用笔记和协调器提升多智能体旅行规划的准确性
Analyzing Information Sharing and Coordination in Multi-Agent Planning
- 引入笔记共享信息，减少幻觉错误
- 协调器使多智能体聚焦关键子任务，错误率再降13.5%
- 组合使用后通过率提升至25%，优于单智能体基准
多智能体系统（MAS）已拓展大语言模型（LLM）在网页研究与软件工程等领域的应用边界。然而，长周期、多约束规划任务需依赖详细信息并满足复杂互依赖约束，对系统构成挑战。本研究构建了一个基于LLM的旅行规划多智能体系统，作为典型范例。我们评估了笔记机制在促进信息共享中的作用，以及协调器在自由对话中提升协作的效果。结果显示，笔记使因幻觉产生的错误减少18%；协调器可引导系统聚焦特定子区域，进一步降低错误达13.5%。两者结合后，在TravelPlanner基准上实现25%的最终通过率，较单智能体基线7.5%有17.5个百分点的绝对提升。结果表明，结构化信息共享与反思式协调是长周期规划中基于LLM的多智能体系统的关键组件。
原文摘要 · Abstract (English)
Multi-agent systems (MASs) have pushed the boundaries of large language model (LLM) agents in domains such as web research and software engineering. However, long-horizon, multi-constraint planning tasks involve conditioning on detailed information and satisfying complex interdependent constraints, which can pose a challenge for these systems. In this study, we construct an LLM-based MAS for a travel planning task which is representative of these challenges. We evaluate the impact of a notebook to facilitate information sharing, and evaluate an orchestrator agent to improve coordination in free form conversation between agents. We find that the notebook reduces errors due to hallucinated details by 18%, while an orchestrator directs the MAS to focus on and further reduce errors by up to 13.5% within focused sub-areas. Combining both mechanisms achieves a 25% final pass rate on the TravelPlanner benchmark, a 17.5% absolute improvement over the single-agent baseline's 7.5% pass rate. These results highlight the potential of structured information sharing and reflective orchestration as key components in MASs for long horizon planning with LLMs.
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