mlsanityData to 14 Oct 2026

Theory-Level Autoformalization: From Isolated Statements to Unified Formal Knowledge Bases

arXiv 2607.13292 ↗First posted 14 Jul 2026cs.AI cs.CL cs.LGcited by 3

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Abstract (arXiv)

Autoformalization translates informal natural language into formal, machine-verifiable languages. While most work focuses on individual statements, real formalization efforts are inherently theory-level: they require an entire web of axioms, definitions, and lemmas before target theorems can even be stated. In this position paper, we argue for theory-level autoformalization: formalizing complete theories, including all their inter-dependencies, as structured libraries. We examine the significance of this shift, address alternative views, identify open challenges, and propose three promising paths forward. Our survey of autoformalization is available at https://github.com/marcusm117/Awesome-Autoformalization.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 37 of its 89 references that are in the map. First posted on arXiv 14 Jul 2026.

Builds on

37 of 89 references.

References · 37

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  12. 2024Lean Workbook: A large-scale Lean problem set formalized from natural language math problemscited by 122
25 more
  1. 2024TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Taskscited by 75
  2. 2025Hilbert: Recursively Building Formal Proofs with Informal Reasoningcited by 60
  3. 2024Herald: A Natural Language Annotated Lean 4 Datasetcited by 59
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  12. 2025Aria: An Agent For Retrieval and Iterative Auto-Formalization via Dependency Graphcited by 21
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  15. 2023A New Approach Towards Autoformalizationcited by 10
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  18. 2025FormaRL: Enhancing Autoformalization with no Labeled Datacited by 9
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.