arXiv：cs.AI· Yinzhu Quan, Zefang Liu, Frederick Benaben, Benoit Montreuil·· 6 小时前
AutoMARS：面向供应链风险评估的自动可扩展多智能体大语言模型框架
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的智能体框架，可自动规划任务、协调专用智能体并调用外部分析工具，将社会经济、灾害、环境与运输等多域风险因素整合为可解释的比较性风险评估。该框架通过并行化独立分析任务，支持跨多个地点的可扩展风险对比。论文题为 arXiv:2610.17206，属于 cs.AI 分类。
正文
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