OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration
A new framework called OMAC is proposed for optimizing large language model (LLM)-based multi-agent systems across five key dimensions, including agent functionality and collaboration structure. OMAC introduces two actors—the Semantic Initializer and Contrastive Comparator—for optimizing individual dimensions, as well as an algorithm for joint multi-dimension optimization. Experimental results indicate that OMAC outperforms recent methods on a range of tasks.
Why it matters: This work offers a systematic approach to designing and optimizing multi-agent LLM systems, addressing the limitations of ad hoc, handcrafted methods.
Full story at: arXiv Multiagent Systems ↗