Evidence record 5206 · automatically gathered

TRUST: A Framework for Decentralized AI Service v.0.1

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

Record details

Published: 29 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Privacy · Agents & autonomy · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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Shared topic: Bias & fairness · Shared topic: Agents & autonomy

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How to cite this record

ethics.ai (29 April 2026), “TRUST: A Framework for Decentralized AI Service v.0.1,” evidence record 5206, https://ethics.ai/record/5206 (originally published by arXiv).

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