arXiv cs.AI· Jiyuan Tan, Vasilis Syrgkanis, Jose Blanchet·· 12 小时前AI 评分38
PaperFormalizer：作者参与式的 Lean 论文自动形式化工具
PaperFormalizer: Author-in-the-Loop Autoformalization of Research Papers in Lean
AI 导读
PaperFormalizer 是一个基于 Lean 的智能体流水线，可将论文从 LaTeX 源码转成依赖图并逐条审计、形式化与证明，最终产出经 Lean 验证的论文版本。它运行在 Claude Code 或 Codex 等编码智能体中，遇到歧义或可能改变论文主张的决策时会停下来交由作者确认，并输出修改报告及与声明对应的 Lean 代码。作者可共享已验证论文，供后续研究者复用其 Lean 代码。
正文 · 原文
Abstract:Even a carefully proofread paper can leave small gaps in its proofs or its stated assumptions. We introduce PaperFormalizer, a Lean-based agentic pipeline that helps authors find and close such gaps in their own papers and turn each paper into a Lean-verified version. Starting from the LaTeX source, PaperFormalizer transcribes the paper into a dependency graph of statements, audits each statement in natural language for faithfulness, ambiguity and correctness, and then formalizes and proves the statements in Lean against Mathlib, optional domain libraries and the author's personal toolkit. Only the author knows what a statement is meant to say and how a formalization gap in a result is best fixed. The pipeline therefore stops and asks the author whenever a statement admits more than one reading, presents every repair, in natural language and TeX symbols, as a proposal that the author can accept, decline or redirect, and leaves to the author every decision that could potentially change what the paper claims. The output is an annotated and revised version of the paper, a report of the changes, and Lean code in which each declaration is linked to the statement it formalizes. The author's toolkit grows as more papers are formalized, and authors can share their verified papers so that researchers who build on them can reuse their Lean code. By making verified papers practical to produce, PaperFormalizer aims to make research that rests on mathematical proofs more robust. PaperFormalizer runs inside a coding agent (Claude Code or Codex) and is available at this https URL. Papers with Lean code shared by users are available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO) |
| Cite as: | arXiv:2610.31810 [cs.AI] |
| (or arXiv:2610.31810v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31810 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiyuan Tan [view email]
[v1]
Thu, 22 Oct 2026 18:46:53 UTC (1,452 KB)
来源：arXiv cs.AI · arxiv.org