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
Record details
Published: 27 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Jobs & economy · Agents & autonomy · Environment
Retrieved: 28 July 2026
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ethics.ai (27 July 2026), “SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents,” evidence record 14038, https://ethics.ai/record/14038 (originally published by arXiv cs.AI).
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