HackerPapers

arXiv:2410.19605cs.SE

CoqPilot, a plugin for LLM-based generation of proofs

Andrei Kozyrev, Gleb Solovev, Nikita Khramov, Anton Podkopaev

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

We present CoqPilot, a VS Code extension designed to help automate writing of Coq proofs. The plugin collects the parts of proofs marked with the admit tactic in a Coq file, i.e., proof holes, and combines LLMs along with non-machine-learning methods to generate proof candidates for the holes. Then, CoqPilot checks if each proof candidate solves the given subgoal and, if successful, replaces the hole with it. The focus of CoqPilot is twofold. Firstly, we want to allow users to seamlessly combine multiple Coq generation approaches and provide a zero-setup experience for our tool. Secondly, we want to deliver a platform for LLM-based experiments on Coq proof generation. We developed a benchmarking system for Coq generation methods, available in the plugin, and conducted an experiment using it, showcasing the framework's possibilities. Demo of CoqPilot is available at: https://youtu.be/oB1Lx-So9Lo. Code at: https://github.com/JetBrains-Research/coqpilot

arXiv comments
Published in the proceedings of the ASE'24 Tool Demonstrations Track
Journal reference
DOI
doi:10.1145/3691620.3695357
Subjects
cs.SESoftware Engineering cs.AIArtificial Intelligence cs.LOLogic in Computer Science

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jan 2025 to Jan 2025.

Comments linking this paper, per year.

Discussed in comments1 thread

    • Not yet, but it's definitely something worth experimenting. FWIW, there are precedents to this, e.g. Coq developers typically write the types and (when they're lucky) have "strategies" that write the bodies of functions. This was before LLMs, but now, some people are…

      YoricView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.