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SWE-Proof: Can Language Models Resolve Real-World Issues with Machine-Checked Proofs?

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check correctness with held-out test suites, which are inherently incomplete and increasingly susceptible to memorization. Formal verification avoids both problems, but existing work covers only standalone tasks whose specifications are given as input, not real issues, which touch large repositories and state intent in vague natural language. We present Benchproofer, a pipeline that turns a coding task with a known correct patch into a formally verified one

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