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1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 | {
"title": "Ropedia Xperience-10M Glossary",
"status": "published",
"purpose": "Define reader-facing project terms and adjacent technical field terms for embodied AI, egocentric multimodal data, spatial intelligence, world models, VLA/policy learning, evaluation, and public artifact reading.",
"categories": [
{
"id": "dataset_scope",
"label": "Dataset and scope",
"description": "Public data boundaries, evidence lines, and how each result family should be read."
},
{
"id": "files_features",
"label": "Files and features",
"description": "Raw sample files, windows, feature manifests, and public-safe derivatives."
},
{
"id": "multimodal_sensing",
"label": "Multimodal sensing",
"description": "Video, audio, depth, IMU, motion capture, calibration, and synchronization terms."
},
{
"id": "spatial_geometry",
"label": "Spatial geometry",
"description": "Camera pose, SLAM, coordinate frames, point clouds, 3D reconstruction, and spatial grounding."
},
{
"id": "temporal_world_models",
"label": "Temporal and world models",
"description": "Future prediction, rollouts, forward dynamics, long-horizon forecasting, and temporal leakage."
},
{
"id": "robotics_vla",
"label": "Robotics and VLA",
"description": "Vision-language-action, policies, action chunks, imitation learning, contact, and dexterity."
},
{
"id": "tasks_metrics",
"label": "Tasks and metrics",
"description": "Task contracts, scored records, direct scores, compact proxies, and audits."
},
{
"id": "training_eval",
"label": "Training and evaluation",
"description": "Splits, held-out evaluation, metric types, prompt/schema checks, adapters, and distributed training."
},
{
"id": "models_runs",
"label": "Models and runs",
"description": "Baseline families, Qwen3-Omni, Cosmos3, LoRA adapters, and full-parameter gates."
},
{
"id": "public_surfaces",
"label": "Public surfaces",
"description": "GitHub, website, Hugging Face repos, parity checks, and package validation."
}
],
"entries": [
{
"term": "Evidence line",
"category": "dataset_scope",
"plain_meaning": "A reading lane for a group of results.",
"project_usage": "Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.",
"do_not_confuse_with": "Qwen run versions v1-v6, which are model-run lineage.",
"primary_files": [
"TWO_EVIDENCE_LINES.md",
"docs/data/two_evidence_lines.json"
]
},
{
"term": "Official gated data",
"category": "dataset_scope",
"plain_meaning": "Upstream files that require official dataset access.",
"project_usage": "Raw Xperience-10M MP4/HDF5/RRD files and full source directories remain outside the public repo.",
"do_not_confuse_with": "Public-safe metrics, derived features, figures, and manifests.",
"primary_files": [
"DATA_NOTICE.md",
"REPRODUCIBILITY.md"
]
},
{
"term": "Public sample episode",
"category": "dataset_scope",
"plain_meaning": "One officially available sample episode.",
"project_usage": "The fully inspectable Line 1 unit used for raw-file browsing, 20-frame windows, task construction, and single-episode baselines.",
"do_not_confuse_with": "The selected-128 comparison rows.",
"primary_files": [
"docs/data/raw_sample_files.json",
"docs/single_episode_explorer.html"
]
},
{
"term": "Selected 128 episodes",
"category": "dataset_scope",
"plain_meaning": "A public-safe selected subset of official gated episode paths.",
"project_usage": "Line 2 uses derived windows/features and keeps links back to official episode ids and gated source paths.",
"do_not_confuse_with": "Redistributed raw MP4/HDF5/RRD data.",
"primary_files": [
"XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md",
"docs/data/xperience10m_128_episode_feature_index.json"
]
},
{
"term": "Xperience-10M",
"category": "dataset_scope",
"plain_meaning": "The upstream embodied human-interaction dataset.",
"project_usage": "Source dataset behind the public sample, selected-128 features, task suite, and model diagnostics.",
"do_not_confuse_with": "This repo, which only redistributes public-safe derived artifacts.",
"primary_files": [
"XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
"docs/data/xperience10m_dataset_card_alignment.json"
]
},
{
"term": "20-frame window",
"category": "files_features",
"plain_meaning": "A fixed short clip slice.",
"project_usage": "The sample episode is converted into aligned 20-frame units for features, labels, and many task heads.",
"do_not_confuse_with": "A full episode or arbitrary video segment.",
"primary_files": [
"results/episode_task_suite/windows.csv",
"EVALUATION_PROTOCOL.md"
]
},
{
"term": "annotation.hdf5",
"category": "files_features",
"plain_meaning": "Upstream annotation container for the sample.",
"project_usage": "Contains original labels/metadata; some public derived files expose processed features instead of every raw text field.",
"do_not_confuse_with": "Task result summaries.",
"primary_files": [
"docs/data/raw_sample_files.json"
]
},
{
"term": "Episode",
"category": "files_features",
"plain_meaning": "One recorded interaction sequence.",
