| const D=[{"name": "Temporal Synchronization", "category": "Alignment", "question": "Are sensor streams, actions and events aligned on the correct timeline?", "metrics": ["timestamp error", "cross-modal lag", "event alignment accuracy"], "inputs": ["sensor timestamps", "action logs", "event markers"], "failure": "Small timing offsets can create false cause-and-effect relationships."}, {"name": "Spatial Calibration", "category": "Alignment", "question": "Do observations agree in a common coordinate frame?", "metrics": ["reprojection error", "pose error", "extrinsic drift"], "inputs": ["intrinsics", "extrinsics", "reference targets"], "failure": "Calibration drift corrupts geometry and state estimation."}, {"name": "Geometry Fidelity", "category": "Reconstruction", "question": "How accurately is real-world geometry preserved?", "metrics": ["surface distance", "depth error", "mesh completeness", "scale error"], "inputs": ["reference geometry", "reconstruction", "depth"], "failure": "Visual realism does not guarantee metric accuracy."}, {"name": "Object Persistence", "category": "Reconstruction", "question": "Are objects and agents tracked consistently over time?", "metrics": ["ID switches", "track fragmentation", "trajectory error"], "inputs": ["tracks", "ground-truth IDs", "trajectories"], "failure": "Identity errors break long-horizon interaction histories."}, {"name": "Semantic Accuracy", "category": "Semantics", "question": "Are objects, relations, tasks and affordances represented correctly?", "metrics": ["object accuracy", "relation F1", "affordance accuracy"], "inputs": ["labels", "scene graph", "task annotations"], "failure": "Correct geometry can still be unusable when semantics are wrong."}, {"name": "Action Alignment", "category": "Interaction", "question": "Are actions paired with the exact state transitions they produced?", "metrics": ["action-state lag", "transition pairing accuracy", "causal consistency"], "inputs": ["state_t", "action_t", "state_t+1"], "failure": "Misaligned actions damage control and action-conditioned world models."}, {"name": "State Completeness", "category": "Representation", "question": "Does the world state preserve variables required by downstream tasks?", "metrics": ["state coverage", "missing-variable rate", "task sufficiency"], "inputs": ["compiled state", "task requirements"], "failure": "Overcompression may remove variables needed for planning."}, {"name": "Representation Efficiency", "category": "Representation", "question": "How compact is the representation relative to preserved information?", "metrics": ["compression ratio", "bytes per step", "encoding latency"], "inputs": ["raw episode", "compiled representation"], "failure": "An accurate representation may still be too costly to scale."}, {"name": "Uncertainty Calibration", "category": "Reliability", "question": "Does confidence reflect actual compilation quality?", "metrics": ["calibration error", "coverage", "confidence-error correlation"], "inputs": ["confidence scores", "ground-truth error"], "failure": "Overconfident world states are dangerous for autonomous systems."}, {"name": "Real2Sim Fidelity", "category": "Simulation", "question": "Does simulation reproduce relevant real-world behavior?", "metrics": ["trajectory divergence", "contact consistency", "sensor similarity", "physics error"], "inputs": ["real trajectories", "sim trajectories", "contact events"], "failure": "Visual similarity alone does not guarantee useful physical behavior."}, {"name": "Downstream World-Model Utility", "category": "Downstream", "question": "Does compiled data improve world-model learning and rollout quality?", "metrics": ["prediction error", "rollout drift", "sample efficiency"], "inputs": ["compiled dataset", "world model", "evaluation tasks"], "failure": "Clean representations may still fail to improve predictive modeling."}, {"name": "Planning & Control Utility", "category": "Downstream", "question": "Does world compilation improve decisions, planning or control?", "metrics": ["task success", "planning regret", "control return", "policy transfer"], "inputs": ["planner", "controller", "compiled world", "task environment"], "failure": "The strongest test is whether better compilation produces better actions."}],P=[{"name": "Sensor Alignment Protocol", "focus": "Temporal + spatial alignment", "steps": ["Inject known timing offsets", "Measure recovery", "Compare calibrated poses", "Report residual error"], "report": ["mean lag", "p95 lag", "pose error"]}, {"name": "Reality-to-State Protocol", "focus": "World-state reconstruction", "steps": ["Capture reference scene", "Compile state", "Compare objects/relations", "Measure completeness"], "report": ["object accuracy", "relation F1", "state coverage"]}, {"name": "Action Causality Protocol", "focus": "State-action-next-state integrity", "steps": ["Replay actions", "Align transitions", "Perturb actions", "Compare consequences"], "report": ["pairing accuracy", "action lag", "causal consistency"]}, {"name": "Real2Sim Transfer Protocol", "focus": "Simulation fidelity", "steps": ["Replay real trajectory", "Replay in sim", "Compare motion/contact", "Test policy transfer"], "report": ["trajectory divergence", "contact consistency", "transfer success"]}, {"name": "World-Model Utility Protocol", "focus": "Downstream learning value", "steps": ["Train raw baseline", "Train compiled baseline", "Compare rollouts", "Measure sample efficiency"], "report": ["prediction error", "rollout drift", "data efficiency"]}, {"name": "Closed-Loop Utility Protocol", "focus": "Planning and control", "steps": ["Build compiled world", "Plan with model", "Execute task", "Compare baseline"], "report": ["task success", "planning regret", "control return"]}],$=id=>document.getElementById(id);let sel=D[0].name; |
