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arXiv · 2607.05478

InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs

Guangyuan Wu·Weining Cao·Zehui Tan·Yuan Yao·Hengfeng Wei·Taolue Chen·Xiaoxing Ma
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Abstract

Loop invariant inference is a fundamental yet challenging problem in program verification. Recent LLM-aided guess-and-check techniques have shown strong performance on single-loop programs, but they often struggle with programs containing multiple interacting loops. This paper presents InvWeaver, a neuro-symbolic framework for synthesizing invariants for such programs. The key idea is to expose inter-loop dependencies and propagate proof obligations through a combination of loop-level abstraction, obligation-guided inference, and weakest-precondition-based refinement. We evaluate InvWeaver on a comprehensive benchmark suite, including a newly curated dataset derived from classic algorithms. Experimental results show that InvWeaver substantially outperforms existing invariant inference methods, solving 72 out of 82 multi-loop benchmark problems and maintaining strong performance on single-loop tasks.

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Guangyuan Wu, Weining Cao, Zehui Tan, Yuan Yao, Hengfeng Wei, Taolue Chen, Xiaoxing Ma. 2026-07-06. InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs. https://arxiv.org/abs/2607.05478

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