3 papers
cs.AI2026
Finding Where the Buck Stops: An Automated Failure Attribution-Based Reflection Framework for Multi-Agent Collaboration
Xiaoqing Wang, Keman Huang, Bin Liang +3
Multi-agent systems (MAS) powered by large language models have shown promise for complex tasks but suffer from high failure rates. Current self-reflection methods for MAS require…
cs.MA2025
AgentODRL: A Large Language Model-based Multi-agent System for ODRL Generation
Wanle Zhong, Keman Huang, Xiaoyong Du
The Open Digital Rights Language (ODRL) is a pivotal standard for automating data rights management. However, the inherent logical complexity of authorization policies, combined wi…
cs.CR2025
Shadows in the Code: Exploring the Risks and Defenses of LLM-based Multi-Agent Software Development Systems
Xiaoqing Wang, Keman Huang, Bin Liang +2
The rapid advancement of Large Language Model (LLM)-driven multi-agent systems has significantly streamlined software developing tasks, enabling users with little technical experti…