5 papers
Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent Workflows
Yuhang Fu, Ruishan Fang, Jiaqi Shao +4
Does adding more agents help an LLM workflow once compared systems share the same benchmark loader, tool access, answer contract, usage accounting, and trajectory logging? We intro…
MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration
Siyuan Lu, Jiaqi Shao, Bing Luo +1
Large Language Model (LLM) based multi-agent systems (MAS) have shown promise in tackling complex tasks, but often rely on predefined roles and centralized coordination, limiting t…
Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives
Jiaqi Shao, Tao Lin, Xiaojin Zhang +2
Federated Unlearning (FU) enables the removal of specific clients' data influence from trained models. However, in non-IID settings, removing clients creates critical side effects:…
Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation
Jiaqi Shao, Tianjun Yuan, Tao Lin +1
Cognitive abilities, such as Theory of Mind (ToM), play a vital role in facilitating cooperation in human social interactions. However, our study reveals that agents with higher To…
Federated Unlearning: a Perspective of Stability and Fairness
Jiaqi Shao, Tao Lin, Xuanyu Cao +1
This paper explores the multifaceted consequences of federated unlearning (FU) with data heterogeneity. We introduce key metrics for FU assessment, concentrating on verification, g…