{
  "id": 14038,
  "url": "https://arxiv.org/abs/2607.24588v1",
  "title": "SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents",
  "summary": "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",
  "authors": "Hang Ni, Weijia Zhang, Fan Liu, Mengqian Lu, Hao Liu",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T15:53:19.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14038",
  "original_url": "https://arxiv.org/abs/2607.24588v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}