{
  "id": 5919,
  "url": "https://arxiv.org/abs/2604.12005v1",
  "title": "BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH",
  "summary": "Bayesian optimization (BO) has for sequential optimization of expensive black-box functions demonstrated practicality and effectiveness in many real-world settings. Meta-Bayesian optimization (meta-BO) focuses on improving the sample efficiency of BO by making use of information from related tasks. Although meta-BO is sample-efficient when task structure transfers, poor alignment between meta-training and test tasks can cause suboptimal queries to be suggested during online optimization. To this",
  "authors": "Rahman Ejaz, Varchas Gopalaswamy, Ricardo Luna, Aarne Lees, Vineet Gundecha, Christopher Kanan et al.",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": "meta",
  "regions": null,
  "published_at": "2026-04-13T19:52:08.000Z",
  "fetched_at": "2026-07-14T16:32:06.468Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5919",
  "original_url": "https://arxiv.org/abs/2604.12005v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}