sophronUnderstanding before autonomy
We believe the ability to act should be grounded in an understanding of the environment and the consequences of action.
World models for autonomous agents
Intelligence, with a world in mind.
We’re building AI-powered world models that help autonomous agents understand their environments—and reason about what comes next.
rollouts 28horizon 9best score 0steps 0
Tap cells to add or remove walls. Shift-click moves the goal.
An internal model. An expanded horizon.
01
Read the environment
Observations are the starting point: signals about the world an agent is operating within.
02
Build a world model
An internal representation connects the current state with the dynamics that shape it.
03
Reason forward
The model helps an agent anticipate how its environment may change in response to action.
04
Make more informed decisions
Connect understanding and prediction to action, with the goal of more reliable behavior.
The power of what if
Before acting, imagine.
What if an agent could consider the consequences of a decision before making it?
We’re developing models that help agents reason through possible futures, compare courses of action, and respond with more than a reflex. Not only “What is happening?” but “What happens if?”
Action-conditioned futuresillustrative
Route through gap0.92
Wait and observe0.64
Go around0.47
Cut the corner0.21
Foundational questions. Long-horizon thinking.
R.01
Representation
What does an agent need to know about its world?
Learning useful structure from observations, so an internal model can support reasoning rather than merely store information.
R.02
Dynamics
How does an environment change over time?
Modeling change and interaction, with attention to the uncertainty that comes with predicting what is not yet observed.
R.03
Consequences
How do actions shape what happens next?
Connecting possible actions to possible outcomes, so predictions can become a useful part of an agent’s decision process.
R.04
Reliability
How can understanding lead to more dependable behavior?
Examining where models are useful, where they fall short, and what those limits mean for autonomous decisions.
Ambitious about intelligence. Careful about its foundations.
Uncertainty belongs in the model
The world is only partially observed. Reasoning about it means taking incomplete information and uncertain outcomes seriously.
Reliability is a goal, not a given
More dependable autonomy requires careful attention to a model’s limits—not just its possibilities.
Start a conversation
Let’s think worlds ahead.
Working on the next generation of autonomous systems? Interested in world models or research collaboration? We’d like to hear from you.