{
  "id": 1403383,
  "title": "Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations",
  "url": "https://urgent.news/2026/08/14/knowing-when-to-stop-bayesian-optimal-stopping-for-llm-evaluations",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-14T16:06:41.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.14425v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a precision-based adaptive stopping framework that treats evaluation as a sequential measurement problem: keep sampling where uncertainty remains high, and stop where estimates are precise or stable enough. The framework builds on hierarchical…",
  "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."
}