{
  "id": 9081608,
  "title": "When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting",
  "url": "https://urgent.news/2026/09/21/when-tomorrow-becomes-today-self-evolving-policies-for-agentic-time",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-21T16:33:59.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.24862v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning strategies, and intervention rules inherently time-varying. Consequently, a time series agent must adapt the forecasts it produces and the orchestration policy that determines which components to trust and how to coordinate them. The deployment…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}