Preguss: It Analyzes, It Specifies, It Verifies

• URL: http://arxiv.org/abs/2508.14532v1
• Date: Wed, 20 Aug 2025 08:40:22 GMT
• Title: Preguss: It Analyzes, It Specifies, It Verifies
• Authors: Zhongyi Wang, Tengjie Lin, Mingshuai Chen, Mingqi Yang, Haokun Li, Xiao Yi, Shengchao Qin, Jianwei Yin,
• Abstract summary: Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification.<n>This paper outlines Preguss - a modular, fine-grained framework for automating the generation and refinement of formal specifications.
• Score: 14.717270519465218
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to context-length limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This paper outlines Preguss - a modular, fine-grained framework for automating the generation and refinement of formal specifications. Preguss synergizes between static analysis and deductive verification by orchestrating two components: (i) potential runtime error (RTE)-guided construction and prioritization of verification units, and (ii) LLM-aided synthesis of interprocedural specifications at the unit level. We envisage that Preguss paves a compelling path towards the automated verification of large-scale programs.

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