Explore how perception, shared representations, memory, reasoning, tools, world models, and actions can connect inside an omnimodal AI system.
Select a layer to focus the map.
Instructions, dialogue, code, documents, metadata and structured information.
Images, screenshots, charts, scenes, objects and visual relationships.
Speech, acoustic events, prosody, streaming audio and environmental sound.
Motion, events, sequence, temporal context and changing environments.
Depth, geometry, maps, scene structure, pose and object relationships.
Cameras, lidar, radar, GPS, inertial data, telemetry and physical state.
A common semantic layer that connects information across modalities.
Links outputs to observed, retrieved or sensed evidence.
Preserves task state, observations, events and relevant multimodal context over time.
Selects which multimodal information should remain available for the current task.
Combines evidence from different modalities and detects agreement or contradiction.
Understands order, duration, change, sequence and prediction over time.
Understands position, depth, geometry, movement and physical relationships.
Represent environment state, dynamics and possible consequences of actions.
Maintain goals, state and execution loops across modalities and time.
Search, APIs, code, databases, enterprise systems and external capabilities.
Changes to software, devices, machines or physical environments.
Connects multimodal perception and reasoning to embodied action in the real world.
Checks grounding, consistency, timing, spatial correctness, robustness and action quality.
Tracks modalities, model calls, routing, memory, tools, failures and actions.
Connects specialized models, tools and infrastructure across common interfaces.
Permissions, escalation, human override and safe operating boundaries.