Researchers have developed SIREN-Bench, a new benchmark designed to evaluate Large Language Model (LLM) agents for extreme weather early warning systems. The benchmark includes 600 question-answer instances across 19 tasks, covering both individual warning procedures and an end-to-end warning chain. Existing weather agent frameworks show significant capability gaps when tested against SIREN-Bench. To address this, the study introduces SIREN, an experience-grounded agent framework that leverages historical weather cases through retrieval, skill distillation, and predictive modeling, demonstrating superior performance over baseline agents. AI
IMPACT This research could lead to more robust and scalable automated systems for predicting and warning about extreme weather events.
RANK_REASON The cluster describes a new academic paper introducing a benchmark and a framework for LLM agents in extreme weather early warning. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- Large Language Model
- LLM agents
- ScienceCast
- SIREN-Bench
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
