A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain

• URL: http://arxiv.org/abs/2511.07577v1
• Date: Wed, 12 Nov 2025 01:04:54 GMT
• Title: A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain
• Authors: Yining Lu, Wenyi Tang, Max Johnson, Taeho Jung, Meng Jiang,
• Abstract summary: decentralization brings a challenge: the numerous independent data sources vary significantly in reliability.<n>Our system achieves a +10.7% performance improvement over its centralized counterpart in the real world-like unreliable data environments.<n>The decentralized infrastructure enables secure and trustworthy scoring management, achieving approximately 56% marginal cost savings.
• Score: 18.738400901246898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing retrieval-augmented generation (RAG) systems typically use a centralized architecture, causing a high cost of data collection, integration, and management, as well as privacy concerns. There is a great need for a decentralized RAG system that enables foundation models to utilize information directly from data owners who maintain full control over their sources. However, decentralization brings a challenge: the numerous independent data sources vary significantly in reliability, which can diminish retrieval accuracy and response quality. To address this, our decentralized RAG system has a novel reliability scoring mechanism that dynamically evaluates each source based on the quality of responses it contributes to generate and prioritizes high-quality sources during retrieval. To ensure transparency and trust, the scoring process is securely managed through blockchain-based smart contracts, creating verifiable and tamper-proof reliability records without relying on a central authority. We evaluate our decentralized system with two Llama models (3B and 8B) in two simulated environments where six data sources have different levels of reliability. Our system achieves a +10.7\% performance improvement over its centralized counterpart in the real world-like unreliable data environments. Notably, it approaches the upper-bound performance of centralized systems under ideally reliable data environments. The decentralized infrastructure enables secure and trustworthy scoring management, achieving approximately 56\% marginal cost savings through batched update operations. Our code and system are open-sourced at github.com/yining610/Reliable-dRAG.

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