"project_usage": "The basic source unit behind windows, labels, and train/val/test splits.",
"do_not_confuse_with": "A 20-frame window.",
"primary_files": [
"docs/data/raw_sample_files.json",
"docs/data/xperience10m_128_episode_feature_index.json"
]
},
{
"term": "Feature manifest",
"category": "files_features",
"plain_meaning": "A map from model-input columns to source modalities.",
"project_usage": "Explains feature groups and dimensions for the sample task suite.",
"do_not_confuse_with": "The raw annotation file.",
"primary_files": [
"results/episode_task_suite/feature_manifest.json"
]
},
{
"term": "Interaction text",
"category": "files_features",
"plain_meaning": "Natural-language interaction/caption content.",
"project_usage": "Used by task 15 and some derived text features; public matrices record direct or compact-proxy status.",
"do_not_confuse_with": "Numeric action ids or subtask ids.",
"primary_files": [
"TASK_SUITE_20.md",
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Modality",
"category": "files_features",
"plain_meaning": "A type of signal.",
"project_usage": "Video, audio, depth, pose/SLAM, motion capture, inertial, calibration, and language-derived signals.",
"do_not_confuse_with": "A task target.",
"primary_files": [
"docs/data/modality_atlas.json",
"results/episode_task_suite/feature_manifest.json"
]
},
{
"term": "Raw sample file map",
"category": "files_features",
"plain_meaning": "A human-readable inventory of the sample episode files.",
"project_usage": "Explains videos, annotations, calibration, motion, and derived previews.",
"do_not_confuse_with": "A training manifest.",
"primary_files": [
"docs/data/raw_sample_files.json"
]
},
{
"term": "visualization.rrd",
"category": "files_features",
"plain_meaning": "Rerun viewer recording for visual inspection.",
"project_usage": "Can be downloaded from the official sample dataset and opened in Rerun 0.29.0 to inspect the sample episode. It is not used for published training or metric rows.",
"do_not_confuse_with": "MP4 video streams or model inputs.",
"primary_files": [
"docs/data/raw_sample_files.json",
"REPRODUCIBILITY.md"
]
},
{
"term": "Window stride",
"category": "files_features",
"plain_meaning": "The frame step between neighboring windows.",
"project_usage": "Creates overlapping examples while preserving chronological order and leakage controls.",
"do_not_confuse_with": "Video frame rate.",
"primary_files": [
"EVALUATION_PROTOCOL.md"
]
},
{
"term": "Audio waveform",
"category": "multimodal_sensing",
"plain_meaning": "A time-series pressure signal from sound.",
"project_usage": "The audio ablation measures whether embedded audio helps selected task contracts.",
"do_not_confuse_with": "Language captions or text labels.",
"primary_files": [
"docs/data/audio_ablation_summary.json"
]
},
{
"term": "Calibration",
"category": "multimodal_sensing",
"plain_meaning": "Parameters that relate sensors to each other and to physical space.",
"project_usage": "Needed to interpret camera streams, depth, pose, and synchronized multimodal features together.",
"do_not_confuse_with": "A model training hyperparameter.",
"primary_files": [
"docs/data/raw_sample_files.json"
]
},
{
"term": "Camera extrinsics",
"category": "multimodal_sensing",
"plain_meaning": "A camera position and orientation relative to another coordinate frame.",
"project_usage": "Connects different camera streams and world coordinates.",
"do_not_confuse_with": "Camera intrinsics.",
"primary_files": [
"docs/data/raw_sample_files.json"
]
},
{
"term": "Camera intrinsics",
"category": "multimodal_sensing",
"plain_meaning": "Internal camera parameters such as focal length and distortion.",
"project_usage": "Explain how image pixels project to rays for geometry tasks.",
"do_not_confuse_with": "Camera extrinsics.",
"primary_files": [
"docs/data/raw_sample_files.json"
]
},
{
"term": "Depth map",
"category": "multimodal_sensing",
"plain_meaning": "A per-pixel estimate of distance from the camera.",
"project_usage": "Depth-derived signals support spatial and geometry-oriented tasks.",
"do_not_confuse_with": "RGB brightness or semantic segmentation.",
"primary_files": [
"docs/data/modality_atlas.json"
]
},
{
"term": "Egocentric video",
"category": "multimodal_sensing",
"plain_meaning": "Video captured from a first-person or body-mounted viewpoint.",
"project_usage": "The sample streams are egocentric views of human interaction and are the visual basis for many tasks.",
"do_not_confuse_with": "Third-person robot-camera footage.",
"primary_files": [
"docs/data/raw_sample_files.json"
]
},
{
"term": "Fisheye camera",
"category": "multimodal_sensing",
"plain_meaning": "A wide-angle camera with strong lens distortion.",
"project_usage": "Multiple fisheye MP4 streams give broad room coverage but need calibration-aware interpretation.",
"do_not_confuse_with": "A rectilinear pinhole camera image.",
"primary_files": [
"docs/data/raw_sample_files.json"
]
},
{
"term": "IMU",
"category": "multimodal_sensing",
"plain_meaning": "An inertial measurement unit with accelerometer and gyroscope signals.",
