{
  "$schema": "../schemas/world-model-label-audit.schema.json",
  "schema_version": "1.0.0",
  "audit_id": "egobowwow-world-model-label-audit-v1",
  "researched_at": "2026-08-25",
  "boundary": "Primary papers and official dataset/toolkit pages define evidence. The audit records published supervision, not inferred capabilities, downloaded rows, or acceptance evidence. A UniversalLabeler translation may change language surfaces but must preserve all grounded dimensions unless a target profile reports explicit loss.",
  "universal_dimensions": [
    {"dimension_id": "environment_context", "graph_paths": ["environment.site_context_id", "environment.physical_environment_id", "environment.scene_type_id", "environment.station_type_id"], "rubric": "Label social site context, indoor/outdoor exposure, scene, and work station independently; do not infer one axis from another."},
    {"dimension_id": "environment_conditions", "graph_paths": ["environment.lighting_id", "environment.weather_id", "environment.clutter_id", "environment.material_state_ids"], "rubric": "Record only observable physical conditions; use explicit unknowns rather than title-based inference."},
    {"dimension_id": "scene_geometry", "graph_paths": ["environment.map_artifact_id", "assertions[kind=mask_ref]", "assertions[kind=keypoints_3d]"], "rubric": "Bind maps, depth-derived geometry, or point clouds by content-addressed reference and named coordinate frame."},
    {"dimension_id": "coordinate_calibration", "graph_paths": ["coordinate_frames"], "rubric": "Every metric 2D/3D value names its clock and coordinate frame; frame transforms bind a calibration digest."},
    {"dimension_id": "time_sync", "graph_paths": ["modalities.clock_id", "assertions.interval", "assertions.timestamp_ns"], "rubric": "Use named synchronized clocks and integer nanoseconds with half-open intervals; frame indices are not time authority."},
    {"dimension_id": "language", "graph_paths": ["speech", "modalities[kind=language]"], "rubric": "Keep verbatim text, language tag, speech act, register, manner, purpose, and grammatical features separate from grounded event semantics."},
    {"dimension_id": "temporal_hierarchy", "graph_paths": ["event.temporal", "procedure.activity_category_id", "procedure.task_id", "procedure.step_id", "assertions[kind=temporal_span]"], "rubric": "Keep activity, task, step, atomic action, and sensor sample boundaries distinct and linked."},
    {"dimension_id": "agent_identity_role", "graph_paths": ["agents.agent_id", "agents.agent_kind", "agents.role_ids"], "rubric": "Give each human or robot a stable event-local identity and explicit performer, instructor, observer, or platform role."},
    {"dimension_id": "hand_laterality_role", "graph_paths": ["agents.hands.laterality", "agents.hands.role_ids"], "rubric": "Label left and right independently and distinguish acting, stabilizing, supporting, idle, and unknown roles."},
    {"dimension_id": "hand_pose", "graph_paths": ["agents.hands.pose_label_keys", "assertions[kind=keypoints_2d]", "assertions[kind=keypoints_3d]", "assertions[kind=pose_3d]"], "rubric": "Pose claims require a handedness identity, model/layout, visibility, coordinate frame, timestamp, confidence, and uncertainty."},
    {"dimension_id": "body_head_pose", "graph_paths": ["agents.body_pose_label_keys", "assertions[kind=keypoints_3d]", "assertions[kind=pose_3d]", "assertions[kind=trajectory_3d]"], "rubric": "Keep body/head geometry separate from action classes and preserve the source skeleton convention."},
    {"dimension_id": "gaze", "graph_paths": ["agents.gaze_label_keys", "relations[predicate_id=gaze_at]", "modalities[kind=eye_gaze]"], "rubric": "Represent gaze as a timed geometric signal in a named frame; gaze-at-object is a separate derived relation."},
    {"dimension_id": "object_identity_role", "graph_paths": ["entities.entity_id", "entities.category_id", "entities.role_ids"], "rubric": "Track object instances separately from categories and record patient, instrument, source, destination, support, and container roles."},
    {"dimension_id": "object_geometry_pose", "graph_paths": ["entities.geometry_label_keys", "entities.pose_label_keys", "entities.trajectory_label_keys", "assertions[kind=box_2d]", "assertions[kind=mask_ref]", "assertions[kind=pose_3d]", "assertions[kind=trajectory_3d]"], "rubric": "Do not flatten boxes, masks, meshes, 6DoF poses, or trajectories into an object noun."},
    {"dimension_id": "contact_spatial_relations", "graph_paths": ["relations", "agents.hands.contact_entity_ids", "assertions[kind=object_relation]"], "rubric": "Relations identify subject, predicate, object, temporal phase, confidence, and supporting assertion."},
    {"dimension_id": "object_state_transition", "graph_paths": ["entities.state_before_ids", "entities.state_after_ids", "assertions[kind=state_transition]"], "rubric": "State axes name the entity and observable before/after values; do not infer success from an action verb."},
    {"dimension_id": "goal_outcome", "graph_paths": ["procedure.goal_state_ids", "procedure.precondition_ids", "procedure.outcome"], "rubric": "Instructions, goals, preconditions, observed terminal state, and outcome are distinct fields."},
    {"dimension_id": "mistake_intervention", "graph_paths": ["procedure.correctness", "procedure.error_ids", "procedure.intervention_ids"], "rubric": "Annotate the observable mistake, recovery, and intervention independently from task identity."},
