Physical world models
Compact latent states that preserve the causal and geometric structure needed to generate action-conditioned futures.
World Superintelligence Labs
Intelligence for
a world in motion.
We build world models that learn from multimodal experience, imagine action-conditioned futures, and turn prediction into planning.
01
Our thesis
Language was the beginning.
The world is the next frontier.
Physical intelligence must learn from continuous, noisy, multimodal streams—not just clean tokens. It must understand how actions change what happens next.
We develop models that preserve the causal structure of the world, simulate possible futures, and make physical knowledge useful for action.
02
Research
From representations to reliable action.
Map-free intelligent driving
Driving intelligence grounded in scenes, intent, uncertainty, and local physics—without brittle high-definition maps.
Robots that reason in motion
Perception, memory, and action working together so robots can adapt, recover, and operate in real environments.
High-dimensional planning
Reasoning systems that hold many constraints in view, simulate consequences, and select safe action sequences.