mlsanityData to 14 Oct 2026

Sorries Are Not the Hard Part: An Expert-Review Case Study of a Semi-Autonomous Formalization

arXiv 2606.13925 ↗First posted 11 Jun 2026cs.AI math.AGcited by 4

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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

  1. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 427
  2. 2022Autoformalization with Large Language Modelscited by 263
  3. 2026Numina-Lean-Agent: An Open and General Agentic Reasoning System for Formal Mathematicscited by 50
  4. 2026130k Lines of Formal Topology in Two Weeks: Simple and Cheap Autoformalization for Everyone?cited by 18
  5. 2026Semi-Autonomous Formalization of the Vlasov-Maxwell-Landau Equilibriumcited by 9
  6. 2026Learning to Repair Lean Proofs from Compiler Feedbackcited by 7
  7. 2026Formalizing the stability of the two Higgs doublet model potential into Lean: identifying an error in the literaturecited by 5
  8. 2026Not All Proofs Are Equal: Evaluating LLM Proof Quality Beyond Correctnesscited by 3
  9. 2026Evaluation of LLMs for Mathematical Formalization in Leancited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.