提出一套系统化框架，优化大模型多智能体协作的五个核心维度。
OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration
- 设计双角色算法，分步优化单个协作维度
- 实现多维度联合优化，提升复杂任务表现
- 适用于代码生成、数学推理等高阶应用
由先进大语言模型驱动的智能体在多种复杂应用中展现出卓越能力。近期，多智能体系统（MAS）通过智能体间的协作与通信，在高质量代码生成和算术推理等复杂任务中表现出更强能力。然而，这类系统的开发常依赖手工设计，针对基于大模型的多智能体系统，缺乏系统性设计与优化方法。本文提出 extbf{OMAC}，一个面向大模型多智能体协作的全局优化框架。我们识别出五项关键优化维度，涵盖智能体功能与协作结构。基于这些维度，首先提出一种通用算法，采用语义初始化器与对比比较器两个角色，优化任一单一维度；随后提出跨多个维度的联合优化算法。大量实验表明，OMAC 在多种任务上优于近期方法。
原文摘要 · Abstract (English)
Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recently, Multi-Agent Systems (MAS), wherein multiple agents collaborate and communicate with each other, have exhibited enhanced capabilities in complex tasks, such as high-quality code generation and arithmetic reasoning. However, the development of such systems often relies on handcrafted methods, and the literature on systematic design and optimization of LLM-based MAS remains limited. In this work, we introduce \textbf{OMAC}, a general framework designed for holistic optimization of LLM-based MAS. Specifically, we identify five key optimization dimensions for MAS, encompassing both agent functionality and collaboration structure. Building upon these dimensions, we first propose a general algorithm, utilizing two actors termed the Semantic Initializer and the Contrastive Comparator, to optimize any single dimension. Then, we present an algorithm for joint optimization across multiple dimensions. Extensive experiments demonstrate the superior performance of OMAC on diverse tasks against recent approaches.
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