Year: 2025
Type: preprint
Abstract: Loop invariants are fundamental for reasoning about the correctness of iterative algorithms. However, deriving suitable invariants remains a challenging and often manual task, particularly for complex programs. In this paper, we introduce BALI, a branch-aware framework that integrates large language models (LLMs) to enhance the inference and verification of loop invariants. Our approach combines automated reasoning with branch-aware static program analysis to improve both precision and scalability. Specifically, unlike prior LLM-only guess-and-check methods, BALI first verifies branch-sequence-level (path-level) clauses with SMT and then composes them into program-level invariants. We outline its key components, present preliminary results, and discuss future directions toward fully automated invariant discovery.
Source: arXiv (Cornell University)
Authors: Wang, Mingxiu, Wang, Jiawei, Cheng, Xiao
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7119078928
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