most citedCut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

MasRouter: Learning to Route LLMs for Multi-Agent Systems

Yanwei Yue, Guibin Zhang, Boyang Liu +4

Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often incur significant costs and face…

cs.CR2025

G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems

Shilong Wang, Guibin Zhang, Miao Yu +5

Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, ranging from collaborative problem-solving to autonom…

cs.LG2025

EvoFlow: Evolving Diverse Agentic Workflows On The Fly

Guibin Zhang, Kaijie Chen, Guancheng Wan +5

The past two years have witnessed the evolution of large language model (LLM)-based multi-agent systems from labor-intensive manual design to partial automation (\textit{e.g.}, pro…

cs.CR20242 cited

LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models

Miao Yu, Junfeng Fang, Yingjie Zhou +4

While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems,…

cs.MA20243 cited

Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems

Guibin Zhang, Yanwei Yue, Zhixun Li +6

Recent advancements in large language model (LLM)-powered agents have shown that collective intelligence can significantly outperform individual capabilities, largely attributed to…