    {"dimension_id": "proficiency", "graph_paths": ["procedure.proficiency"], "rubric": "Proficiency is separately assessed metadata and must not be inferred from identity or one local mistake."},
    {"dimension_id": "multimodal_observation", "graph_paths": ["modalities"], "rubric": "Declare each available RGB, depth, audio, IMU, gaze, pose, segmentation, force, tactile, language, action, or proprioceptive stream with clock/frame bindings."},
    {"dimension_id": "embodied_action_proprioception", "graph_paths": ["assertions[kind=action_chunk]", "assertions[kind=proprio_series]", "modalities[kind=robot_action]", "modalities[kind=proprioception]"], "rubric": "Robot command vectors and observed state vectors keep their layouts, sample periods, coordinate frames, and timestamps."},
    {"dimension_id": "quality_uncertainty_provenance", "graph_paths": ["assertions.confidence", "assertions.uncertainty", "assertions.visibility", "provenance"], "rubric": "Every claim retains observability, confidence/uncertainty, producer, rubric, source profile, and review state; missing labels are not negative labels."}
  ],
  "typed_label_kinds": [
    "text_class",
    "temporal_span",
    "box_2d",
    "mask_ref",
    "keypoints_2d",
    "keypoints_3d",
    "pose_3d",
    "trajectory_3d",
    "audio_event",
    "object_relation",
    "state_transition",
    "action_chunk",
    "proprio_series"
  ],
  "sources": [
    {
      "source_id": "ego_exo4d_v2",
      "title": "Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives",
      "paper_url": "https://arxiv.org/abs/2311.18259",
      "official_url": "https://docs.ego-exo4d-data.org/",
      "release_year": 2024,
      "native_units": ["take", "synchronized ego/exo camera stream", "narration", "atomic action interval", "keystep interval", "3D hand/body pose sample", "proficiency assessment"],
      "annotation_rubric": "Preserve synchronized multiview capture and task-specific layers: narration and expert commentary, fine-grained action/keystep timing, proficiency, gaze, camera pose, and 3D hand/body pose are parallel supervision rather than one caption.",
      "supervision": ["multichannel audio", "eye gaze", "IMU", "3D point clouds", "camera poses", "language descriptions", "expert commentary", "fine-grained activity", "proficiency", "3D hand/body pose"],
      "supported_dimensions": ["time_sync", "language", "temporal_hierarchy", "agent_identity_role", "hand_laterality_role", "hand_pose", "body_head_pose", "gaze", "proficiency", "multimodal_observation", "quality_uncertainty_provenance"],
      "known_gaps": ["The activity label alone does not encode object state, contact, or exact environment conditions.", "Different benchmark annotation layers have different temporal and visibility coverage."]
    },
    {
      "source_id": "hot3d_v1",
      "title": "HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos",
      "paper_url": "https://arxiv.org/abs/2411.19167",
      "official_url": "https://github.com/facebookresearch/hot3d",
      "release_year": 2025,
      "native_units": ["sequence", "timestamp_ns", "camera stream", "left/right MANO or UmeTrack hand pose", "object 6DoF pose", "headset pose", "visibility/QA mask"],
      "annotation_rubric": "At each timestamp and stream, keep handedness, wrist transform, hand model parameters/joints, object instance pose, camera calibration, visibility, exposure, availability, and manual pose-QA masks distinct.",
      "supervision": ["multi-view RGB/monochrome", "eye gaze", "scene point clouds", "camera poses", "left/right 3D hand poses", "object 6DoF poses", "hand/object 3D models", "per-stream quality masks"],
      "supported_dimensions": ["scene_geometry", "coordinate_calibration", "time_sync", "hand_laterality_role", "hand_pose", "gaze", "object_identity_role", "object_geometry_pose", "multimodal_observation", "quality_uncertainty_provenance"],
      "known_gaps": ["Pose availability is not pose accuracy; HOT3D exports separate availability and QA masks.", "The geometric labels do not by themselves state semantic goal, success, error, or hand functional role."]
    },
    {
      "source_id": "aria_digital_twin_v1",
      "title": "Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception",
      "paper_url": "https://arxiv.org/abs/2306.06362",
      "official_url": "https://www.projectaria.com/datasets/adt/",
      "release_year": 2023,
      "native_units": ["sequence", "sensor calibration", "continuous device/object 6DoF pose", "3D gaze vector", "3D human pose", "2D segmentation", "depth map"],
      "annotation_rubric": "Treat the real scene, calibrated sensor rig, static/dynamic object instances, device trajectory, gaze, human pose, segmentation, depth, and synthetic twin as synchronized but separately typed ground truth.",
      "supervision": ["RGB/monochrome", "IMU", "complete calibration", "device and object 6DoF poses", "3D eye gaze", "3D human pose", "2D segmentations", "depth", "photorealistic twin"],
      "supported_dimensions": ["environment_context", "scene_geometry", "coordinate_calibration", "time_sync", "body_head_pose", "gaze", "object_identity_role", "object_geometry_pose", "multimodal_observation", "quality_uncertainty_provenance"],
      "known_gaps": ["The benchmark ground truth is primarily geometric and does not supply a universal manipulation-action or mistake ontology.", "The published release covers two indoor scenes and should not imply broad environment diversity."]