"project_usage": "Supports motion, temporal, and sensor-bridging tasks.",
"do_not_confuse_with": "Motion capture skeleton data.",
"primary_files": [
"docs/data/modality_atlas.json"
]
},
{
"term": "Metric depth",
"category": "multimodal_sensing",
"plain_meaning": "Depth expressed in physical units rather than arbitrary relative scale.",
"project_usage": "Useful for distance-sensitive spatial reasoning and reconstruction targets.",
"do_not_confuse_with": "Relative monocular depth.",
"primary_files": [
"docs/data/modality_atlas.json"
]
},
{
"term": "Motion capture",
"category": "multimodal_sensing",
"plain_meaning": "A system that records body or hand motion over time.",
"project_usage": "Provides hand/body motion evidence when exposed through public-safe derived features.",
"do_not_confuse_with": "Video-only pose estimation.",
"primary_files": [
"docs/data/modality_atlas.json"
]
},
{
"term": "RGB frame",
"category": "multimodal_sensing",
"plain_meaning": "A color image frame from a video stream.",
"project_usage": "Used for visual statistics, previews, and many model inputs.",
"do_not_confuse_with": "Depth values or point-cloud coordinates.",
"primary_files": [
"results/episode_task_suite/feature_manifest.json"
]
},
{
"term": "Sensor alignment",
"category": "multimodal_sensing",
"plain_meaning": "Putting different sensor streams into a shared temporal or spatial reference.",
"project_usage": "Used to make video, audio, pose, depth, IMU, and mocap usable in the same task input.",
"do_not_confuse_with": "Model ensembling.",
"primary_files": [
"docs/data/modality_atlas.json"
]
},
{
"term": "Stereo camera",
"category": "multimodal_sensing",
"plain_meaning": "A paired-camera setup that supports depth or geometry estimation.",
"project_usage": "The sample browser exposes stereo streams as part of the visual modality set.",
"do_not_confuse_with": "Single-view RGB video.",
"primary_files": [
"docs/data/raw_sample_files.json"
]
},
{
"term": "Timestamp synchronization",
"category": "multimodal_sensing",
"plain_meaning": "Aligning sensor samples by time.",
"project_usage": "The task suite assumes aligned windows across modalities so labels and features refer to the same moment.",
"do_not_confuse_with": "Randomly joining files with similar names.",
"primary_files": [
"EVALUATION_PROTOCOL.md"
]
},
{
"term": "3D reconstruction",
"category": "spatial_geometry",
"plain_meaning": "Recovering 3D scene structure from sensor data.",
"project_usage": "One core spatial-intelligence direction for Xperience-style data.",
"do_not_confuse_with": "Next-action classification.",
"primary_files": [
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Affordance",
"category": "spatial_geometry",
"plain_meaning": "An action possibility offered by an object or scene.",
"project_usage": "Relevant when moving from observed human interaction to robot-action or VLA tasks.",
"do_not_confuse_with": "A detected object category alone.",
"primary_files": [
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Camera pose",
"category": "spatial_geometry",
"plain_meaning": "The camera position and orientation at a time step.",
"project_usage": "Supports spatial-intelligence tasks, view synchronization, and geometry diagnostics.",
"do_not_confuse_with": "The human body pose.",
"primary_files": [
"docs/data/modality_atlas.json"
]
},
{
"term": "Coordinate frame",
"category": "spatial_geometry",
"plain_meaning": "A reference system for positions and orientations.",
"project_usage": "Needed when comparing camera, body, object, and world measurements.",
"do_not_confuse_with": "A video frame.",
"primary_files": [
"EVALUATION_PROTOCOL.md"
]
},
{
"term": "Object-centric representation",
"category": "spatial_geometry",
"plain_meaning": "A representation organized around objects and their relations.",
"project_usage": "Useful for object relevance, object-set forecast, and action-object relation tasks.",
"do_not_confuse_with": "A flat feature vector without object identity.",
"primary_files": [
"docs/data/task_suite_20.json"
]
},
{
"term": "Odometry",
"category": "spatial_geometry",
"plain_meaning": "Motion estimated from sensor changes over time.",
"project_usage": "A relevant spatial term for ego-motion and camera-pose reasoning.",
"do_not_confuse_with": "Ground-truth motion capture.",
"primary_files": [
"docs/data/modality_atlas.json"
]
},
{
"term": "Point cloud",
"category": "spatial_geometry",
"plain_meaning": "A set of 3D points representing scene structure.",
"project_usage": "A likely target or intermediate representation for spatial-intelligence extensions.",
"do_not_confuse_with": "A 2D image grid.",
"primary_files": [
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "SLAM",
"category": "spatial_geometry",
"plain_meaning": "Simultaneous localization and mapping.",
"project_usage": "A field term for estimating camera motion and scene structure from sensor observations.",
"do_not_confuse_with": "A task label or action class.",
"primary_files": [
"docs/data/modality_atlas.json"
]
},
{
"term": "Spatial grounding",
