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arXiv cs.AI· Zihua She, Xiao Wang·· 4 小时前AI 评分54

KAPPA:用 Claude Code 智能体与 Lean 4 核验统计学论文证明

KAPPA: Checking the Proofs in Statistics Papers with Lean 4 and Language Model Agents

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作者发布开源工具 KAPPA,组织由 Claude Code 运行的语言模型智能体,将 LaTeX 论文中的证明逐条翻译到 Lean 4(含 Mathlib)并给出四类核验结论。该工具在三篇已发表统计学论文中定位出勘误所指的错误及更多经人工确认的问题;对比实验显示,同一模型直接读论文时在最长一篇上分类正确率更低且误判部分命题。

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Abstract:Papers in statistical theory rest on long proofs that few readers check line by line. We present KAPPA (Kit of Agents for Proof-checking Papers with a proof Assistant), open-source software that organizes language model agents, run by Claude Code, to translate the proofs of a LaTeX paper into the proof assistant Lean 4 with its library Mathlib and check them there, result by result. Each numbered result receives one of four verdicts: verified unconditionally; verified conditionally, with every cited or unproved input stated as a named hypothesis; issue recorded, with its place in the paper and the side at fault; or unfinished, with the reason. The kit follows the paper's own proof step by step, rather than finding a new one. Separate agents write and review, each statement is compared with the paper before it is proved and fingerprinted afterwards, and every verdict rests on evidence that can be rerun without a language model. The kit runs unattended for hours, waits out usage limits, re-checks only what a revision changed and reports its cost by role, model and result. We describe the design, walk through a complete run, and apply the kit to four example papers with answer keys and to three published statistical papers, checked blind in their versions from before public corrections. In all three, the kit found the published error at the step that the correction names, together with further problems that we confirmed by hand. Given the same papers directly, the same language model working with Lean classified fewer results correctly on the longest paper and reported some statements narrower than the paper's as proved; no verdict of the kit claimed more than its Lean evidence showed.
Comments: 31 pages, 2 figures. Software: this https URL
Subjects: Computation (stat.CO); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
Cite as: arXiv:2610.35596 [stat.CO]
  (or arXiv:2610.35596v1 [stat.CO] for this version)
  https://doi.org/10.48550/arXiv.2610.35596

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zihua She [view email]
[v1] Mon, 26 Oct 2026 15:22:15 UTC (45 KB)

来源:arXiv cs.AI · arxiv.org