arXiv:2608.15432cs.AI2026-08被引 1

让形式化证明忠实还原自然语言推理过程,提升数学证明的可验证性。

Does the Proof Prove It That Way? Faithful Formalization of Elements Proofs

  • 提出五条忠实形式化必要条件,设计OrderDecompose搜索策略保持推理路径一致。
  • 在欧几里得《几何原本》前3卷中生成高质量忠实证明,编译速度提升33倍。
  • 可识别原始证明中的漏洞,适合数学家与AI协作验证复杂论证。

在形式化验证中,命题的自动形式化与自动化证明搜索已广泛研究。尽管自动化证明可生成可编译的形式化证明,但其未必反映自然语言论证的推理路径——这一特性称为忠实性。具备忠实性的形式化证明可用于检验人类或AI写出的论证逻辑,并辅助数学家形式化证明草图。然而,由于形式化证明策略与自然语言推理之间的错位,实现忠实性极具挑战。本文严格定义了五项忠实形式化证明必须满足的必要条件,提出Pistis系统,一种基于代理、受引导的证明搜索方法,能生成满足这些条件的Lean形式化证明。核心是名为OrderDecompose的新颖分治搜索机制,通过追踪引用依赖关系并阻止非忠实捷径,结合反证搜索以揭示自然语言证明中的漏洞和错误。OrderDecompose在12小时预算内完成基线无法闭合的证明,其产物编译速度比之前工作快33倍以上。我们将Pistis应用于欧几里得《几何原本》前三个卷,生成高质量的忠实形式化证明。在盲评人类研究和基于LLM的评判协议下,其生成的证明分别被人类评审员和LLM judge选择2.89倍和5.2倍更多。该系统还发现了欧几里得证明及其翻译中的缺陷,能够接受或驳回由人类或AI撰写的自然语言证明,表明忠实形式化可作为有效的证明检查工具。

原文摘要 · Abstract (English)

In formal verification, both the autoformalization of statements and automated proof search have been studied extensively. While automated proof search can produce a formal proof that compiles, the generated proof does not necessarily reflect how the natural-language argument arrives at its conclusion--a property we refer to as faithfulness. With faithfully formalized proofs, one can check the reasoning behind a human- or AI-written argument, and assist mathematicians in formalizing their proof sketches. However, it is particularly challenging due to misalignment of formal proof tactics and natural language reasoning. In this work, we rigorously describe a set of five necessary conditions a faithful formal proof must satisfy, and introduce Pistis, an agentic, oracle-guided proof search that produces formal Lean proofs that satisfy them. At its core is a novel faithfulness-preserving divide-and-conquer search, which we name OrderDecompose, that tracks citation dependencies and blocks unfaithful shortcuts, paired with a refutation search, that surfaces gaps and errors in the natural language proof source. OrderDecompose completes proofs that baselines cannot close even within a 12-hour budget, and its artifacts compile over 33$\times$ as fast as prior work's. We apply Pistis on the first three books of Euclid's Elements, producing high-quality artifacts containing faithful formal proofs. Under a blinded human study and an LLM-as-a-judge protocol on rigorous rubrics, Pistis-generated proofs are favored over prior works--2.89$\times$ and 5.2$\times$ as often by human reviewers and the LLM judge, respectively. It further uncovers gaps in Euclid's proofs and their translation, and can accept or refute natural language proofs written by humans or AI, demonstrating that faithful formalization is useful as a proof-checking tool.

形式化验证数学证明忠实性Lean

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