9 citations · 9 across the 3 of their papers we have counts for
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.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…
cs.CR2024★ 9 cited
Practically implementing an LLM-supported collaborative vulnerability remediation process: a team-based approach
Xiaoqing Wang, Yuanjing Tian, Keman Huang +1
Incorporating LLM into cybersecurity operations, a typical real-world high-stakes task, is critical but non-trivial in practice. Using cybersecurity as the study context, we conduc…