基于大语言模型的多智能体系统：对新兴设计模式与范式的实证评估与洞察
LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms
AI总结：
本文通过实证评估，探讨了基于大语言模型的多智能体系统在不同领域中的应用效果，揭示了新兴设计模式与范式的优劣及改进方向。
摘要：
本文系统梳理了大语言模型（LLM）赋能的多智能体系统（MAS）中新兴的设计模式和范式文献，评估了这些模式在不同领域中的实际效用。我们定义了关键的架构组件，包括智能体编排、通信机制和控制流策略，并展示了这些组件如何加速模块化、领域自适应解决方案的开发。三个现实世界案例研究在电信安全、国家文化遗产资产管理以及公用事业客户服务自动化领域进行了受控的容器化试点测试。初步实证结果表明，对于这些案例研究，原型在两周内完成，试点就绪的解决方案在一个月内完成，表明与传统方法相比开发开销有所降低，用户可访问性得到提升。然而，研究结果也强化了文献中记录的局限性，包括LLM行为的可变性导致从原型到生产成熟度的过渡面临挑战。我们最后概述了改进MAS架构可靠性、可扩展性和治理的关键研究方向，以及进一步工作以成熟MAS设计模式以缓解固有挑战的必要性。
英文摘要：
This paper formalises the literature on emerging design patterns and paradigms for Large Language Model (LLM)-enabled multi-agent systems (MAS), evaluating their practical utility across various domains. We define key architectural components, including agent orchestration, communication mechanisms, and control-flow strategies, and demonstrate how these enable rapid development of modular, domain-adaptive solutions. Three real-world case studies are tested in controlled, containerised pilots in telecommunications security, national heritage asset management, and utilities customer service automation. Initial empirical results show that, for these case studies, prototypes were delivered within two weeks and pilot-ready solutions within one month, suggesting reduced development overhead compared to conventional approaches and improved user accessibility. However, findings also reinforce limitations documented in the literature, including variability in LLM behaviour that leads to challenges in transitioning from prototype to production maturity. We conclude by outlining critical research directions for improving reliability, scalability, and governance in MAS architectures and the further work needed to mature MAS design patterns to mitigate the inherent challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
