Ding Wang, Chao Jiang, Dugang Liu, Zhiwu Xu, Cheng Wen, Zhong Ming, Yuwei Liu, Xinyi Wan · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26464 · open-access PDF
대형 언어 모델(LLMs)은 Rust 프로그램에 대한 Verus 계약을 생성하기 위해 점점 더 많이 사용되고 있으며, 이를 확립하는 증거와 함께 사용됩니다.그러나 실행이 생성된 계약을 충족한다는 것을 보여주는 일반적인 성공 측정인 Verifier의 수용은 실행이 생성된 계약을 충족한다는 것을 보여줍니다, 따라서 계약이 의도된 행동을 캡처하는지 여부가 열려 있습니다.특별화 생성을 평가하는 것은 단일 메트릭이 아닌 평가 방법론을 필요로합니다.
Machine translation (Meta M2M100); the English original follows.
Large language models (LLMs) are increasingly used to generate Verus contracts for Rust programs together with the proofs that establish them. Yet verifier acceptance, the common measure of success, shows only that the implementation satisfies the generated contract, so it leaves open whether the contract captures the intended behavior. Assessing specification generation therefore requires an evaluation methodology, not a single metric. We present VerusEval, a framework that evaluates generated contracts along six dimensions, including Verus-native checks for semantic triviality, validated inp
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