"category": "spatial_geometry",
"plain_meaning": "Linking language or labels to locations, objects, or geometry.",
"project_usage": "Connects language grounding tasks with 3D/spatial reasoning.",
"do_not_confuse_with": "General text classification.",
"primary_files": [
"docs/data/research_directions.json"
]
},
{
"term": "Trajectory",
"category": "spatial_geometry",
"plain_meaning": "A sequence of positions over time.",
"project_usage": "Used for hand motion, camera motion, and future-path tasks.",
"do_not_confuse_with": "A single coordinate or label.",
"primary_files": [
"TASK_SUITE_20.md"
]
},
{
"term": "Action forecasting",
"category": "temporal_world_models",
"plain_meaning": "Predicting a future action before it happens.",
"project_usage": "Covered by next-action and long-horizon task contracts.",
"do_not_confuse_with": "Recognizing the current action only.",
"primary_files": [
"docs/data/task_suite_20.json"
]
},
{
"term": "Autoregressive prediction",
"category": "temporal_world_models",
"plain_meaning": "Generating each future token, state, or frame conditioned on prior outputs.",
"project_usage": "Relevant for model branches that produce structured JSON or temporal predictions.",
"do_not_confuse_with": "A one-shot classifier.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Forward dynamics",
"category": "temporal_world_models",
"plain_meaning": "Predicting the next state from the current state and action/context.",
"project_usage": "The Cosmos3-Super LoRA branch uses a forward-dynamics-style diagnostic contract.",
"do_not_confuse_with": "Reverse inference from result back to cause.",
"primary_files": [
"docs/data/omni_model_comparison.json"
]
},
{
"term": "Latent state",
"category": "temporal_world_models",
"plain_meaning": "A hidden representation that summarizes observed context.",
"project_usage": "Useful for future foundation-model and world-model training plans.",
"do_not_confuse_with": "A visible annotation column.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Long-horizon prediction",
"category": "temporal_world_models",
"plain_meaning": "Predicting outcomes several seconds or steps ahead.",
"project_usage": "Tasks 13 and 14 test longer temporal context beyond immediate recognition.",
"do_not_confuse_with": "Single-frame classification.",
"primary_files": [
"docs/data/task_suite_20.json"
]
},
{
"term": "Next-frame prediction",
"category": "temporal_world_models",
"plain_meaning": "Predicting future visual frames from past frames.",
"project_usage": "A field-level world-model objective related to the human-video world-model direction.",
"do_not_confuse_with": "Next-action prediction.",
"primary_files": [
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Object persistence",
"category": "temporal_world_models",
"plain_meaning": "Tracking that an object remains present over time even when view or interaction changes.",
"project_usage": "Relevant for object-set forecast and long-video reasoning.",
"do_not_confuse_with": "A single-frame object detection.",
"primary_files": [
"docs/data/task_suite_20.json"
]
},
{
"term": "Rollout",
"category": "temporal_world_models",
"plain_meaning": "Repeatedly predicting future steps from a model state.",
"project_usage": "Important for judging world models beyond one-step prediction.",
"do_not_confuse_with": "A held-out static test row.",
"primary_files": [
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Subtask forecasting",
"category": "temporal_world_models",
"plain_meaning": "Predicting the next higher-level step in an activity.",
"project_usage": "Used in the future-task probe line for Qwen3-Omni.",
"do_not_confuse_with": "Frame-level action classification.",
"primary_files": [
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Teacher forcing",
"category": "temporal_world_models",
"plain_meaning": "Training a sequence model using ground-truth previous outputs.",
"project_usage": "A likely training option for future sequence/world-model baselines.",
"do_not_confuse_with": "Free-running rollout evaluation.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Temporal leakage",
"category": "temporal_world_models",
"plain_meaning": "Using future information that would not be available at prediction time.",
"project_usage": "Avoided by chronological splits and target-side feature controls.",
"do_not_confuse_with": "A low model score.",
"primary_files": [
"EVALUATION_PROTOCOL.md"
]
},
{
"term": "Transition timing",
"category": "temporal_world_models",
"plain_meaning": "Estimating when the next state or action transition happens.",
"project_usage": "Task 20 turns temporal change into a regression target.",
"do_not_confuse_with": "Classifying the transition type only.",
"primary_files": [
"docs/data/task_suite_20.json"
]
},
{
"term": "Action chunk",
"category": "robotics_vla",
"plain_meaning": "A short sequence of low-level actions predicted together.",
"project_usage": "The VLA figure and plan use action chunks as the policy-output concept.",
"do_not_confuse_with": "A natural-language action label.",
