3 citations · 5 across the 5 of their papers we have counts for
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cs.AI2026
Do More Agents Help? Evaluating Accuracy and Cost in LLM Multi-Agent Workflows
Yuhang Fu, Ruishan Fang, Jiaqi Shao +5
Choosing whether to replace a single-agent controller requires evidence on both task performance and inference expenditure. We present BenchAgent, a shared evaluation framework, an…
cs.AI2024★ 3 cited
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…
cs.AI2024★ 2 cited
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…