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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.
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.
SemantEq introduces a principled method for verifying that formal statements faithfully translate natural‑language mathematics by requiring each statement to transform into the other through a chain of allowed logical steps, rather than relying on trivial logical equivalence. The system evaluates proofs using Lean’s proof terms, achieving 100 % precision and 65.6 % recall on the ProofNetVerif test split, outperforming prior methods. A complementary audit tool, SemantEq‑ProofAudit, further improves recall for equivalent pairs while rejecting all non‑equivalent pairs.
arXiv:2610.17358v1 Announce Type: cross Abstract: Formal verification proves that a program satisfies its specification, but says nothing about whether the specification captures what the author mean...
arXiv:2609.39228v1 Announce Type: new Abstract: We present FYAN, a human--AI harness for document-level mathematical formalization. Rather than treating theorems in isolation, FYAN coordinates an end...
arXiv:2607. 22511v1 Announce Type: cross Abstract: Automating theoretical research is constrained not only by the generation of candidate results, but also by their reliable evaluation.