"primary_files": [
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Behavior cloning",
"category": "robotics_vla",
"plain_meaning": "A supervised imitation-learning method for predicting demonstrated actions.",
"project_usage": "A plausible baseline once action targets are converted.",
"do_not_confuse_with": "Generative video modeling.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Contact event",
"category": "robotics_vla",
"plain_meaning": "A moment when a hand, body, or tool touches an object or surface.",
"project_usage": "Used in contact-related tasks and action-quality interpretation.",
"do_not_confuse_with": "Visual co-occurrence without touch.",
"primary_files": [
"docs/data/task_suite_20.json"
]
},
{
"term": "Dexterity",
"category": "robotics_vla",
"plain_meaning": "Fine-grained physical manipulation ability.",
"project_usage": "Relevant to hand-object interaction, contact, and VLA/policy directions.",
"do_not_confuse_with": "High text-generation accuracy.",
"primary_files": [
"docs/data/research_directions.json"
]
},
{
"term": "End effector",
"category": "robotics_vla",
"plain_meaning": "The robot part that acts on the world, such as a gripper or hand.",
"project_usage": "A key target frame for future manipulation-policy conversion.",
"do_not_confuse_with": "A camera or global scene coordinate.",
"primary_files": [
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Hand-object interaction",
"category": "robotics_vla",
"plain_meaning": "A physical interaction between hands and objects.",
"project_usage": "A central signal family behind action, contact, object relevance, and interaction-text tasks.",
"do_not_confuse_with": "Object detection without action.",
"primary_files": [
"docs/data/task_suite_20.json"
]
},
{
"term": "Imitation learning",
"category": "robotics_vla",
"plain_meaning": "Training a policy to imitate demonstrated behavior.",
"project_usage": "Relevant when converting human video/motion into action supervision.",
"do_not_confuse_with": "Reinforcement learning from online robot trials.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Language grounding",
"category": "robotics_vla",
"plain_meaning": "Connecting text to observed objects, actions, or spatial context.",
"project_usage": "Task 8 and VLA directions use language as grounded supervision rather than standalone text.",
"do_not_confuse_with": "Caption fluency alone.",
"primary_files": [
"docs/data/task_suite_20.json"
]
},
{
"term": "Policy",
"category": "robotics_vla",
"plain_meaning": "A mapping from observations to actions.",
"project_usage": "A future target for robot-compatible Xperience-derived action data.",
"do_not_confuse_with": "A benchmark metric.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Robot-compatible action target",
"category": "robotics_vla",
"plain_meaning": "An action representation a robot policy can execute or imitate.",
"project_usage": "Needed before OpenVLA/openpi/GR00T-style policy training is meaningful here.",
"do_not_confuse_with": "Human-only caption text.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Vision-language-action model",
"category": "robotics_vla",
"plain_meaning": "A model that maps visual context and language into actions.",
"project_usage": "The VLA direction is a future path after action targets are converted into robot-compatible chunks.",
"do_not_confuse_with": "A vision-language model that only answers text.",
"primary_files": [
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Compact-proxy score",
"category": "tasks_metrics",
"plain_meaning": "A bounded proxy metric when a direct raw target is not publicly available.",
"project_usage": "Kept explicit in the matrix and gap audit so readers do not over-read it.",
"do_not_confuse_with": "A direct target measurement.",
"primary_files": [
"TASK_METHOD_20_GAP_AUDIT.md",
"docs/data/task_method_20_gap_audit.json"
]
},
{
"term": "Direct score",
"category": "tasks_metrics",
"plain_meaning": "A metric computed against the task target directly.",
"project_usage": "The preferred score type in the 20-task matrix.",
"do_not_confuse_with": "Compact-proxy score.",
"primary_files": [
"TASK_METHOD_20_GAP_AUDIT.md",
"docs/data/task_method_20_gap_audit.json"
]
},
{
"term": "Gap audit",
"category": "tasks_metrics",
"plain_meaning": "A coverage and source-status audit.",
"project_usage": "Explains scored, proxy, and unsupported cells.",
"do_not_confuse_with": "A performance leaderboard.",
"primary_files": [
"TASK_METHOD_20_GAP_AUDIT.md",
"docs/data/task_method_20_gap_audit.json"
]
},
{
"term": "Leakage control",
"category": "tasks_metrics",
"plain_meaning": "A split or feature rule that prevents using target information unfairly.",
"project_usage": "Chronological splits, held-out splits, and source audits protect task interpretation.",
"do_not_confuse_with": "Lower training accuracy.",
"primary_files": [
"EVALUATION_PROTOCOL.md",
"docs/data/evaluation_protocol.json"
]
},
{
"term": "Normalized radar value",
"category": "tasks_metrics",
"plain_meaning": "A 0-1 plotting value used only to draw comparable radar polygons.",
