MaxProof：面向数学证明的群体级别测试时扩展框架（MiniMax-M3）
MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling
MaxProof 是为 MiniMax-M3 系列设计的群体级别测试时扩展框架，用于竞赛级数学证明。M3 模型训练了证明生成、证明验证和基于 critique 的证明修复三种能力，验证器采用低假阳性率的深度防御生成式架构。这些能力合并到单个 M3 模型。测试时，MaxProof 将模型用作生成器、验证器、精炼器和排序器，在候选证明群体中搜索并通过锦标赛选择返回最终证明。M3 模型在 IMO 2025 达 35/42，USAMO 2026 达 36/42，均超过人类金牌阈值。
MiniMax-M3用生成-验证器RL把数学证明推到了人类金牌水平，IMO 2025 35/42，USAMO 2026 36/42。这篇的意义不只分数，而在于验证-修复-群体搜索的技术路线跑通了最难的人类竞赛。
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.
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org