发表机构
The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology; The World Sustainable Development Institute（香港科技大学（广州）; 香港科技大学; 世界可持续发展研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究借助大语言模型智能体实现端到端极端天气预警。开发了包含多任务问答实例的SIREN-Bench基准，发现现有框架差距后，构建基于经验的SIREN框架，结合多种技术，实验证明其在预警程序和预警链上优于基线。
AI 中文摘要
极端天气预警对于减轻恶劣天气事件带来的社会、经济和环境风险至关重要。然而，以专家为中心的预警工作流程成本高昂、劳动密集且在整个预警到行动过程中难以扩展。尽管大语言模型智能体的最新进展实现了与天气相关任务的自动化，但现有研究仍集中在孤立的科学任务上，忽视了极端天气预警业务所需的相互依存流程链。为弥补这一差距，本研究通过大语言模型智能体研究自动化的端到端极端天气预警。首先开发了SIREN-Bench，这是一个全面的基准，包含19项任务中的600个问答实例，涵盖四个单独的预警程序和一个端到端预警链。对SIREN-Bench的评估揭示了现有天气智能体框架中的重大能力差距。这促使我们开发SIREN，一个受专家使用历史案例启发的基于经验的智能体框架，它将整合异构天气证据和工具的智能执行环境与一系列通过检索、技能提炼和预测建模利用历史案例的智能体 harnesses 相结合。大量实验表明，SIREN在单独的预警程序和端到端预警链上均优于天气智能体基线。
英文摘要
Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events. However, expert-centered warning workflows are costly, labor-intensive, and difficult to scale throughout the warning-to-action process. Although recent advances in Large Language Model (LLM) agents have enabled the automation of weather-related tasks, existing studies remain centered on isolated scientific tasks and overlook the chain of interdependent processes required for operational extreme-weather early warning. To bridge this gap, this study investigates automated end-to-end extreme-weather early warning through LLM agents. We first develop SIREN-Bench, a comprehensive benchmark comprising 600 question-answer instances across 19 tasks, and covering four individual warning procedures and an end-to-end warning chain. Evaluation on SIREN-Bench reveals substantial capability gaps in existing weather agent frameworks. This motivates us to develop SIREN, an experience-grounded agent framework inspired by experts' use of historical cases, which combines an agentic execution environment integrating heterogeneous weather evidence and tools with a family of agent harnesses that exploit historical cases through retrieval, skill distillation, and predictive modeling. Extensive experiments demonstrate that SIREN outperforms weather-agent baselines on both individual warning procedures and end-to-end warning chains.