"project_usage": "Helps visualize metrics with different scales and directions.",
"do_not_confuse_with": "The raw metric value to cite.",
"primary_files": [
"docs/data/unified_task_model_radar.json",
"docs/assets/charts/unified_task_model_radar.svg"
]
},
{
"term": "Raw metric value",
"category": "tasks_metrics",
"plain_meaning": "The original metric value emitted by the runner or verified result package.",
"project_usage": "This is the value to cite from the 180-result table.",
"do_not_confuse_with": "The normalized radar value.",
"primary_files": [
"TASK_METHOD_20_RESULT_MATRIX.md",
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Task contract",
"category": "tasks_metrics",
"plain_meaning": "The definition of one benchmark task.",
"project_usage": "Includes input, target/output, metric, split, source artifact, and limitation.",
"do_not_confuse_with": "A model architecture.",
"primary_files": [
"TASK_SUITE_20.md",
"docs/data/task_suite_20.json"
]
},
{
"term": "Task-method record",
"category": "tasks_metrics",
"plain_meaning": "One method evaluated on one task.",
"project_usage": "9 methods x 20 tasks gives 180 public result records.",
"do_not_confuse_with": "A single prediction row.",
"primary_files": [
"TASK_METHOD_20_RESULT_MATRIX.md",
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Unified 20-task suite",
"category": "tasks_metrics",
"plain_meaning": "The current task surface.",
"project_usage": "All 20 task contracts are presented together and scored across methods where real artifacts exist.",
"do_not_confuse_with": "Historical tier2_task_suite filenames, which are provenance paths rather than a second suite.",
"primary_files": [
"TASK_SUITE_20.md",
"docs/data/task_suite_20.json"
]
},
{
"term": "Adapter checkpoint",
"category": "training_eval",
"plain_meaning": "Saved adapter weights from a fine-tuning run.",
"project_usage": "The public model branches publish adapters when validated and public-safe.",
"do_not_confuse_with": "Full base-model checkpoint.",
"primary_files": [
"docs/data/omni_model_comparison.json"
]
},
{
"term": "Balanced accuracy",
"category": "training_eval",
"plain_meaning": "Accuracy averaged across classes to reduce majority-class dominance.",
"project_usage": "Useful for imbalanced task labels.",
"do_not_confuse_with": "Overall accuracy.",
"primary_files": [
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Chronological split",
"category": "training_eval",
"plain_meaning": "A split ordered by time.",
"project_usage": "Used for the single-episode baselines to reduce future-window leakage.",
"do_not_confuse_with": "A random row split.",
"primary_files": [
"EVALUATION_PROTOCOL.md"
]
},
{
"term": "Confusion matrix",
"category": "training_eval",
"plain_meaning": "A table of predicted classes versus true classes.",
"project_usage": "Helps inspect which task labels a method confuses.",
"do_not_confuse_with": "A scalar leaderboard score.",
"primary_files": [
"results/episode_task_suite/neural_mlp"
]
},
{
"term": "FSDP",
"category": "training_eval",
"plain_meaning": "Fully Sharded Data Parallel, a distributed training strategy.",
"project_usage": "Appears in full-parameter feasibility and multi-GPU training notes.",
"do_not_confuse_with": "A model architecture.",
"primary_files": [
"docs/data/qwen3_full_parameter_gates.json"
]
},
{
"term": "Held-out evaluation",
"category": "training_eval",
"plain_meaning": "Testing on examples not used for training.",
"project_usage": "Required before promoting Qwen/Cosmos results to public evidence.",
"do_not_confuse_with": "Training-set loss.",
"primary_files": [
"docs/data/omni_model_comparison.json"
]
},
{
"term": "JSON validity",
"category": "training_eval",
"plain_meaning": "Whether model output parses as the required JSON schema.",
"project_usage": "A key diagnostic for Qwen3-Omni structured-output runs.",
"do_not_confuse_with": "Task correctness after parsing.",
"primary_files": [
"docs/data/omni_model_comparison.json"
]
},
{
"term": "Macro F1",
"category": "training_eval",
"plain_meaning": "The average F1 score across classes, usually treating classes equally.",
"project_usage": "Used when class imbalance matters in classification tasks.",
"do_not_confuse_with": "Accuracy dominated by frequent classes.",
"primary_files": [
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Mean absolute error",
"category": "training_eval",
"plain_meaning": "The average absolute difference between predicted and true numeric values.",
"project_usage": "Used for regression-style task rows such as timing or trajectory targets.",
"do_not_confuse_with": "A classification F1 score.",
"primary_files": [
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Overfit check",
"category": "training_eval",
"plain_meaning": "A small training test that verifies a model can learn a tiny subset.",
"project_usage": "Useful for catching data/model wiring bugs before full training.",
"do_not_confuse_with": "Evidence of generalization.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Parameter-efficient fine-tuning",
