AI 中文总结
针对RAG系统难以验证文档可信度的问题，提出TrustRAG，结合区块链与委员会机制，通过零知识协议、安全多方计算及哈希承诺实现可验证的文档信任评分，保障关键领域决策安全。
AI 中文摘要
检索增强生成（RAG）使大型语言模型（LLMs）能够获取最新的、特定领域的信息，而非仅依赖训练数据。然而，大多数RAG系统仍从监督有限的集中式数据库中检索内容，难以验证文档来源、是否被篡改或是否可信。在医疗、金融、物流、法律判例等对检索内容的时效性和准确性要求极高的领域，这一问题尤为严重，错误或被篡改的文档可能直接导致决策失误。本文提出TrustRAG，一种基于委员会机制、由区块链支撑的RAG系统：文档在使用前，会由领域专家委员会通过零知识协议进行认证，委员会的隐藏评分通过安全多方计算合并为任何客户端均可验证的信任评分。这些评分及底层文档数据通过哈希承诺在链间共同维护，确保文档或评分不会被悄然篡改或删除，且每次排名均可独立复现和核查。
英文摘要
Retrieval-Augmented Generation (RAG) lets Large Language Models (LLMs) pull in up-to-date, domain-specific information instead of relying only on what they were trained on. Yet most RAG systems still draw from centralized databases with limited oversight, making it difficult to verify where a document came from, whether it has been tampered with, or whether it should be trusted at all. This is a serious problem in domains where both the timeliness and accuracy of retrieved content are critical, such as healthcare, finance, logistics, and legal case law, where a wrong or manipulated document can directly lead to bad decisions. We present TrustRAG, a committee-based, blockchain-backed RAG system: before a document is used, it is certified by a committee of domain experts through a zero-knowledge protocol, and the committee's hidden scores are combined via secure multi-party computation into a trust score that any client can verify. These scores, along with the underlying document data, are maintained jointly across chains through hash commitments, so no document or score can be silently altered or dropped, and every ranking can be independently replayed and checked.
