SIREN: LLM Agents for End-to-End Extreme-Weather Early Warning
A new study introduces SIREN, a framework using Large Language Model (LLM) agents for automated end-to-end extreme-weather early warning. The researchers developed SIREN-Bench, a benchmark of 600 question-answer instances across 19 tasks covering four individual warning procedures and a full warning chain. Evaluation on SIREN-Bench reveals significant capability gaps in existing LLM agents for operational warning tasks. The work aims to address the high cost and labor intensity of expert-centered warning workflows by automating the chain of interdependent processes from data analysis to action recommendations. The study is published on arXiv.
Key facts
- SIREN is a framework for automated end-to-end extreme-weather early warning using LLM agents.
- SIREN-Bench comprises 600 question-answer instances across 19 tasks.
- The benchmark covers four individual warning procedures and an end-to-end warning chain.
- Evaluation reveals substantial capability gaps in existing LLM agents.
- The study aims to scale warning-to-action processes by reducing reliance on expert labor.
- Published on arXiv with ID 2607.24588.
- The work addresses the chain of interdependent processes required for operational early warning.
- Existing studies focus on isolated scientific tasks, not end-to-end warning.
Entities
Institutions
- arXiv