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Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.24588v1

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Plain-Language Brief

Researchers are working on an automated system called SIREN to improve extreme weather early warning systems using Large Language Model (LLM) agents. The system is designed to bridge the gap between isolated scientific tasks and operational early warning processes, which are currently labor-intensive and costly. To test the system, they developed a comprehensive benchmark called SIREN-Bench with 600 question-answer instances across 19 tasks. Evaluation on this benchmark reveals substantial capability gaps in existing weather agent frameworks, leading to the development of SIREN which is inspired by experts' use of historical cases and combines an agentic execution environment with a family of agent harnesses. Experiments show that SIREN outperforms weather-agent baselines.

What the Document Says

  • The study aims to develop an automated end-to-end extreme-weather early warning system using Large Language Model (LLM) agents.
  • SIREN-Bench, a comprehensive benchmark with 600 question-answer instances across 19 tasks, was developed for evaluation.
  • Evaluation on SIREN-Bench reveals substantial capability gaps in existing weather agent frameworks.
  • SIREN was developed as an experience-grounded agent framework inspired by experts' use of historical cases.
  • SIREN combines an agentic execution environment with a family of agent harnesses that exploit historical cases through retrieval, skill distillation, and predictive modeling.
  • Experiments show that SIREN outperforms weather-agent baselines on individual warning procedures and end-to-end warning chains.

Key Concepts Mentioned

  • Large Language Model (LLM) agents
  • Extreme weather early warning
  • SIREN-Bench
  • Historical cases
  • Agentic execution environment
  • Agent harnesses
  • Weather-agent baselines

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.

UNKNOWN

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.