用多智能体自动解决快递末端配送中断问题
Project Synapse: A Hierarchical Multi-Agent Framework with Hybrid Memory for Autonomous Resolution of Last-Mile Delivery Disruptions
- 分层架构：总控智能体分解任务，专业执行智能体处理细节
- 基于6000条用户评论构建30个复杂故障场景数据集
- 采用大模型评判并消除偏见，确保评估客观性
本文提出Project Synapse，一种用于自主解决末端配送中断的新型智能体框架。该框架采用分层多智能体结构，由中央调度智能体进行战略任务分解，并将子任务委派给负责战术执行的专业工作智能体。系统通过LangGraph管理复杂的循环工作流。为验证框架性能，研究者基于对超过6000条真实用户评论的定性分析，构建了一个包含30个复杂中断场景的基准数据集。系统表现通过大模型作为裁判的评估协议进行衡量，并采取显式偏见缓解措施以保证评估公正性。
原文摘要 · Abstract (English)
This paper introduces Project Synapse, a novel agentic framework designed for the autonomous resolution of last-mile delivery disruptions. Synapse employs a hierarchical multi-agent architecture in which a central Resolution Supervisor agent performs strategic task decomposition and delegates subtasks to specialized worker agents responsible for tactical execution. The system is orchestrated using LangGraph to manage complex and cyclical workflows. To validate the framework, a benchmark dataset of 30 complex disruption scenarios was curated from a qualitative analysis of over 6,000 real-world user reviews. System performance is evaluated using an LLM-as-a-Judge protocol with explicit bias mitigation.
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