"category": "training_eval",
"plain_meaning": "Updating a small number of added or selected parameters.",
"project_usage": "LoRA is the current parameter-efficient path for Qwen/Cosmos branches.",
"do_not_confuse_with": "Full-parameter fine-tuning.",
"primary_files": [
"docs/data/foundation_model_plan.json"
]
},
{
"term": "Schema compliance",
"category": "training_eval",
"plain_meaning": "Whether an output follows the expected field names and value types.",
"project_usage": "Needed for structured task probes and public package validation.",
"do_not_confuse_with": "High semantic accuracy.",
"primary_files": [
"docs/data/omni_model_comparison.json"
]
},
{
"term": "Smoke run",
"category": "training_eval",
"plain_meaning": "A short run that checks whether a pipeline can start and execute key steps.",
"project_usage": "Used for feasibility gates before expensive full runs.",
"do_not_confuse_with": "A complete benchmark result.",
"primary_files": [
"docs/data/qwen3_full_parameter_gates.json"
]
},
{
"term": "Top-k accuracy",
"category": "training_eval",
"plain_meaning": "A score that counts a prediction correct if the target is among the k highest-ranked outputs.",
"project_usage": "Useful for large-label or retrieval-style tasks.",
"do_not_confuse_with": "Top-1 exact accuracy.",
"primary_files": [
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Train/validation/test split",
"category": "training_eval",
"plain_meaning": "A partition that separates model fitting, tuning, and final evaluation examples.",
"project_usage": "The selected-128 setup uses a held-out split discipline for model branches.",
"do_not_confuse_with": "A random shuffle without temporal or episode boundaries.",
"primary_files": [
"EVALUATION_PROTOCOL.md"
]
},
{
"term": "Cosmos3-Nano",
"category": "models_runs",
"plain_meaning": "A smaller Cosmos3 compatibility/future-window branch.",
"project_usage": "Used for the Nano Future Window row and related diagnostics.",
"do_not_confuse_with": "Cosmos3-Super fine-tuned adapter.",
"primary_files": [
"docs/data/omni_model_comparison.json"
]
},
{
"term": "Cosmos3-Super",
"category": "models_runs",
"plain_meaning": "The larger Cosmos3-style branch tracked in this project.",
"project_usage": "Published as Reasoner diagnostics and a separate forward-dynamics LoRA adapter/result branch when verified.",
"do_not_confuse_with": "Cosmos3-Nano.",
"primary_files": [
"docs/data/omni_model_comparison.json"
]
},
{
"term": "Foundation pipeline",
"category": "models_runs",
"plain_meaning": "A high-level training direction.",
"project_usage": "Spatial intelligence, human-video world modeling, and vision-language-action are documented as trainable directions with task mappings.",
"do_not_confuse_with": "A completed public result row.",
"primary_files": [
"THREE_FOUNDATION_PIPELINES.md",
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Full-parameter fine-tuning",
"category": "models_runs",
"plain_meaning": "Updating the whole model rather than only adapters.",
"project_usage": "This project records feasibility gates and short pilots, but does not publish full checkpoints.",
"do_not_confuse_with": "LoRA adapter publication.",
"primary_files": [
"docs/data/qwen3_full_parameter_gates.json"
]
},
{
"term": "Human-video world model",
"category": "models_runs",
"plain_meaning": "Learning future frames, actions, and interaction dynamics from human video.",
"project_usage": "Uses temporal prediction, next-action, transition, and object-forecast tasks.",
"do_not_confuse_with": "Robot policy execution.",
"primary_files": [
"THREE_FOUNDATION_PIPELINES.md",
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "LoRA adapter",
"category": "models_runs",
"plain_meaning": "A lightweight set of trainable adapter weights.",
"project_usage": "Published only when the package is verified and public-safe.",
"do_not_confuse_with": "Full base-model weights.",
"primary_files": [
"OMNI_MODEL_EXTENSION_CONTRACT.md",
"docs/data/omni_model_comparison.json"
]
},
{
"term": "Metadata baseline",
"category": "models_runs",
"plain_meaning": "A selected-128 baseline using metadata or text-derived public-safe features.",
"project_usage": "Compares simple and neural heads on the held-out split.",
"do_not_confuse_with": "Raw video, depth, or audio feature baselines.",
"primary_files": [
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Minimal baseline",
"category": "models_runs",
"plain_meaning": "A simple non-neural task head; the \"minimum\" reference row in casual wording.",
"project_usage": "Provides a reproducible lower-complexity comparison for task feasibility.",
"do_not_confuse_with": "Metadata-only selected-128 baseline family.",
"primary_files": [
"RESEARCH_TAKEAWAYS.md",
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Neural MLP",
"category": "models_runs",
"plain_meaning": "A compact neural task head.",
"project_usage": "Used for single-episode and selected-128 baseline comparisons.",
"do_not_confuse_with": "Foundation-model fine-tuning.",
"primary_files": [
"results/episode_task_suite/neural_mlp/",
