Lemur: Integrating Large Language Models in Automated Program Verification
Haoze Wu, Clark W. Barrett, Nina Narodytska
Abstract
The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that demands high-level abstract reasoning about program properties that is challenging for verification tools. We propose a general methodology to combine the power of LLMs and automated reasoners for automated program verification. We formally describe this methodology as a set of transition rules and prove its soundness. We instantiate the calculus as a sound automated verification procedure and demonstrate practical improvements on a set of synthetic and competition benchmarks.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 41fedde4-bf57-4717-a0e2-da75ed4cea43Cited by top-tier papers15
- Enchanting Program Specification Synthesis by Large Language Models Using Static Analysis and Program VerificationCheng Wen, Jialun Cao, Jie Su, Zhiwu Xu et al.CAV 2024 · 60 citations
- Guiding Enumerative Program Synthesis with Large Language ModelsYixuan Li, Julian Parsert, Elizabeth PolgreenCAV 2024 · 19 citations
- Towards Reliable Code-as-Policies: A Neuro-Symbolic Framework for Embodied Task PlanningSanghyun Ahn, Wonje Choi, Junyong Lee, Jinwoo Park et al.NeurIPS 2025 · 14 citations
- AutoVerus: Automated Proof Generation for Rust CodeChenyuan Yang, Xuheng Li, Md Rakib Hossain Misu, Jianan Yao et al.OOPSLA 2025 · 11 citations
- LLM Meets Bounded Model Checking: Neuro-symbolic Loop Invariant InferenceGuangyuan Wu, Weining Cao, Yuan Yao, Hengfeng Wei et al.ASE 2024 · 9 citations
Builds on5
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma et al.NeurIPS 2022 · 22,562 citations
- Solving Quantitative Reasoning Problems with Language ModelsAitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer et al.NeurIPS 2022 · 2,039 citations
- Can Large Language Models Reason about Program Invariants?Kexin Pei, David Bieber, Kensen Shi, Charles Sutton et al.ICML 2023 · 128 citations
- Baldur: Whole-Proof Generation and Repair with Large Language ModelsEmily First, Markus N. Rabe, Talia Ringer, Yuriy BrunFSE 2023 · 89 citations
Related papers
- Agentic Verification of Software SystemsHaoxin Tu, Huan Zhao, Yahui Song, Mehtab Zafar et al.FSE 2026 · 1 citation
- A²RBench: An Automatic Paradigm for Formally Verifiable Abstract Reasoning Benchmark GenerationQingchuan Ma, Yuexiao Ma, Yongkang Xie, Tianyu Xie et al.ICML 2026 · 1 citation
- LLM-Assisted Synthesis of High-Assurance C ProgramsPrasita Mukherjee, Minghai Lu, Benjamin DelawareASE 2025 · 1 citation
- Can LLMs Reason About Program Semantics? A Comprehensive Evaluation of LLMs on Formal Specification InferenceThanh Le-Cong, Bach Le, Toby MurrayACL 2025
- LLM-Guided Loop Bound Generation for Program Termination VerificationZan Gong, Biting Huang, Fei HeICML 2026
