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20242026
most citedLLM-Empowered State Representation for Reinforcement Learning

1 citations · 1 across the 8 of their papers we have counts for

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cs.MA2026

OptiMAS: Automatically Optimize Multi-Agent System

Yuxin Cheng, Chang Liu, Hanxin Yu +9

Automated evolution of Multi-Agent Systems (MAS) holds significant potential for reducing the manual effort required to design and optimize LLM-based agent architectures. However,…

cs.AI2026

Wuying-Browser-Agent: Real-World Centric Fundamental Long-Horizon Browser Agents

AIMAE Team, Tianxiang Chen, Yan Cheng +39

Browser agents perform well on short, clean demonstrations, but real deployment is fundamentally different: agents must sustain dozens of decisions on live websites while recoverin…

cs.AI2026

SkillShapley: Boundary-Adaptive Shapley Valuation for Skill Step Attribution in LLM Agents

Chang Liu, Yuqi Zhang, Yiman Zhong +3

Agent skills are crucial external instructions that enable language agents to execute long procedural tasks such as coding or document processing. Existing agent skills are primari…

cs.MA2026

ATOM: Instantiating Budget-Controllable Multi-Agent Collaboration via Nucleus-Electron Hierarchy

Xinkui Zhao, Sai Liu, Yifan Zhang +6

Large Language Model (LLM)-based multi-agent systems rely on optimized collaboration topologies to balance performance and communication costs. However, current methods struggle wi…

cs.AI2026

Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training

Yangyi Fang, Jiaye Lin, Xiaoliang Fu +4

Multi-turn LLM agents are becoming pivotal to production systems, spanning customer service automation, e-commerce assistance, and interactive task management, where accurately dis…