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Reasonable is training the models, building the tooling, and developing the agentic frameworks to make formal verification accessible to all software engineers. Our latest updates, findings, research, and contributions:

Every line of formally verified production code requires on average six or seven lines of machine-checked proof alongside it. Today, only the largest, slowest models write those proofs with some reliability. That is a bottleneck for formal verification and one of the inhibitors of broader adoption in mainstream software development.

We fine-tuned NVIDIA Nemotron 3.5 Lightning to write machine-checkable proofs in Verus, a verification layer that lets developers mathematically prove their Rust code does what its specification says. The result is a model that nearly matches the pass@3 performance of a model ~50× its size, while yielding more successful attempts overall and generating tokens faster than any similarly sized open-weight model we tested. Along the way, we catalogued how tested models fail at formal proofs, how they cheat, and the effects of fine-tuning.

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© reasonable ai 2026

Inspect

Verify

Trust

© reasonable ai 2026

Inspect

Verify

Trust

© reasonable ai 2026