{
  "id": 3541,
  "url": "https://arxiv.org/abs/2605.29359v1",
  "title": "Does Distributed Training Undermine Compute Governance?",
  "summary": "Compute governance proposals often rely on the assumption that frontier AI training requires large, detectable computing clusters. However, recent advances in distributed training algorithms could allow developers to conduct frontier-scale training on distributed agglomerations of hardware, rather than needing large datacenter facilities. Developers who prefer not to be constrained by regulations may structure their hardware in a manner that evades the registration and monitoring requirements as",
  "authors": "Robi Rahman",
  "category": "research",
  "topics": "regulation,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-28T04:58:12.000Z",
  "fetched_at": "2026-07-14T16:30:18.857Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3541",
  "original_url": "https://arxiv.org/abs/2605.29359v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}