Research lab

MiniMax

Foundation-model research across language, speech, video, multimodal generation, and agents.

Papers: 4
Citations: 54
Official code: 2

4 papers from MiniMax

Research paper

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

MaxProof is a population-level test-time scaling framework for competition-level mathematical proof, developed for the MiniMax-M3 model series. The M3 model is trained with three proof-oriented capabilities: proof generation via verifier-guided reinforcement learning, proof verification through aligned error finding, and critique-conditioned proof repair.…

Jiacheng Chen, Xinyu Zhang, Shunkai Zhang, Yanmohan Wang, et al.

Published: Jun 2026
Citations: 1
Code: Not linked

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Research paper

MiniMax Sparse Attention

MiniMax Sparse Attention (MSA) is a blockwise sparse attention mechanism built on Grouped Query Attention (GQA) to address the quadratic cost of softmax attention in ultra-long contexts. A lightweight Index Branch scores key-value blocks and selects a Top-k subset per GQA group, while the Main Branch computes exact block-sparse attention over selected…

Xunhao Lai, Weiqi Xu, Yufeng Yang, Qiaorui Chen, et al.

Published: Jun 2026
Citations: 7
Code: 442 stars

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arXiv.org

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

The MiniMax-M2 series is a family of Mixture-of-Experts language models designed to achieve frontier performance with minimal activated parameters. The flagship M2 has 229.9B total parameters but only 9.8B activated per token, using a 62-layer decoder-only Transformer with 256 fine-grained experts, sigmoid gating, full multi-head attention with GQA, a…

Aili Chen, Aonian Li, Baichuan Zhou, Bangwei Gong, et al.

Published: May 2026
Citations: 23
Code: Not linked

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arXiv.org

Towards Scalable Pre-training of Visual Tokenizers for Generation

The paper introduces VTP, a visual tokenizer pre-training framework that integrates image-text contrastive learning, self-supervised learning (MIM and self-distillation), and reconstruction losses to address the 'pre-training scaling problem' in latent diffusion models. The authors argue that reconstruction-only training biases the latent space toward…

Jingfeng Yao, Yuda Song, Yucong Zhou, Xinggang Wang

Published: Dec 2025
Citations: 23
Code: 497 stars

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