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LLMs and symbolic verification combine to prove program termination

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RAINFALL, a novel neuro-symbolic framework that uses Large Language Models (LLMs) to generate ranking functions for proving program termination. This framework treats ranking function synthesis as a code-completion task, inserting placeholders into programs for LLMs to fill. The generated code is then verified by a symbolic verifier, with feedback used for iterative refinement. RAINFALL achieved a 90.22% success rate on a benchmark suite of C programs, significantly outperforming existing methods and demonstrating the potential of LLM-assisted formal verification in software engineering. AI

IMPACT This research could accelerate the development of more reliable software by automating a complex aspect of formal verification.

RANK_REASON The cluster describes a new research paper detailing a novel framework for program termination verification using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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LLMs and symbolic verification combine to prove program termination

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The cluster describes a new research paper detailing a novel framework for program termination verification using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rongkang Li, Jiaxin Hu, Taolue Chen, Yong Li, Fu Song, Zhilin Wu ·

    Ranking Function Generation with LLMs for Termination

    arXiv:2610.35523v1 Announce Type: cross Abstract: Ranking functions are a fundamental technique for proving loop termination, but constructing them manually is challenging and requires substantial expertise. We propose RAINFALL, a neuro-symbolic framework which follows the genera…

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