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P1 Data-driven lemma synthesis for interactive proofs

面向交互式证明的数据驱动引理合成
OOPSLA 6(OOPSLA2)2022匹配度 5/10 · 论文属于交互式定理证明中的引理合成,与形式化验证/证明工程相关,并具备理论归约与工具实现;但它采用数据驱动程序合成而非 LLM/Agent,未涉及静态分析、浮点程序分析或贝叶斯程序分析,因此仅与“AI→PL”中的非 LLM 形式化验证方向部分相关。引用 8 inbox
Aishwarya Sivaraman, Alex Sanchez-Stern, Bretton Chen, Sorin Lerner, Todd Millstein
crossref: 2026-09-10

AI 速览

一句话:提出数据驱动的引理合成方法,将交互式证明中的辅助引理生成归约为程序合成,并实现为 Coq 策略 lfind。

交互式定理证明常需辅助引理,但现有自动合成方法难以兼顾目标导向与表达力:目标导向方法生成引理受限,基于文法的方法虽表达力强却缺乏方向性,不适合交互使用。本文提出兼具目标导向与表达力的引理合成方法,核心是将引理合成归约为数据驱动的程序合成问题,从当前证明状态生成合成样例;同时给出系统化引入新变量的技术,以及对候选引理进行过滤和排序以便呈现给用户。作者实现 Coq 策略 lfind,在四个基准套件上评估:在人类证明者使用引理取得进展的案例中,68% 能生成有用引理,这些引理或使原目标可全自动证明,或与人工引理匹配。

问题
交互式证明中辅助引理合成如何同时做到目标导向与表达力,以适配用户当前证明状态并生成有用引理。
方法
将引理合成归约为数据驱动程序合成,从当前证明状态生成样例,并系统引入新变量,再对候选引理过滤与排序;实现为 Coq 策略 lfind。
评估
在四个基准套件上评估,lfind 在人类证明者使用引理取得进展的案例中 68% 生成有用引理,或自动化原目标或匹配人工引理。
原文摘要(Abstract)

Interactive proofs of theorems often require auxiliary helper lemmas to prove the desired theorem. Existing approaches for automatically synthesizing helper lemmas fall into two broad categories. Some approaches are goal-directed, producing lemmas specifically to help a user make progress from a given proof state, but they have limited expressiveness in terms of the lemmas that can be produced. Other approaches are highly expressive, able to generate arbitrary lemmas from a given grammar, but they are completely undirected and hence not amenable to interactive usage. In this paper, we develop an approach to lemma synthesis that is both goal-directed and expressive. The key novelty is a technique for reducing lemma synthesis to a data-driven program synthesis problem, whereby examples for synthesis are generated from the current proof state. We also describe a technique to systematically introduce new variables for lemma synthesis, as well as techniques for filtering and ranking candidate lemmas for presentation to the user. We implement these ideas in a tool called lfind, which can be run as a Coq tactic. In an evaluation on four benchmark suites, lfind produces useful lemmas in 68% of the cases where a human prover used a lemma to make progress. In these cases lfind synthesizes a lemma that either enables a fully automated proof of the original goal or that matches the human-provided lemma.

链接与引用

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BibTeX
@article{paperbot1454,
  title = {Data-driven lemma synthesis for interactive proofs},
  author = {Aishwarya Sivaraman and Alex Sanchez-Stern and Bretton Chen and Sorin Lerner and Todd Millstein},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {6},
  number = {OOPSLA2},
  year = {2022},
  doi = {10.1145/3563306}
}