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ARXIV:2605.14061 · AI FOR MATHEMATICS · SUBMITTED 15 MAY · 20:14 UTC · FRESHNESS FRESH
ARXIV:2605.14061AI FOR MATHEMATICSSUBMITTED 15 MAY · 20:14 UTCFRESHNESS FRESHNilay Patel · Noah Arias · Davit Babayan · Victoria Cochran · Timothy Libman · Hafsah Mahmood · +4 at arXiv
MathAtlas, a large-scale benchmark for autoformalization of graduate-level mathematics, revealing significant challenges for current models.
Opportunity summary
Pain MathAtlas, a large-scale benchmark for autoformalization of graduate-level mathematics, revealing significant challenges for current models.
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MathAtlas, a large-scale benchmark for autoformalization of graduate-level mathematics, revealing significant challenges for current models. In this paper, we introduce MathAtlas, the first large-scale autoformalization benchmark of in the wild graduate-level mathematics, containing ~52k…
Current autoformalization benchmarks are largely focused on olympiad or undergraduate mathematics, while graduate and research-level mathematics remains underexplored. In this paper, we introduce MathAtlas, the first large-scale autoformalization benchmark of in the wild graduate-level…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our extensive experiments show that MathAtlas is high quality but extremely challenging: strong baselines achieve at most 9.8% correctness on theorem statements and 16.7%…
AI for Mathematics moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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MathAtlas, a large-scale benchmark for autoformalization of graduate-level mathematics, revealing significant challenges for current models.
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10.48550/arXiv.2605.14061MathAtlas, a large-scale benchmark for autoformalization of graduate-level mathematics, revealing significant challenges for current models.
Abstract
Current autoformalization benchmarks are largely focused on olympiad or undergraduate mathematics, while graduate and research-level mathematics remains underexplored. In this paper, we introduce MathAtlas, the first large-scale autoformalization benchmark of in the wild graduate-level mathematics, containing ~52k theorems, definitions, exercises, examples, and proofs extracted from 103 graduate mathematics textbooks. MathAtlas is enriched with a mathematical dependency graph containing ~178k relations, and is the first autoformalization benchmark to include such relations, facilitating evaluation and development of dependency-aware autoformalization systems. Our extensive experiments show that MathAtlas is high quality but extremely challenging: strong baselines achieve at most 9.8% correctness on theorem statements and 16.7% on definitions. Furthermore, we find performance of state-of-the-art models degrades substantially with dependency depth: on MA-Hard, a subset of 700 entities with the deepest dependency trees, the best model achieves only 2.6% correctness for autoformalization on this challenging dataset. We release MathAtlas to the community as a benchmark set for large-scale autoformalization of graduate-level mathematics in the wild.
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PROBLEM
MathAtlas, a large-scale benchmark for autoformalization of graduate-level mathematics, revealing significant challenges for current models. In this paper, we introduce MathAtlas, the first large-scale autoformalization benchmark of in the wild graduate-level mathematics, contai...
METHOD
Current autoformalization benchmarks are largely focused on olympiad or undergraduate mathematics, while graduate and research-level mathematics remains underexplored. In this paper, we introduce MathAtlas, the first large-scale autoformalization benchmark of in the wild graduat...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our extensive experiments show that MathAtlas is high quality but extremely challenging: strong baselines achieve at most 9.8% correctness on theorem statements and 16.7% on definitions. Code availability...
WHY NOW
AI for Mathematics moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
MathAtlas, a large-scale benchmark for autoformalization of graduate-level mathematics, revealing significant challenges for current models. In this paper, we introduce MathAtlas, the first large-scale autoformalization benchmark of in the wild graduate-level mathematics, containing ~52k theorems, definitions, exercises, examples, and proofs extracted from 103 graduate mathematics textbooks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Current autoformalization benchmarks are largely focused on olympiad or undergraduate mathematics, while graduate and research-level mathematics remains underexplored. In this paper, we introduce MathAtlas, the first large-scale autoformalization benchmark of in the wild graduate-level mathematics, containing ~52k theorems, definitions, exercises, examples, and proofs extracted from 103 graduate mathematics textbooks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our extensive experiments show that MathAtlas is high quality but extremely challenging: strong baselines achieve at most 9.8% correctness on theorem statements and 16.7% on definitions. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI for Mathematics moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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MathAtlas, a large-scale benchmark for autoformalization of graduate-level mathematics, revealing significant challenges for current models.
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