1 citations · 1 across the 5 of their papers we have counts for
6 papers
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
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…
Feedback Control for Multi-Objective Graph Self-Supervision
Karish Grover, Theodore Vasiloudis, Han Xie +3
Can multi-task self-supervised learning on graphs be coordinated without the usual tug-of-war between objectives? Graph self-supervised learning (SSL) offers a growing toolbox of p…
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
MiniMax, :, Aili Chen +125
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combin…
Deal: Distributed End-to-End GNN Inference for All Nodes
Shiyang Chen, Xiang Song, Vasiloudis Theodore +1
Graph Neural Networks (GNNs) are a new research frontier with various applications and successes. The end-to-end inference for all nodes, is common for GNN embedding models, which…
MiniMax-01: Scaling Foundation Models with Lightning Attention
MiniMax, Aonian Li, Bangwei Gong +87
We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer conte…
Spin: An Efficient Secure Computation Framework with GPU Acceleration
Wuxuan Jiang, Xiangjun Song, Shenbai Hong +5
Accuracy and efficiency remain challenges for multi-party computation (MPC) frameworks. Spin is a GPU-accelerated MPC framework that supports multiple computation parties and a dis…