arXiv cs.AI· Yinzhu Quan, Zefang Liu, Frederick Benaben, Benoit Montreuil·· 3 天前AI 评分20
AutoMARS：面向供应链风险评估的自动可扩展多智能体 LLM 框架
AutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessment
AI 导读
AutoMARS 是一个面向供应链网络风险对比评估的智能体框架，可自动规划任务、协调专用智能体、调用外部分析工具，并将结果整合为综合风险评估。该框架采用多领域、多智能体、多工具架构，融合社会经济、灾害、环境与交通等异构风险因素，并通过并行化独立分析任务实现多地点风险的规模化对比。
正文 · 原文
Abstract:We propose AutoMARS (Automatic Multi-Agent Risk Assessment), an agentic framework for automatic and scalable comparative risk assessment in supply chain networks. The framework enables end-to-end execution of analytical workflows by automatically planning tasks, coordinating specialized agents, invoking external analytical tools, and synthesizing domain-specific results into integrated risk evaluations. AutoMARS further supports scalable risk comparison across multiple locations by parallelizing independent analytical tasks. The framework adopts a multi-domain, multi-agent, and multi-tool architecture that integrates heterogeneous risk factors from socioeconomic, disaster, environmental, and transportation domains. Through coordinated agent interactions and tool integration, AutoMARS enables structured, systematic, and interpretable comparative analysis to support supply chain risk-aware location evaluation.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17206 [cs.AI] |
| (or arXiv:2610.17206v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17206 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yinzhu Quan [view email]
[v1]
Mon, 12 Oct 2026 17:59:40 UTC (15,129 KB)
来源：arXiv cs.AI · arxiv.org