Research
MaxProof： Scaling Mathematical Proof with Generative-Verifier RL and Evolutionary Search
MaxProof框架：MiniMax M3在IMO 2025和USAMO 2026超越人类金牌线
MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Evolutionary Search - MiniMax Research
MiniMaxIn the M3 release post, we reported the performance of the M3 model on two international mathematical olympiad benchmarks: IMO 2025 and USAMO 2026. With the MaxProof framework, M3 exceeded the human gold-medal threshold on both. This article further elaborates on our technical path toward advancing mathematical proof capabilities, including base model enhancement, verifier alignment, refinement capability building, and the design of the test-time scaling framework MaxProof.
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What to take from this signal
Context
"MaxProof： Scaling Mathematical Proof with Generative-Verifier RL and Evolutionary Search" is archived here as a source-linked AI signal from MiniMax. The useful part is the connection between MaxProof, Scaling, Mathematical, Proof, Generative-Verifier and technical due diligence, roadmap bets, agent design, and evaluation strategy, which makes the item more actionable than a normal feed headline. The source context says: In the M3 release post, we reported the performance of the M3 model on two international mathematical olympiad benchmarks: IMO 2025 and USAMO 2026. With the MaxProof framework, M3 exceeded the human gold-medal threshold on both. This article further elaborates on our technical path toward advancing mathematical proof capabilities, including base model enhancement, verifier alignment, refinement capability building, and the design of the test-time scaling framework MaxProof.
Builder takeaway
For an AI builder, the main takeaway is to watch how this signal changes practical decisions around technical feasibility, evaluation design, safety limits, and product primitives. It can inform what to test next, which product surface to compare, and whether the underlying workflow is ready for real users.
Source context
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