    },
    {
      "source_id": "holoassist_v1",
      "title": "HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World",
      "paper_url": "https://arxiv.org/abs/2309.17024",
      "official_url": "https://holoassist.github.io/",
      "release_year": 2023,
      "native_units": ["collaborative session", "synchronized sensor stream", "coarse/fine action interval", "conversation turn", "mistake", "intervention", "hand forecast target"],
      "annotation_rubric": "Keep performer and instructor roles, grounded verbal guidance, coarse/fine physical actions, mistakes, recovery/intervention behavior, gaze/head/hand signals, and future-hand targets as linked layers.",
      "supervision": ["seven synchronized streams", "action annotations", "conversation annotations", "mistakes", "intervention types", "hand forecasting"],
      "supported_dimensions": ["time_sync", "language", "temporal_hierarchy", "agent_identity_role", "hand_laterality_role", "hand_pose", "gaze", "goal_outcome", "mistake_intervention", "multimodal_observation", "quality_uncertainty_provenance"],
      "known_gaps": ["Conversation does not replace physical action or state evidence.", "A mistake label is task-relative and cannot be projected safely without the enclosing procedure context."]
    },
    {
      "source_id": "nymeria_v1",
      "title": "Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild",
      "paper_url": "https://arxiv.org/abs/2406.09905",
      "official_url": "https://www.projectaria.com/datasets/nymeria/",
      "release_year": 2024,
      "native_units": ["recording", "localized synchronized device stream", "full-body motion sample", "fine motion narration", "atomic action", "activity summary"],
      "annotation_rubric": "Preserve metric-world localization, synchronized multi-device streams, full-body motion, gaze/IMU, and the hierarchy from fine narration through atomic action to high-level activity summary.",
      "supervision": ["full-body ground-truth motion", "egocentric video", "eye tracking", "IMU", "third-person view", "metric-world localization", "hierarchical motion language"],
      "supported_dimensions": ["environment_context", "coordinate_calibration", "time_sync", "language", "temporal_hierarchy", "agent_identity_role", "body_head_pose", "gaze", "multimodal_observation", "quality_uncertainty_provenance"],
      "known_gaps": ["Motion-language hierarchy does not by itself provide object instance state, contact, or manipulation success.", "Natural-language summaries and metric motion have different annotation granularity."]
    },
    {
      "source_id": "droid_rlds_v1",
      "title": "DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset",
      "paper_url": "https://arxiv.org/abs/2403.12945",
      "official_url": "https://github.com/droid-dataset/droid",
      "release_year": 2024,
      "native_units": ["episode", "RLDS step", "three language instructions", "three camera observations", "robot Cartesian/joint/gripper state", "commanded action"],
      "annotation_rubric": "At every ordered step, keep language instruction variants, camera observations, measured Cartesian/joint/gripper state, commanded Cartesian/joint/gripper action, reward, discount, and terminal flags separate.",
      "supervision": ["wrist and exterior RGB", "language instruction variants", "Cartesian state/action", "joint state/action", "gripper state/action", "reward and terminal flags", "scene diversity"],
      "supported_dimensions": ["environment_context", "time_sync", "language", "agent_identity_role", "goal_outcome", "multimodal_observation", "embodied_action_proprioception", "quality_uncertainty_provenance"],
      "known_gaps": ["The released RLDS schema does not universally provide human hand pose, object instance pose, contact, or explicit before/after object state.", "Vector meaning depends on robot/action layout and coordinate conventions and must never be translated as untyped numbers."]
    },
    {
      "source_id": "open_x_embodiment_rlds_v1",
      "title": "Open X-Embodiment: Robotic Learning Datasets and RT-X Models",
      "paper_url": "https://arxiv.org/abs/2310.08864",
      "official_url": "https://robotics-transformer-x.github.io/",
      "release_year": 2024,
      "native_units": ["dataset", "episode", "RLDS step", "observation", "action", "language instruction", "robot-specific metadata"],
      "annotation_rubric": "Normalize container structure while preserving each contributor's robot morphology, observation/action fields, units, coordinate frames, success semantics, language, and missing modalities as dataset-specific contracts.",
      "supervision": ["standardized RLDS episodes", "multi-robot observations", "robot actions", "language instructions", "527 skills across heterogeneous platforms"],
      "supported_dimensions": ["environment_context", "time_sync", "language", "agent_identity_role", "goal_outcome", "multimodal_observation", "embodied_action_proprioception", "quality_uncertainty_provenance"],
      "known_gaps": ["A common container is not a common semantic action space; robot-specific layouts and units remain essential.", "Missing modalities and heterogeneous success labels require explicit loss/availability reporting."]
    }
  ]
}