想了解LLM智能体在极端天气预警中怎么用？这篇论文建了个600题的基准，还搞了个历史案例驱动的框架，比现有方案强不少。
SIREN-Bench基准包含600个问答实例、19个任务，覆盖4个独立预警流程和1个端到端预警链。现有天气智能体框架在SIREN-Bench上表现出显著能力差距。研究者提出SIREN，一种结合历史案例检索、技能蒸馏和预测建模的经验驱动智能体框架。实验显示SIREN在独立预警流程和端到端预警链上均超越基线。该框架利用异构天气证据和工具集成环境，提升预警自动化水平。
SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents
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.