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今天的主题是：Multi-agent Architecture Search via Agentic Supernet

Summary

The provided text introduces MaAS, a novel framework for automating the design of multi-agent systems powered by Large Language Models. Instead of searching for a single optimal architecture, MaAS optimizes an agentic supernet, which is a probabilistic distribution of various agent configurations. This approach allows MaAS to dynamically sample query-dependent agent systems, tailoring resource allocation and achieving high performance with significantly reduced inference costs compared to existing methods. Evaluations across multiple benchmarks demonstrate MaAS's effectiveness, resource efficiency, and transferability.

本文介绍了 MaAS，一个创新框架，旨在自动化设计由大语言模型驱动的多智能体系统。与寻找单一最优架构不同，MaAS 优化了一个代理超网（agentic supernet），这是一个不同代理配置的概率分布。这种方法使得 MaAS 能够动态地根据查询需求抽样代理系统，从而定制资源分配，并在显著降低推理成本的同时实现高性能。多个基准测试的评估表明，MaAS 在效果、资源效率和可迁移性方面表现出色。

原文链接：https://arxiv.org/abs/2502.04180