Theory-Level Autoformalization: From Isolated Statements to Unified Formal Knowledge Bases
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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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25 more
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Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026VALG: An Agentic System for ML Theory Researchcited by 2
- 2026Aligning Faithfulness for Theory-Scale Autoformalizationcited by 0
- 2026Definition Grounding in Theory-Level Auto-formalizationcited by 0
- 2026Definition Grounding in Theory-Level Auto-formalization13 Oct 2026
- 2026Aligning Faithfulness for Theory-Scale Autoformalization13 Oct 2026
- 2026VALG: An Agentic System for ML Theory Research13 Aug 2026