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arXiv · 2607.24588

SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents

Hang Ni·Weijia Zhang·Fan Liu·Mengqian Lu·Hao Liu
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Abstract

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.

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Hang Ni, Weijia Zhang, Fan Liu, Mengqian Lu, Hao Liu. 2026-07-27. SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents. https://arxiv.org/abs/2607.24588

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