2605.26494v1 May 26, 2026 cs.AI

MiniMax-M2 시리즈: 미니 활성화를 통해 극대화된 실질적인 지능

The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

Ziyun Zhang

Citations: 0

h-index: 0

Junjie Yan

Citations: 2,333

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Qihan Ren

Citations: 382

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Yulin Hu

Citations: 257

h-index: 10

Wei Cheng

Citations: 601

h-index: 13

Wenqian Zhao

Citations: 6

h-index: 2

Aonian Li

Citations: 328

h-index: 2

Wei Liu

Citations: 26

h-index: 3

Chenyang Zhang

Tongji University

Citations: 22

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Ruize Zhang

Tsinghua University

Citations: 101

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Yehui Tang

Citations: 18

h-index: 3

Chang Yu

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h-index: 3

Zhi Zhang

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Ling Yang

Citations: 3,474

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Ming Li

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Mingyuan Chi

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Yu Zhang

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Zhaochen Su

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Jingbo Zhu

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Tian Xie

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Deming Ding

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Chengjun Xiao

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Qidi Xu

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Binyan Jiang

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Chao Wang

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Chengyi Yang

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Zhanpeng Zhou

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Keyu Duan

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Chengyu Du

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Junteng Liu

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Pengyu Zhao

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Weitao Li

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Yusen Zhang

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Baichuan Zhou

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Haonan Lu

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Jian Zhang

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Hao Zhang

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Chi Zhang

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Zijian Zhou

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Yubo Wang

University of Waterloo

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Junxian He

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Yutong Zhang

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Tengfei Li

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Yang Xu

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Yang Wang

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Qibing Ren

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Chen Zhu

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MiniMax Aili Chen

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Bangwei Gong

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Chengzhi Zhong

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Chuang Huang

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Chunhui Du

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Chunyu Zhao

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Cong Guo

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Daocheng Chen

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Dianjun Sun

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Enhui Yang

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F. Yu

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Guangxuan Zheng

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Guodong Zheng

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Guohong Li

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Haichao Zhu

Harbin Institute of Technology

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Han Ding

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Hao Lyu

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Huajie Shi

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Jiacheng Chen

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Jiaqi Zhuang

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Jia Song

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Jihao Gu

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Jing Zhuang

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Jun Ruan

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Jun-Feng Xu

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Kang Xu

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Ke Ji

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Ke Yang

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Kecheng Xiao

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Letian Ruan

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Lin Yuan

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Lijie Mo

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Lin Bao

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Loki

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Lu-Chuan Ceng

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Ming Zhong

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Ming Tao

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Mu-Han Lin

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Nan Hu

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Ningxin Chen

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Peiyin Zhu

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Peng Gao

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Qin Wang

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Quan Chen

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Qunhong Ceng

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Rong Tian

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Rui Dong

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Ruitao Leng

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Sheng Jia

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Shun Yao

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Shu Yu

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Sicheng Pan

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Song Zhu

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Tian Qin

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Tianrun Liang

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Xihao Song

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Xinyu Guan

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Yuanxiang Ying

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Yu Miao

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Yu Liao

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Ziyu Ge

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Shu Zhao

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J. Dong

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Xiaohan Su

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Jinhao Tian

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Jinli Liu

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Jun Tao

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Jun Zhang

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Ke Li

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Le Han

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Lianfei Yu

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Li Feng

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Lin Li

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Lin N. Zhou

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본 논문에서는 MiniMax-M2 시리즈를 소개합니다. 이 시리즈는 미니 활성화가 최대의 실질적인 지능을 발휘할 수 있다는 원칙에 기반한 Mixture-of-Experts 언어 모델 제품군입니다. 플래그십 모델인 M2는 총 2299억 개의 파라미터를 가지고 있지만, 토큰당 활성화되는 파라미터는 98억 개뿐입니다. 에이전트 배포를 위해 설계된 M2 시리즈는 다음과 같은 세 가지 구성 요소로 이루어져 있습니다. (i) 에이전트 기반 데이터 파이프라인은 실행 가능한 작업 공간과 아티팩트 연계 보상을 기반으로, 대규모의 검증 가능 트레이저리를 생성하며, 여기에는 에이전트 코딩 및 에이전트 협업이 포함됩니다. (ii) Forge는 확장 가능한 에이전트 내재형 강화 학습 시스템으로, 장기적인 에이전트 트레이저리에 적응하며, 윈도우 기반 FIFO 스케줄링, 프리픽스 트리 병합, 추론 최적화 및 깨끗한 학습-추론-에이전트 분리를 통해 화이트 박스 및 블랙 박스 에이전트를 모두 지원합니다. (iii) 최신 M2.7 체크포인트는 자체 진화를 위한 초기 단계를 보여주며, 훈련 실행을 자율적으로 디버깅하고 자체 구조를 수정합니다. M2부터 M2.7까지, 이러한 조합은 미니 활성화의 특성을 활용하여 에이전트 코딩, 심층 검색, 사무 작업 및 추론 벤치마크에서 최첨단 성능을 달성합니다.

Original Abstract

We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The flagship M2 contains 229.9B total parameters with only 9.8B activated per token. Designed end-to-end for agentic deployment, the M2 series rests on three components: (i) agent-driven data pipelines producing large-scale, verifiable trajectories across agentic coding and agentic cowork, each grounded in an executable workspace and an artifact-aligned reward; (ii) Forge, a scalable agent-native RL system that adapts to long-horizon agent trajectories, paired with windowed-FIFO scheduling, prefix-tree merging, inference optimization, and a clean training-inference-agent decoupling that supports both white-box and black-box agents; (iii) the latest M2.7 checkpoint takes an early step toward self-evolution -- autonomously debugging training runs and modifying its own scaffold. Across M2 through M2.7, this combination translates a mini-activation footprint into frontier-tier performance on agentic coding, deep search, office-task, and reasoning benchmarks.

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