用智能体群自动抓取全球疫情数据，实时发现潜在威胁
ARIES: A Scalable Multi-Agent Orchestration Framework for Real-Time Epidemiological Surveillance and Outbreak Monitoring
- 构建分层智能体架构，自动查询世卫组织、疾控中心等数据源
- 能实时识别新兴传染病信号和数据矛盾，比通用模型更准
- 适合公共卫生机构做疫情预警，也适合作为研究工具
全球健康监测正面临知识断层问题。尽管通用人工智能迅速发展，但因其持续产生幻觉且难以接入专业数据孤岛，仍不适用于高风险的流行病学领域。本文提出ARIES（流行病学监测的代理检索智能），一种专用的自主多智能体框架，旨在突破静态、疾病特定的仪表板局限，构建动态智能生态。该框架采用分层指挥结构，利用GPT协调可扩展的子智能体集群，自主查询世界卫生组织（WHO）、疾病控制与预防中心（CDC）及同行评审论文。通过自动化提取并逻辑整合监测数据，ARIES实现专业化推理，能够近实时识别新兴威胁和信号分歧。其模块化设计证明，任务专用的智能体群可超越通用模型表现，为下一代疫情响应与全球健康情报提供稳健、可扩展的解决方案。
原文摘要 · Abstract (English)
Global health surveillance is currently facing a challenge of Knowledge Gaps. While general-purpose AI has proliferated, it remains fundamentally unsuited for the high-stakes epidemiological domain due to chronic hallucinations and an inability to navigate specialized data silos. This paper introduces ARIES (Agentic Retrieval Intelligence for Epidemiological Surveillance), a specialized, autonomous multi-agent framework designed to move beyond static, disease-specific dashboards toward a dynamic intelligence ecosystem. Built on a hierarchical command structure, ARIES utilizes GPTs to orchestrate a scalable swarm of sub-agents capable of autonomously querying World Health Organization (WHO), Center for Disease Control and Prevention (CDC), and peer-reviewed research papers. By automating the extraction and logical synthesis of surveillance data, ARIES provides a specialized reasoning that identifies emergent threats and signal divergence in near real-time. This modular architecture proves that a task-specific agentic swarm can outperform generic models, offering a robust, extensible for next-generation outbreak response and global health intelligence.
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