_level_type": "dict"
     },
     {
       "path": "data/project_status.json",
-      "bytes": 8868,
       "top_level_type": "dict"
     },
     {
@@ -374,12 +374,12 @@
     },
     {
       "path": "data/source_alignment_audit.json",
-      "bytes": 4432,
       "top_level_type": "dict"
     },
     {
       "path": "data/summary_metrics.json",
-      "bytes": 25179,
       "top_level_type": "dict"
     },
     {

 {
   "status": "pass",
+  "generated_at_utc": "2026-06-03T20:35:48+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 66059,
+      "evidence_index": 83262
     },
     {
       "name": "project_status_links_json",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 66059,
+      "protocol_index": 80126,
+      "evidence_index": 83262
     },
     {
       "name": "evaluation_protocol_links_json",
     },
     {
       "path": "data/evaluation_protocol.json",
+      "bytes": 13686,
       "top_level_type": "dict"
     },
     {
     },
     {
       "path": "data/mirror_parity.json",
+      "bytes": 108609,
       "top_level_type": "dict"
     },
     {
     },
     {
       "path": "data/project_brief.json",
+      "bytes": 3645,
       "top_level_type": "dict"
     },
     {
     },
     {
       "path": "data/project_packet.json",
+      "bytes": 7627,
       "top_level_type": "dict"
     },
     {
       "path": "data/project_status.json",
+      "bytes": 9233,
       "top_level_type": "dict"
     },
     {
     },
     {
       "path": "data/source_alignment_audit.json",
+      "bytes": 4724,
       "top_level_type": "dict"
     },
     {
       "path": "data/summary_metrics.json",
+      "bytes": 25260,
       "top_level_type": "dict"
     },
     {

docs/data/xperience10m_dataset_card_alignment.json CHANGED Viewed

@@ -171,7 +171,7 @@
     "validated_windows": 1161,
     "current_feature_dim": 8546,
     "raw_data_redistributed": false,
-    "audio_feature_status": "Audio is present in the sample MP4 streams and represented in the current baseline feature vector.",
     "implemented_task_count": 12,
     "neural_head_count": 12,
     "covered_by_current_tasks": [

     "validated_windows": 1161,
     "current_feature_dim": 8546,
     "raw_data_redistributed": false,
+    "audio_feature_status": "Audio is present in the sample MP4 streams and extracted into the current baseline feature vector.",
     "implemented_task_count": 12,
     "neural_head_count": 12,
     "covered_by_current_tasks": [

docs/index.html CHANGED Viewed

@@ -2172,7 +2172,7 @@
       <div class="wrap">
         <div class="section-head">
           <h2>Project overview and contributions.</h2>
-          <p>The page is organized like a compact research project: motivation and scope, dataset sample, task suite, method, baselines, research directions, interactive walkthroughs, and resources for continuing the work.</p>
         </div>
         <div class="brief-panel">
           <div class="brief-panel-head">
@@ -2200,6 +2200,24 @@
               <p>The next model-quality stage is a held-out episode pilot over the selected multi-episode relay, with no train/test episode leakage and a completed omni-model evaluation report.</p>
             </article>
           </div>
           <div class="brief-actions">
             <a href="research_roadmap.html">Open interactive roadmap</a>
             <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_BRIEF.md">Read the brief</a>

       <div class="wrap">
         <div class="section-head">
           <h2>Project overview and contributions.</h2>
+          <p>The page is organized like a compact research project: motivation and scope, dataset sample, task suite, method, baselines, research directions, interactive walkthroughs, and resources for continuing the work. The public sample is used as a real but bounded research system, not as a final full-dataset benchmark.</p>
         </div>
         <div class="brief-panel">
           <div class="brief-panel-head">
               <p>The next model-quality stage is a held-out episode pilot over the selected multi-episode relay, with no train/test episode leakage and a completed omni-model evaluation report.</p>
             </article>
           </div>
+          <div class="brief-grid" aria-label="Research capability map">
+            <article class="brief-card">
+              <strong>Data understanding</strong>
+              <p>Maps one public episode into synchronized windows across video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals.</p>
+            </article>
+            <article class="brief-card">
+              <strong>Task design</strong>
+              <p>Defines embodied-AI inputs, process modules, outputs, metrics, and case-study walkthroughs instead of treating the sample as a generic classification file.</p>
+            </article>
+            <article class="brief-card">
+              <strong>Evaluation discipline</strong>
+              <p>Keeps chronological splits, predictions, confusion matrices, leakage notes, and single-episode limitations explicit before claiming broader model quality.</p>
+            </article>
+            <article class="brief-card">
+              <strong>Scale-up readiness</strong>
+              <p>Connects the same data contract to 32/128-episode held-out pilots, Qwen3-Omni LoRA, Cosmos-style world modeling, and later policy-model branches.</p>
+            </article>
+          </div>
           <div class="brief-actions">
             <a href="research_roadmap.html">Open interactive roadmap</a>
             <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_BRIEF.md">Read the brief</a>

