{
  "id": 14430,
  "url": "https://arxiv.org/abs/2607.25877v1",
  "title": "Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks",
  "summary": "This paper investigates how multi-agent systems (MAS)-based on large language models (LLMs) can support actuarial risk modelling, with a particular focus on uncertainty quantification. Actuarial workflows represent a high-stakes decision-support setting where unreliable outputs may lead to incorrect risk assessment, unfair pricing, and regulatory non-compliance. To address uncertainty introduced by the probabilistic nature of LLMs and dependencies between agents, a multi-agent framework is propo",
  "authors": "Bart Custers, Koorosh Aslansefat",
  "category": "research",
  "topics": "regulation,agents-autonomy,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T15:39:43.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "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/14430",
  "original_url": "https://arxiv.org/abs/2607.25877v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}