{
  "id": 5206,
  "url": "https://arxiv.org/abs/2604.27132v1",
  "title": "TRUST: A Framework for Decentralized AI Service v.0.1",
  "summary": "Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification, yet centralized approaches suffer four limitations: (1) Robustness, with single points of failure vulnerable to attacks and bias; (2) Scalability, as reasoning complexity creates bottlenecks; (3) Opacity, as hidden auditing erodes trust; and (4) Privacy, as exposed reasoning traces risk model theft. We introduce TRUST (Transparent, Robust, and Unified Services for Trustworthy AI), a d",
  "authors": "Yu-Chao Huang, Zhen Tan, Mohan Zhang, Pingzhi Li, Zhuo Zhang, Tianlong Chen",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-29T19:32:58.000Z",
  "fetched_at": "2026-07-14T16:31:35.574Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5206",
  "original_url": "https://arxiv.org/abs/2604.27132v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}