TRUST: A Decentralized Framework for Auditing Large Language Model Reasoning

Morris Yu-Chao Huang, Zhen Tan, Mohan Zhang, Pingzhi Li, Zezhen Ding, ZHUO ZHANG, Tianlong Chen

Keywords: Reasoning Audit, LLM Safety

TL;DR: TRUST is a decentralized auditing framework for large language models reasoning.

Abstract: Large Language Models (LLMs) can produce complex reasoning chains, offering a window into their decision-making processes. However, verifying the quality (e.g., faithfulness and harmlessness) of these intermediate steps is a critical, unsolved challenge. Current auditing methods are often centralized, opaque, and struggle to scale, creating significant risks for the deployment of proprietary models in high-stakes domains. This paper addresses four key challenges in reasoning verification: (1) *Robustness*: Centralized systems are single points of failure, vulnerable to attacks and systemic bias. (2) *Scalability*: The length and complexity of reasoning traces create a severe bottleneck for human auditors. (3) *Opacity*: Internal auditing processes are typically hidden from end-users, undermining public trust. (4) *Privacy*: Model providers risk intellectual property theft or unauthorized model distillation when exposing complete reasoning traces. To overcome these barriers, we introduce TRUST, a decentralized framework for auditing LLM reasoning. TRUST makes the following contributions: (1) It establishes a decentralized consensus mechanism among a diverse set of auditors, provably guaranteeing audit correctness with up to 30\% malicious participants and mitigating single-source bias. (2) It introduces a scalable decomposition method that transforms reasoning traces into hierarchical directed acyclic graphs, enabling atomic reasoning steps to be audited in parallel by a distributed network. (3) All verification decisions are recorded on a transparent blockchain ledger, creating a permanent and publicly auditable record. (4) The framework is privacy-preserving by distributing only partial segments of the reasoning trace to auditors, thus protecting the full proprietary logic from distillation. We provide theoretical guarantees for the security and economic incentives of the TRUST framework. Experiments across multiple LLMs (e.g., GPT-OSS, DeepSeek-r1, Qwen) and reasoning tasks (e.g., mathematical, medical, science, and humanities) demonstrate that TRUST is highly effective at identifying reasoning flaws and is significantly more resilient to corrupted auditors than centralized baselines. Our work pioneers the field of decentralized AI auditing, offering a practical pathway for the safe and secure deployment of AI systems.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 3788