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Qwen v1-v6",
"category": "models_runs",
"plain_meaning": "The Qwen3-Omni run lineage.",
"project_usage": "v1-v4 are earlier pipeline/ablation evidence, v5 is the prior pinned release, and v6 is the current public 20-task row.",
"do_not_confuse_with": "Six different evidence lines.",
"primary_files": [
"QWEN3_OMNI_RUN_LINEAGE.md",
"docs/data/qwen3_omni_run_lineage.json"
]
},
{
"term": "Qwen3-Omni",
"category": "models_runs",
"plain_meaning": "The multimodal foundation-model family used for the Qwen branch.",
"project_usage": "The current public 20-task Qwen row is Qwen3-Omni v6 LoRA plus task-specific probes.",
"do_not_confuse_with": "Cosmos3 or single-episode task-head baselines.",
"primary_files": [
"QWEN3_OMNI_RUN_LINEAGE.md",
"docs/data/qwen3_omni_run_lineage.json"
]
},
{
"term": "Raw-feature baseline",
"category": "models_runs",
"plain_meaning": "A selected-128 baseline using exported public-safe raw-feature groups.",
"project_usage": "Tracks what non-foundation heads can do with richer processed inputs.",
"do_not_confuse_with": "Raw gated media redistribution.",
"primary_files": [
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Simple baseline",
"category": "models_runs",
"plain_meaning": "A non-neural baseline family for the selected-128 rows.",
"project_usage": "Used for metadata/text and raw-feature 128-episode comparisons before NN/foundation-model rows.",
"do_not_confuse_with": "The single-episode Minimal baseline.",
"primary_files": [
"RESEARCH_TAKEAWAYS.md",
"docs/data/task_method_20_result_matrix.json"
]
},
{
"term": "Spatial intelligence",
"category": "models_runs",
"plain_meaning": "Learning geometry and spatial reasoning from egocentric data.",
"project_usage": "Uses video, depth, camera pose, and language tasks to target 3D/space reasoning.",
"do_not_confuse_with": "World-model future prediction.",
"primary_files": [
"THREE_FOUNDATION_PIPELINES.md",
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "Vision-language-action",
"category": "models_runs",
"plain_meaning": "Mapping perception and language to action chunks.",
"project_usage": "A future policy/VLA direction that needs action-target conversion and stronger policy packaging.",
"do_not_confuse_with": "Qwen3-Omni diagnostic scoring.",
"primary_files": [
"THREE_FOUNDATION_PIPELINES.md",
"docs/data/three_foundation_pipelines.json"
]
},
{
"term": "HF artifact dataset",
"category": "public_surfaces",
"plain_meaning": "Hugging Face dataset repo for derived evidence.",
"project_usage": "Stores public-safe reports, metrics, website JSON, and sanitized result packages.",
"do_not_confuse_with": "Original Xperience-10M dataset.",
"primary_files": [
"ARTIFACT_GUIDE.md",
"docs/data/artifact_index.json"
]
},
{
"term": "HF baseline model repo",
"category": "public_surfaces",
"plain_meaning": "Hugging Face model repo for lightweight baseline artifacts.",
"project_usage": "Mirrors baseline weights, figures, metrics, and task artifacts.",
"do_not_confuse_with": "Qwen/Cosmos adapter-specific repos.",
"primary_files": [
"PUBLIC_READER_MAP.md",
"docs/data/public_reader_map.json"
]
},
{
"term": "HF Space",
"category": "public_surfaces",
"plain_meaning": "Hugging Face-hosted app/site surface.",
"project_usage": "Mirrors the dashboard and static website assets.",
"do_not_confuse_with": "HF artifact dataset or model repo.",
"primary_files": [
"PUBLIC_READER_MAP.md",
"docs/data/public_reader_map.json"
]
},
{
"term": "HF weights/results repo",
"category": "public_surfaces",
"plain_meaning": "A consolidated public-safe model-result bundle.",
"project_usage": "Groups baseline weights, verified model artifacts, analysis files, and manifests.",
"do_not_confuse_with": "The upstream raw dataset.",
"primary_files": [
"PUBLIC_READER_MAP.md"
]
},
{
"term": "Mirror parity",
"category": "public_surfaces",
"plain_meaning": "A check that public copies match the source files.",
"project_usage": "Records whether GitHub, website, and HF mirrors agree.",
"do_not_confuse_with": "A model-quality metric.",
"primary_files": [
"docs/data/mirror_parity.json"
]
},
{
"term": "Public-safe artifact",
"category": "public_surfaces",
"plain_meaning": "A file that can be mirrored publicly without raw gated content.",
"project_usage": "Metrics, JSON summaries, model cards, figures, derived manifests, and approved lightweight weights/adapters.",
"do_not_confuse_with": "Raw dataset redistribution.",
"primary_files": [
"ARTIFACT_GUIDE.md",
"docs/data/artifact_index.json"
]
},
{
"term": "Publication audit",
"category": "public_surfaces",
"plain_meaning": "A public-package validation report.",
"project_usage": "Confirms required files exist and forbidden raw/private assets are not included.",
"do_not_confuse_with": "Scientific peer review.",
"primary_files": [
"docs/data/publication_audit.json"
]
},
{
"term": "Verified package",
"category": "public_surfaces",
"plain_meaning": "A result or artifact bundle that passed local/public validators.",
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