Sorries Are Not the Hard Part: An Expert-Review Case Study of a Semi-Autonomous Formalization
Not on the map. Why?
Abstract (arXiv)
Large language models can often close proof gaps in interactive theorem provers, but a verified theorem is not the same thing as a reusable library contribution. We study this distinction through a detailed case study: a semi-autonomous formalization of Grothendieck's vanishing theorem. The initial version compiles with no sorries, but an expert review found serious problems in definitions, theorem generality, file organization, and the API. We then ran a review-driven refactor and compression process and obtained a second expert review. The before-and-after comparison shows a sharp split: agents adapted well to local, mechanically checkable feedback, but remained weak at choosing definitions and designing APIs. We argue that autoformalization should be evaluated not only by closed sorries, but by whether the resulting formalization survives expert review.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 11 Jun 2026.
Builds on
9 of 17 references.
References · 9
- 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 427
- 2022Autoformalization with Large Language Modelscited by 263
- 2026Numina-Lean-Agent: An Open and General Agentic Reasoning System for Formal Mathematicscited by 50
- 2026130k Lines of Formal Topology in Two Weeks: Simple and Cheap Autoformalization for Everyone?cited by 18
- 2026Semi-Autonomous Formalization of the Vlasov-Maxwell-Landau Equilibriumcited by 9
- 2026Learning to Repair Lean Proofs from Compiler Feedbackcited by 7
- 2026Formalizing the stability of the two Higgs doublet model potential into Lean: identifying an error in the literaturecited by 5
- 2026Not All Proofs Are Equal: Evaluating LLM Proof Quality Beyond Correctnesscited by 3
- 2026Evaluation of LLMs for Mathematical Formalization in Leancited by 3
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inferencecited by 1
- 2026Lean Pool: An AI-Maintained Archive of Formalized Mathematicscited by 1
- 2026AIProver: Agentic Auto-Formalization of Mathematical Research via Certificate-Driven Evolving Harnesscited by 0
- 2026Definition Grounding in Theory-Level Auto-formalizationcited by 0
- 2026Definition Grounding in Theory-Level Auto-formalization13 Oct 2026
- 2026AIProver: Agentic Auto-Formalization of Mathematical Research via Certificate-Driven Evolving Harness4 Oct 2026
- 2026Lean Pool: An AI-Maintained Archive of Formalized Mathematics21 Sep 2026
- 2026CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference24 Jul 2026