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cs.AI2026
WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning
Zelai Xu, Zhexuan Xu, Ruize Zhang +7
Recent advancements in Large Language Models (LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use.…
cs.AI2025
Adapting Like Humans: A Metacognitive Agent with Test-time Reasoning
Yang Li, Zhiyuan He, Yuxuan Huang +5
Recent Vision-Language Models (VLMs) exhibit strong perceptual reasoning abilities, yet they often struggle to adapt efficiently when encountering novel tasks at test time. In cont…
cs.AI2025
A Survey on Self-play Methods in Reinforcement Learning
Ruize Zhang, Zelai Xu, Chengdong Ma +8
Self-play, a learning paradigm where agents iteratively refine their policies by interacting with historical or concurrent versions of themselves or other evolving agents, has show…