1 citations · 2 across the 7 of their papers we have counts for
7 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…
OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding
Deming Ding, Shichun Liu, Enhui Yang +12
Modern coding scaffolds turn LLMs into capable software agents, but their ability to follow scaffold-specified instructions remains under-examined, especially when constraints are…
Automated Healthcare Thematic Analysis using Multi-Agent Large Language Model: Algorithm Development and Evaluation
Qidi Xu, Nuzha Amjad, Grace Giles +5
Understanding patients experiences is essential for advancing patient-centered care. Qualitative thematic analysis is widely used to explore these experiences, however, the process…
WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents
Junteng Liu, Yunji Li, Chi Zhang +12
The paradigm of Large Language Models (LLMs) has increasingly shifted toward agentic applications, where web browsing capabilities are fundamental for retrieving information from d…
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…
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…
Hybrid Interest Modeling for Long-tailed Users
Lifang Deng, Jin Niu, Angulia Yang +4
User behavior modeling is a key technique for recommender systems. However, most methods focus on head users with large-scale interactions and hence suffer from data sparsity issue…