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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Hang Ni, Weijia Zhang, Fan Liu, Mengqian Lu, Hao Liu
• Venue: arXiv: cs.AI
• Published: 2026-07-27
• Collected: 2026-07-28
• URL: http://arxiv.org/abs/2607.24588v1
• PDF: https://arxiv.org/pdf/2607.24588v1
• DOI: UNKNOWN

Plain-Language Brief

The study presents SIREN, a framework for automating end-to-end extreme-weather early warning using Large Language Model (LLM) agents. The researchers developed SIREN-Bench, a comprehensive benchmark, and found existing weather agent frameworks to have substantial capability gaps. To address this, they created SIREN which utilizes historical cases, integrates heterogeneous weather evidence, and outperforms other weather-agent baselines.

What the Document Says

• The research focuses on automating end-to-end extreme-weather early warning using LLM agents.
• SIREN-Bench was developed as a comprehensive benchmark for the evaluation of the system.
• Evaluation on SIREN-Bench revealed substantial capability gaps in existing weather agent frameworks.
• SIREN was created to address these gaps by utilizing historical cases and integrating heterogeneous weather evidence.

Key Concepts Mentioned

• Large Language Model (LLM) agents
• Extreme-weather early warning
• Historical cases
• SIREN-Bench

Claims Made by the Document

• Existing weather agent frameworks have substantial capability gaps.
• SIREN outperforms weather-agent baselines.

Evidence Used

• Evaluation on SIREN-Bench reveals substantial capability gaps in existing weather agent frameworks.
• Extensive experiments demonstrate that SIREN outperforms weather-agent baselines.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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.