More general intelligence must do more than reproduce a familiar solution. We are interested in systems that can reason through new problems, examine assumptions, and revise a plan when the evidence changes.
Questions we’re interested in
- How can a model reliably solve problems outside its familiar training patterns?
- How should a system reason about uncertainty over long sequences of decisions?
- What separates a persuasive explanation from a well-supported conclusion?
Our starting point
Our research direction connects model architectures, learning and inference-time reasoning. We want methods that can be examined through clear task definitions, careful comparisons and analysis of failures.
What this means today
These are research questions. We have not announced a reasoning model or published benchmark results; any future capability claim will need reproducible evidence.
Frontier model research