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[ARTICLE Β· art-25173] src=arxiv.org pub= topic=machine-learning verified=true sentiment=↑ positive

Maxproof

Researchers have developed MaxProof, a population-level test-time scaling framework for mathematical proof that enables the MiniMax-M3 model to achieve 35 out of 42 on IMO 2025 and 36 out of 42 on USAMO 2026, surpassing the human gold-medal threshold on both competitions. The system integrates proof generation, verification, and critique-conditioned repair into a single model, then searches over candidate proofs through tournament selection to produce a final output. This marks the first time an AI system has exceeded the gold-medal standard on these elite mathematical olympiads.

read2 min publishedJun 12, 2026
[Submitted on 11 Jun 2026]


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Abstract:We present MaxProof, a population-level test-time scaling framework for competition-level mathematical proof in the MiniMax-M3 series. M3 first trains three proof-oriented capabilities -- proof generation, proof verification, and critique-conditioned proof repair -- using a defense-in-depth generative verifier engineered for low false-positive rate. These capabilities are merged into a single released M3 model. At test time, MaxProof treats the model as a generator, verifier, refiner, and ranker, searches over a population of candidate proofs, and returns one final proof through tournament selection. With MaxProof test-time scaling, the M3 model reaches 35/42 on IMO 2025 and 36/42 on USAMO 2026, exceeding the human gold-medal threshold on both.

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