Showing cs.AIShow all
3 papers · 1 filter
cs.AI2026
The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
Ningxin Pan, Hanyu Li, Yeqing Shen +3
Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an otherwise correct action ma…
cs.AI2026
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…
cs.AI2026
To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models
Haoqing Wang, Xiang Long, Ziheng Li +3
Reinforcement Learning with Verifiable Rewards (RLVR) plays a key role in stimulating the explicit reasoning capability of Large Language Models (LLMs). We can achieve expert-level…