arXiv AI By Jaewon Chu, Jinwoo Seo, Jaewon Cho, Jeehye Na, Yunyang Xiong, Youngdae Kim, Hyunwoo J. Kim

AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

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arXiv AI
5d ago

Rethinking Multi-Agent Collaboration: When More Is Less

The paper examines when multi‑agent collaboration is beneficial versus single‑agent approaches. It finds that collaboration yields systematic advantages mainly in long‑horizon tasks with sparse dependencies, while single agents perform better in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight multi‑agent mechanism that models collaboration as a dynamically evolving graph, and show that it balances context efficiency and task performance without always improving outcomes as more agents are added.

By Yishuo Yuan, Yibo Wu, Yihan Zhang, Minyuan Sun, Shenliang Li, Xinkai Ma, Yifan Li, Jiaheng Liu
Hugging Face Trending Papers
5d ago

Rethinking Multi-Agent Collaboration: When More Is Less

The paper examines when multi‑agent collaboration is truly beneficial as large language models grow more capable. It finds that multi‑agent systems yield systematic advantages mainly for long‑horizon tasks with sparse dependencies, while single‑agent approaches excel in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight, graph‑based collaboration framework that balances context efficiency and performance, demonstrating that adding more agents or deeper recursion does not always improve outcomes.