arXiv AI By Jiyuan Tan, Vasilis Syrgkanis, Jose Blanchet

PaperFormalizer: Author-in-the-Loop Autoformalization of Research Papers in Lean

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PaperFormalizer is a Lean-based pipeline that assists authors in identifying and closing gaps in their research papers, converting them into Lean-verified versions. Starting from LaTeX, it transcribes the paper into a dependency graph, audits statements for faithfulness and correctness, and formalizes them in Lean using Mathlib and optional domain libraries. The process is author-in-the-loop, prompting the author whenever multiple interpretations exist and allowing them to accept, decline, or redirect proposed repairs, ultimately producing an annotated paper, a change report, and linked Lean code.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
5d ago

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

KAPPA is an open‑source toolkit that uses language‑model agents to translate LaTeX proofs from statistical papers into Lean 4, where each numbered result is verified step by step against the original text. The system assigns one of four verdicts—verified unconditionally, verified conditionally, issue recorded, or unfinished—to every result, and it can run unattended, re‑check only changed sections, and report resource usage. In tests on four example papers and three published statistical papers, KAPPA detected the known errors and additional issues that were later confirmed by hand, while a direct language‑model approach without the kit produced fewer correct verdicts and sometimes over‑claimed proofs.

By Zihua She, Xiao Wang