FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning 文章

ArXiv CS.AI2026-06-19NEWSen作者: Haoran Ding, Zhaoguo Wang, Haibo Chen

详细信息

来源站点
ArXiv CS.AI
作者
Haoran Ding, Zhaoguo Wang, Haibo Chen
文章类型
NEWS
语言
en
发布日期
2026-06-19

摘要

arXiv:2604.11556v2 Announce Type: replace-cross Abstract: LLM-assisted software development has become increasingly prevalent, and can generate large-scale systems, such as compilers. It becomes crucial to strengthen the correctness of the generated code. However, automated reasoning for large-scale systems remains challenging due to code complexity. Hoare logic offers an approach to decomposing a large system into smaller components and reasoning about them separately (i.e., compositional reasoning). However, existing works still struggle to scale, because Hoare logic requires writing formal specifications for each function, imposing a heavy human burden. The problem is exacerbated when code is generated by LLMs, as developers lack a deep understanding of each function's expected behavior. This paper presents FM-Agent, the first framework that realizes automated compositional reasoning for large-scale systems.

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