INTERPRETABILITY

Mechanistic interpretability at RA1 Labs means analyzing neural systems at the level of what they actually compute — the circuits, the attributions, the real execution — rather than what their outputs appear to suggest.

The problem

AI is deployed at scale without real understanding of what these systems compute. You cannot trust what you do not understand. You cannot responsibly deploy what you cannot verify. RA1 Labs exists to close that gap.

How we work

• Behavioral attribution through hardware traces — grounding interpretation in real execution, not approximation.
• Circuit-level analysis of model decisions.
• Verification infrastructure that bypasses interpretability approximations to check behavior at the instruction level.

Tied to inference

Our interpretability work runs on Raven, our own inference engine — which means we can instrument computation directly rather than treating the model as a black box.

Led by Kanishk Joshi, founder of RA1 Labs.