scripts/publish_hf_bundles.py CHANGED Viewed

@@ -16,7 +16,9 @@ silently drift behind the model card.
 from __future__ import annotations
 import argparse
 import getpass
 import os
 import shutil
 from pathlib import Path
@@ -47,6 +49,7 @@ LEGACY_SCORECARD_JSON = "rev" + "iewer_scorecard.json"
 STALE_ARTIFACT_REMOTE_FILES = [
     "results/omni_finetune/adapter_lora/tokenizer.json",
     "results/omni_finetune/hf_upload/tokenizer.json",
     LEGACY_SCORECARD_MD,
     "docs/data/" + LEGACY_PACKET_JSON,
     "docs/data/" + LEGACY_SCORECARD_JSON,
@@ -73,6 +76,140 @@ ARTIFACT_BINARY_ALLOWLIST = [
     "results/audio_ablation/raw_logmel_fisheye_cam0_sr16000_mels64_fft512_hop160.npz",
 ]
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description=__doc__)
@@ -203,6 +340,7 @@ def main() -> int:
     hf_root = args.hf_root.resolve()
     prune_generated_artifacts(hf_root)
     prune_artifact_bundle(hf_root)
     token = args.token or getpass.getpass("HF token: ").strip()
     if not token:

 from __future__ import annotations
 import argparse
+import csv
 import getpass
+import json
 import os
 import shutil
 from pathlib import Path
 STALE_ARTIFACT_REMOTE_FILES = [
     "results/omni_finetune/adapter_lora/tokenizer.json",
     "results/omni_finetune/hf_upload/tokenizer.json",
+    "viewer/dataset_viewer_summary.jsonl",
     LEGACY_SCORECARD_MD,
     "docs/data/" + LEGACY_PACKET_JSON,
     "docs/data/" + LEGACY_SCORECARD_JSON,
     "results/audio_ablation/raw_logmel_fisheye_cam0_sr16000_mels64_fft512_hop160.npz",
 ]
+ARTIFACT_VIEWER_CONFIG = """configs:
+  - config_name: episode_sample
+    data_files:
+      - split: public_sample
+        path: viewer/episode_windows.jsonl
+"""
+def load_json(path: Path) -> dict:
+    if not path.exists():
+        return {}
+    return json.loads(path.read_text(encoding="utf-8"))
+def find_status_readout(project_status: dict, area: str, fallback: str) -> str:
+    for row in project_status.get("rows", []):
+        if row.get("area") == area:
+            return row.get("readout", fallback)
+    return fallback
+def read_csv_by_window(path: Path) -> dict[int, dict]:
+    if not path.exists():
+        return {}
+    with path.open("r", encoding="utf-8", newline="") as handle:
+        return {int(row["window_index"]): row for row in csv.DictReader(handle)}
+def sample_fps(available_modalities: list[dict]) -> float:
+    for entry in available_modalities:
+        if "fps" in entry:
+            return float(entry["fps"])
+    return 20.00137419266181
+def modality_summary(modality_atlas: dict) -> str:
+    names = [entry.get("id", entry.get("name", "")) for entry in modality_atlas.get("modalities", [])]
+    names = [name for name in names if name]
+    if "calibration" not in names:
+        names.append("calibration")
+    return "|".join(names)
+def ensure_artifact_dataset_viewer_config(hf_root: Path) -> None:
+    """Expose the public sample episode as HF-viewable window rows."""
+    artifact_root = hf_root / "artifacts"
+    readme_path = artifact_root / "README.md"
+    viewer_dir = artifact_root / "viewer"
+    viewer_dir.mkdir(parents=True, exist_ok=True)
+    project_status = load_json(artifact_root / "docs/data/project_status.json")
+    modality_atlas = load_json(artifact_root / "docs/data/modality_atlas.json")
+    available_modalities = load_json(artifact_root / "results/episode_task_suite/available_modalities.json")
+    feature_manifest = load_json(artifact_root / "results/episode_task_suite/feature_manifest.json")
+    if not isinstance(available_modalities, list):
+        available_modalities = []
+    if not isinstance(feature_manifest, list):
+        feature_manifest = []
+    scope = project_status.get("scope_boundary", {})
+    fps = sample_fps(available_modalities)
+    modalities = modality_summary(modality_atlas)
