Reproduce “SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents” on OpenResearch
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About this paper
Hang Ni, Weijia Zhang, Fan Liu, Mengqian Lu, Hao Liu
Researchers from The Hong Kong University of Science and Technology developed SIREN-Bench, a comprehensive benchmark for end-to-end extreme-weather early warning, and SIREN, an experience-grounded LLM agent framework. SIREN agents, which leverage historical operational experience, consistently improve performance on both individual warning procedures and complex end-to-end warning chains, outperforming existing baselines by up to 32.5% in chained tasks.
Key insights
- Grounding LLM agents in historical operational experience substantially improves their capabilities in complex end-to-end extreme-weather early warning tasks, akin to human expert behavior.
- End-to-end warning chains pose a significantly greater challenge for LLM agents compared to isolated warning procedures, underscoring the complexities of integrating sequential, interdependent tasks.
- Different experience-grounding mechanisms (retrieval-augmented generation, skill distillation, predictive modeling) offer complementary strengths, with SIREN-RAG often performing well overall in chained tasks and SIREN-Modeling excelling in specific prediction and decision-making subtasks.
Summary by alphaXiv.
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