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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.29955 |
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| _version_ | 1866911728441229312 |
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| author | Rammal, Ahmad Patel, Niket Gloeckle, Fabian Hayat, Amaury Kempe, Julia Munos, Remi Arnal, Charles Cabannes, Vivien |
| author_facet | Rammal, Ahmad Patel, Niket Gloeckle, Fabian Hayat, Amaury Kempe, Julia Munos, Remi Arnal, Charles Cabannes, Vivien |
| contents | We present AutoformBot, a multi-agent system for building an Autoformalized Textbook Library At Scale (Atlas) in Lean 4. AutoformBot orchestrates thousands of LLM agents, equipped with formal verification tools, dependency-aware task scheduling, and collaborative version control, to translate informal textbook prose into machine-checked definitions and proofs. We apply our methods to a corpus of 26 open-access textbooks spanning analysis, algebra, topology, combinatorics, and probability, producing Atlas: a verified library of over 45,000 Lean 4 declarations and 500 thousand lines of code. We release two artifacts: (i) AutoformBot, the open-source multi-agent framework; and (ii) Atlas, the resulting formal library. Our results suggest that autoformalizing the core content of graduate-level mathematics at scale is now economically and technically feasible. This opens the door to the automated verification of both human- and machine-generated mathematics at a research level. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29955 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Formalizing Mathematics at Scale Rammal, Ahmad Patel, Niket Gloeckle, Fabian Hayat, Amaury Kempe, Julia Munos, Remi Arnal, Charles Cabannes, Vivien Artificial Intelligence We present AutoformBot, a multi-agent system for building an Autoformalized Textbook Library At Scale (Atlas) in Lean 4. AutoformBot orchestrates thousands of LLM agents, equipped with formal verification tools, dependency-aware task scheduling, and collaborative version control, to translate informal textbook prose into machine-checked definitions and proofs. We apply our methods to a corpus of 26 open-access textbooks spanning analysis, algebra, topology, combinatorics, and probability, producing Atlas: a verified library of over 45,000 Lean 4 declarations and 500 thousand lines of code. We release two artifacts: (i) AutoformBot, the open-source multi-agent framework; and (ii) Atlas, the resulting formal library. Our results suggest that autoformalizing the core content of graduate-level mathematics at scale is now economically and technically feasible. This opens the door to the automated verification of both human- and machine-generated mathematics at a research level. |
| title | Formalizing Mathematics at Scale |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.29955 |