ICML 2026poster0 citations

LLM-Guided Loop Bound Generation for Program Termination Verification

Zan Gong, Biting Huang, Fei He

Abstract

Program termination is a fundamental liveness property in software verification. Proving termination of a given program is a formidable challenge due to the undecidability of the problem. In this paper, we propose LIFT, a termination verification framework that leverages LLMs to generate loop bounds within a guess-and-check workflow. LIFT couples this generation with a sound formal validation procedure that both guarantees all reported terminations and refutes invalid loop bounds via violation analysis. Experiments on publicly accessible termination benchmarks show that LIFT significantly outperforms existing termination verification tools.

LLMTheoryRetrievalBenchmark
BibTeX
@inproceedings{
gong2026llmguided,
title={{LLM}-Guided Loop Bound Generation for Program Termination Verification},
author={Zan Gong and Biting Huang and Fei He},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=ym8FNvY5z4}
}