SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents· SIREN：基于经验的大模型代理极端天气预警
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 c
基于经验的大模型代理实现端到端的极端天气预警.
- 核心方法
- 构建SIREN-Bench基准测试集，包含19项任务的600个问答实例，评估大模型代理在极端天气预警中的表现.
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有极端天气预警流程依赖专家，成本高且难以规模化，而大模型代理在实际应用中的能力有限.
- 关键实验
- 使用SIREN-Bench基准测试集评估了多个大模型代理的性能，展示了其在不同任务中的效果.
- 主要贡献
- 提出了一个新的综合基准测试集，推动大模型代理在极端天气预警应用中的研究和发展.
- 意义与局限
- 为自动化极端天气预警提供了新的研究方向和工具，有助于降低预警成本和提高效率，但目前仍存在实际应用中的局限性.