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
大型语言模型(LLMs)越来越多地被用来生成Rust计划的Verus合同,以及建立它们的证据。然而,验证器的接受,成功的常见尺度,只表明实施满足生成的合同,因此它不清楚合同是否捕捉了预期的行为。因此,评估规格生成需要一个评估方法,而不是一个单一的指标。
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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