2 citations · 3 across the 4 of their papers we have counts for
5 papers
When Non-Adversarial Noise Hurts Learning: Benchmarking Skill Self-Evolution in Agents
Tao Yu, Zhongtian Luo, Yuchen Long +19
Large language model agents increasingly improve by distilling experience into reusable skills, but errors in that experience can persist in learned rules and affect later tasks. E…
AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent System
Hui Yi Leong, Yuheng Li, Yuqing Wu +4
Although large language models (LLMs) have revolutionized natural language processing capabilities, their practical implementation as autonomous multi-agent systems (MAS) for indus…
DynaSwarm: Dynamically Graph Structure Selection for LLM-based Multi-agent System
Hui Yi Leong, Yuqing Wu
Current multi-agent systems (MAS) frameworks often rely on manually designed and static collaboration graph structures, limiting adaptability and performance. To address these limi…
Combining Induction and Transduction for Abstract Reasoning
Wen-Ding Li, Keya Hu, Carter Larsen +11
When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test…
Robust Detection of LLM-Generated Text: A Comparative Analysis
Yongye Su, Yuqing Wu
The ability of large language models to generate complex texts allows them to be widely integrated into many aspects of life, and their output can quickly fill all network resource…