综述多智能体大模型如何通过协作提升问题解决能力
Literature Review Of Multi-Agent Debate For Problem-Solving
- 对比分析智能体角色、沟通结构与决策机制的设计方案
- 指出多智能体系统在复杂任务上表现优于单智能体，但计算开销更高
- 适合关注AI协作框架与系统设计的研究者参考
多智能体大语言模型（MA-LLMs）是快速发展的研究方向，通过多个交互式语言智能体协同处理复杂任务，性能超越单智能体大模型。本文综述了智能体角色、通信结构与决策机制的最新研究，结合传统多智能体系统与前沿MA-LLM成果，填补该领域缺乏直接比较的空白。研究表明，可扩展性、通信架构与决策流程显著影响MA-LLM表现。尽管多智能体方法能取得更优结果，但面临计算成本上升及独特挑战未充分探索等问题。综述为研究人员和实践者提供了构建稳健高效多智能体AI系统的路线图。
原文摘要 · Abstract (English)
Multi-agent large language models (MA-LLMs) are a rapidly growing research area that leverages multiple interacting language agents to tackle complex tasks, outperforming single-agent large language models. This literature review synthesizes the latest research on agent profiles, communication structures, and decision-making processes, drawing insights from both traditional multi-agent systems and state-of-the-art MA-LLM studies. In doing so, it aims to address the lack of direct comparisons in the field, illustrating how factors like scalability, communication structure, and decision-making processes influence MA-LLM performance. By examining frequent practices and outlining current challenges, the review reveals that multi-agent approaches can yield superior results but also face elevated computational costs and under-explored challenges unique to MA-LLM. Overall, these findings provide researchers and practitioners with a roadmap for developing robust and efficient multi-agent AI solutions.
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