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{
"family": "Latent Dynamics",
"capability": "State prediction",
"metrics": [
"Latent prediction error",
"Reconstruction quality",
"Rollout stability"
],
"best_for": "Compact predictive models and model-based RL",
"questions": "Does the latent state preserve information needed for future prediction?"
},
{
"family": "Visual Prediction",
"capability": "Future observation prediction",
"metrics": [
"Perceptual similarity",
"Temporal consistency",
"Object persistence"
],
"best_for": "Video and image-based world models",
"questions": "Are generated future observations coherent and useful?"
},
{
"family": "Interactive Environments",
"capability": "Action-conditioned simulation",
"metrics": [
"Action fidelity",
"Controllability",
"State consistency"
],
"best_for": "Interactive generative environments",
"questions": "Do control inputs produce stable and meaningful changes?"
},
{
"family": "Model-Based Reinforcement Learning",
"capability": "Planning utility",
"metrics": [
"Return",
"Sample efficiency",
"Planning success",
"Model error"
],
"best_for": "Agents learning through imagined trajectories",
"questions": "Does the learned world model improve decisions and reduce environment interaction?"
},
{
"family": "Robotics & Manipulation",
"capability": "Physical interaction",
"metrics": [
"Task success",
"Trajectory error",
"Action success",
"Safety violations"
],
"best_for": "Robot control and manipulation",
"questions": "Can the model predict and support physically grounded actions?"
},
{
"family": "Embodied Navigation",
"capability": "Spatial planning",
"metrics": [
"Navigation success",
"Path efficiency",
"Localization error",
"Goal completion"
],
"best_for": "Embodied agents and mobile robots",
"questions": "Does the model preserve useful spatial structure over time?"
},
{
"family": "Autonomous Systems",
"capability": "Trajectory forecasting",
"metrics": [
"Trajectory error",
"Collision rate",
"Calibration",
"Scenario coverage"
],
"best_for": "Driving, drones and autonomous machines",
"questions": "Can the system model multiple actors and plausible future trajectories?"
},
{
"family": "Physical Reasoning",
"capability": "Physics consistency",
"metrics": [
"Constraint violations",
"Interaction accuracy",
"State transition validity"
],
"best_for": "Physical AI and simulation",
"questions": "Do generated dynamics respect useful physical regularities?"
},
{
"family": "Spatial Intelligence",
"capability": "3D and relational consistency",
"metrics": [
"Pose error",
"Depth consistency",
"Object relation accuracy",
"Map consistency"
],
"best_for": "3D, scene understanding and spatial AI",
"questions": "Does the model maintain geometry and object relationships?"
},
{
"family": "Agent Planning",
"capability": "Long-horizon decision support",
"metrics": [
"Goal success",
"Plan validity",
"Recovery rate",
"Action efficiency"
],
"best_for": "Digital and embodied agents",
"questions": "Can the model support useful multi-step plans under uncertainty?"
},
{
"family": "Long-Horizon Simulation",
"capability": "Rollout stability",
"metrics": [
"Drift",
"Consistency decay",
"Error accumulation",
"State validity"
],
"best_for": "Persistent simulations and long rollouts",
"questions": "How quickly do errors compound as the imagined future gets longer?"
},
{
"family": "Multimodal World Modeling",
"capability": "Cross-modal state understanding",
"metrics": [
"Cross-modal consistency",
"Alignment",
"Prediction quality",
"Task utility"
],
"best_for": "Vision-language-sensor world models",
"questions": "Can multiple modalities contribute to one coherent world representation?"
}
] |