TRUST: A Framework for Decentralized AI Service v.0.1
The TRUST framework proposes a decentralized approach to improve the reliability and trustworthiness of Large Reasoning Models and Multi-Agent Systems. It addresses limitations in robustness, scalability, transparency, and privacy through innovations like HDAGs, the DAAN protocol, and a multi-tier consensus mechanism. The framework demonstrates improved accuracy, root-cause attribution, and resilience against adversarial attacks across multiple benchmarks.
- ▪TRUST introduces Hierarchical Directed Acyclic Graphs (HDAGs) to enable parallel distributed auditing of AI reasoning processes.
- ▪The DAAN protocol achieves 70% root-cause attribution accuracy with 60% token savings compared to standard methods.
- ▪TRUST's multi-tier consensus mechanism ensures correctness even with up to 30% adversarial participation.
- ▪The framework attains 72.4% accuracy, outperforming baselines by 4-18%, and maintains resilience under 20% corruption.
- ▪All audit decisions are recorded on-chain, with privacy-preserving design preventing reconstruction of proprietary model logic.
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Computer Science > Artificial Intelligence arXiv:2604.27132 (cs) [Submitted on 29 Apr 2026] Title:TRUST: A Framework for Decentralized AI Service v.0.1 Authors:Yu-Chao Huang, Zhen Tan, Mohan Zhang, Pingzhi Li, Zhuo Zhang, Tianlong Chen View a PDF of the paper titled TRUST: A Framework for Decentralized AI Service v.0.1, by Yu-Chao Huang and 5 other authors View PDF HTML (experimental) Abstract: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…
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