FormalProofBench: Can Models Write Graduate Level Math Proofs That Are Formally Verified?
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arXiv:2603.26996v1 Announce Type: new Abstract: We present FormalProofBench, a private benchmark designed to evaluate whether AI models can produce formally verified mathematical proofs at the graduate level. Each task pairs a natural-language problem with a Lean~4 formal statement, and a model must output a Lean proof accepted by the Lean 4 checker. FormalProofBench targets advanced undergraduate and graduate mathematics, with problems drawn from qualifying exams and standard textbooks across t
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Computer Science > Artificial Intelligence
[Submitted on 27 Mar 2026]
FormalProofBench: Can Models Write Graduate Level Math Proofs That Are Formally Verified?
Nikil Ravi, Kexing Ying, Vasilii Nesterov, Rayan Krishnan, Elif Uskuplu, Bingyu Xia, Janitha Aswedige, Langston Nashold
We present FormalProofBench, a private benchmark designed to evaluate whether AI models can produce formally verified mathematical proofs at the graduate level. Each task pairs a natural-language problem with a Lean~4 formal statement, and a model must output a Lean proof accepted by the Lean 4 checker. FormalProofBench targets advanced undergraduate and graduate mathematics, with problems drawn from qualifying exams and standard textbooks across topics including analysis, algebra, probability, and logic. We evaluate a range of frontier models with an agentic harness, and find that the best-performing foundation model achieves 33.5% accuracy, with performance dropping rapidly after that. In addition to the accuracy numbers, we also provide empirical analysis of tool-use, failure modes, cost and latency, thereby providing a thorough evaluation of the formal-theorem proving abilities of frontier models.
Comments: Accepted at ICLR 2026 Workshop: VerifAI-2: The Second Workshop on AI Verification in the Wild. Live leaderboard hosted here: this https URL
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Programming Languages (cs.PL)
Cite as: arXiv:2603.26996 [cs.AI]
(or arXiv:2603.26996v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2603.26996
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From: Nikil Ravi [view email]
[v1] Fri, 27 Mar 2026 21:14:53 UTC (430 KB)
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