+    feature_blocks = "|".join(block.get("name", "") for block in feature_manifest if block.get("name"))
+    objects_by_window = read_csv_by_window(
+        artifact_root / "results/single_episode_diagnostics/object_labels/window_object_labels.csv"
+    )
+    rows = []
+    windows_path = artifact_root / "results/episode_task_suite/windows.csv"
+    with windows_path.open("r", encoding="utf-8", newline="") as handle:
+        for row in csv.DictReader(handle):
+            window_index = int(row["window_index"])
+            start_frame = int(row["start_frame"])
+            end_frame = int(row["end_frame"])
+            center_frame = int(row["center_frame"])
+            object_row = objects_by_window.get(window_index, {})
+            rows.append(
+                {
+                    "episode_id": "xperience-10m-sample/public_episode",
+                    "source_sample_repo": "ropedia-ai/xperience-10m-sample",
+                    "window_index": window_index,
+                    "start_frame": start_frame,
+                    "end_frame": end_frame,
+                    "center_frame": center_frame,
+                    "start_time_s": round(start_frame / fps, 3),
+                    "end_time_s": round(end_frame / fps, 3),
+                    "center_time_s": round(center_frame / fps, 3),
+                    "window_frames": int(scope.get("window_frames", 20) or 20),
+                    "stride_frames": 5,
+                    "action_label": row["action_label"],
+                    "action_fraction": float(row["action_fraction"]),
+                    "subtask_label": row["subtask_label"],
+                    "subtask_fraction": float(row["subtask_fraction"]),
+                    "objects": object_row.get("objects", ""),
+                    "object_count": int(object_row.get("object_count", 0) or 0),
+                    "modalities": modalities,
+                    "feature_dim": int(scope.get("current_feature_dimensions", 8546) or 8546),
+                    "feature_blocks": feature_blocks,
+                    "derived_features_file": "results/episode_task_suite/shared_windows.npz",
+                    "source_window_table": "results/episode_task_suite/windows.csv",
+                    "raw_data_included": False,
+                }
+            )
+    viewer_path = viewer_dir / "episode_windows.jsonl"
+    viewer_path.write_text(
+        "\n".join(json.dumps(row, ensure_ascii=True) for row in rows) + "\n",
+        encoding="utf-8",
+    )
+    (viewer_dir / "dataset_viewer_summary.jsonl").unlink(missing_ok=True)
+    if not readme_path.exists():
+        return
+    readme = readme_path.read_text(encoding="utf-8")
+    readme = readme.replace("  - n<1K", "  - 1K<n<10K")
+    if readme.startswith("---"):
+        parts = readme.split("---", 2)
+        if len(parts) == 3:
+            metadata_lines = parts[1].strip().splitlines()
+            kept_lines = []
+            skip = False
+            for line in metadata_lines:
+                if line.startswith("configs:"):
+                    skip = True
+                    continue
+                if skip and not line.startswith((" ", "-")):
+                    skip = False
+                if not skip:
+                    kept_lines.append(line)
+            metadata = "\n".join(kept_lines).rstrip() + "\n" + ARTIFACT_VIEWER_CONFIG
+            readme_path.write_text("---\n" + metadata + "---" + parts[2], encoding="utf-8")
+            return
+    readme_path.write_text(ARTIFACT_VIEWER_CONFIG + "\n" + readme, encoding="utf-8")
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description=__doc__)
     hf_root = args.hf_root.resolve()
     prune_generated_artifacts(hf_root)
     prune_artifact_bundle(hf_root)
+    ensure_artifact_dataset_viewer_config(hf_root)
     token = args.token or getpass.getpass("HF token: ").strip()
     if not token: