Abstract
Multi-agent systems powered by large language models have demonstrated remarkable capabilities across diverse domains, yet existing automated design approaches seek monolithic solutions that fail to adapt resource allocation based on query complexity and domain requirements. This paper introduces AutoMaAS, a self-evolving multi-agent architecture search framework that leverages neural architecture search principles to automatically discover optimal agent configurations through dynamic operator lifecycle management and automated machine learning techniques. Our approach incorporates four key innovations: (1) automatic operator generation, fusion, and elimination based on performance-cost analysis, (2) dynamic cost-aware optimization with real-time parameter adjustment, (3) online feedback integration for continuous architecture refinement, and (4) enhanced interpretability through decision tracing mechanisms. Extensive experiments across six benchmarks demonstrate that AutoMaAS achieves 1.0-7.1\% performance improvement while reducing inference costs by 3-5\% compared to state-of-the-art methods. The framework shows superior transferability across datasets and LLM backbones, establishing a new paradigm for automated multi-agent system design in the era of large language models.
Abstract (translated)
由大型语言模型驱动的多代理系统在多个领域展示了显著的能力，但现有的自动化设计方法寻求的是单一解决方案，无法根据查询复杂度和特定领域的需要来调整资源分配。本文介绍了AutoMaAS，这是一个自我演化的多代理架构搜索框架，利用神经结构搜索原则通过动态操作生命周期管理和自动机器学习技术自动发现最优的代理配置。 我们的方法包含四个关键创新点： 1. **基于性能-成本分析的自动化操作生成、融合和消除**：这种方法可以优化多个代理之间的工作流程。 2. **实时参数调整的成本意识型动态优化**：根据查询需求，系统能够灵活地调整其运行策略以适应各种场景。 3. **在线反馈整合用于持续架构改进**：通过用户或环境的即时反馈，框架能够在使用过程中不断优化和增强自身性能。 4. **决策追踪机制提升可解释性**：这种方法使得复杂且自动化的架构搜索过程变得更加透明。 在六个基准测试中的广泛实验表明，与当前最佳方法相比，AutoMaAS能够实现1.0%-7.1%的性能改进，并将推理成本降低3-5%。该框架显示出了跨数据集和大型语言模型骨干网络的良好迁移能力，在大规模语言模型时代为自动化多代理系统设计确立了新的范式。 通过这些创新点，AutoMaAS不仅提高了多代理系统的效率和效果，还增强了它们的适应性和可解释性，从而推动了这一领域的发展。
URL
https://arxiv.org/abs/2510.02669
