{
  "id": 925,
  "url": "https://arxiv.org/abs/2606.17383v1",
  "title": "Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation",
  "summary": "Agentic artificial intelligence systems introduce a new class of model risk. Unlike traditional predictive models, autonomous agents continuously acquire information, form beliefs regarding latent states of the environment, generate forecasts, select actions, and adapt their behavior over time. Existing validation methodologies focus primarily on predictive accuracy and therefore provide limited insight into the quality of the underlying decision process. This paper proposes a model validation f",
  "authors": "Matthew Francis Dixon",
  "category": "research",
  "topics": "regulation,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-16T00:40:55.000Z",
  "fetched_at": "2026-07-14T14:14:54.532Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/925",
  "original_url": "https://arxiv.org/abs/2606.17383v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}