Autonomous systems have never feared mistakes themselves, but rather that something goes wrong and no one can clearly explain why it was done.

People can accept judgment errors, but it’s hard to tolerate a situation where:
The result has already occurred, yet the decision-making process remains a black box.

Many AI systems get stuck in high-risk scenarios and cannot move forward, not because of lack of capability, but because their decision logic cannot be externally verified at all.

@inference_labs’ approach is very clear:
Instead of wasting effort explaining what the model is "thinking" in its "brain," it directly proves whether its behavior has crossed any boundaries.

Whether the behavior is compliant, whether rules are strictly followed, and whether decisions are traceable.
In the world of autonomous systems, this is often more critical than "making the reasoning sound."