{
  "id": 14813,
  "url": "https://arxiv.org/abs/2607.26946v1",
  "title": "Belief-Guided Decision Making with Uncertainty Gating in the Game of Go",
  "summary": "Recent advancements in Computer Go, driven by AlphaZero and MuZero, rely heavily on Monte Carlo Tree Search (MCTS) to correct the errors of the neural network policy. While effective on massive computational clusters, this dependence creates a critical bottleneck on consumer-grade hardware, where the computational cost of tree management severely limits inference rates. Furthermore, without deep search, these models suffer from hallucination, proposing moves with high confidence that are strateg",
  "authors": "Mehrad Yaghoubi, Azam Bastanfard, Abbas Jalilvand, Ashkan Rezaei",
  "category": "research",
  "topics": "regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T14:15:29.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "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/14813",
  "original_url": "https://arxiv.org/abs/2607.26946v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}