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arXiv:2606.13473cs.LG

MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling

Jiacheng Chen, Xinyu Zhang, Shunkai Zhang, Yanmohan Wang, Lin Li, Tiancheng Qin, Qin Wang, Zhengmao Zhu, Tianle Li, Jingyang Li, Zehan Li, Binyang Jiang, Jin Zhu, Han Ding, Fei Yu, Chenyu Du, Zijian Song, Jiayuan Song, Zhi Zhang, Yunan Huang, Weiyu Cheng, Pengyu Zhao, Yu Cheng

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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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DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence cs.CLComputation and Language

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jul 2026 to Jul 2026. Found in 1 HN submission from Jun 2026 to Jun 2026. Open submission discussions.

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    • > this year or next maybe; I meant "in a single year eventually", not this year necessarily > what research transformers were published in 2017, and nobody anticipated agentic coding back then; but if I had to guess: 1. lean self play as in https://arxiv.org/abs/2502.00212 and…

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