{
  "id": 4259,
  "url": "https://arxiv.org/abs/2605.16198v1",
  "title": "Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems",
  "summary": "We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI development lifecycle, from pre-deployment testing to post-deployment auditing. Combining principles from formal methods with SoTA machine learning, we propose techniques that enable AI-enabled product and service developers, as well as third party AI developers and evaluators, to perform offline auditing and online (runtime) monitoring of product-specific (temporally",
  "authors": "Parand A. Alamdari, Toryn Q. Klassen, Sheila A. McIlraith",
  "category": "research",
  "topics": "regulation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-15T17:13:27.000Z",
  "fetched_at": "2026-07-14T16:30:50.574Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4259",
  "original_url": "https://arxiv.org/abs/2605.16198v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}