Paper 2026/637

VeriRAG: Efficient Zero-Knowledge Proofs for Verifiable Retrieval-Augmented Generation

Yubo Cui, Peking University

Zhelei Zhou, Ant Group

Cheng Hong, Ant Group

Yufei Wang, DAMO Academy, Alibaba Group, Hupan Lab

Zhaohui Chen, DAMO Academy, Alibaba Group, Hupan Lab

Meng Li, Peking University

Abstract

Retrieval-Augmented Generation (RAG) is widely used to enhance Large Language Models (LLMs), yet the "hallucination" characteristic allows malicious providers to bypass retrieval or claim non-existent data quality. To address these challenges, we present VeriRAG, a framework that leverages Zero-Knowledge Proofs (ZKP) to provide efficient integrity guarantees for RAG systems without compromising dataset privacy. Leveraging the robustness of AI inference, our framework supports Approximate Nearest Neighbor Search (ANNS)-based retrieval to avoid exhaustive searches. For the verification of top-$k$ sorting, we propose an innovative protocol that bypasses the intricate verification of sorting processes. To further enhance performance, we introduce a joint optimization leveraging vector lookup and chunk-merging strategies, which collectively drive down verification overhead while maintaining high generation accuracy. Experimental results demonstrate that VeriRAG scales efficiently to a 37GB dataset, achieving a prover time of 96s and a verifier time of 3s.

@misc{cryptoeprint:2026/637,
      author = {Chenqi Lin and Yubo Cui and Zhelei Zhou and Cheng Hong and Yufei Wang and Zhaohui Chen and Meng Li},
      title = {{VeriRAG}: Efficient Zero-Knowledge Proofs for Verifiable Retrieval-Augmented Generation},
      howpublished = {Cryptology {ePrint} Archive, Paper 2026/637},
      year = {2026},
      url = {https://eprint.iacr.org/2026/637}
}