BlockQwen: A Robust LLM Powered by Blockchain and Smart Contracts

Abstract

Large Language Models (LLMs) are increasingly being adopted in sensitive domains such as healthcare and finance. However, persistent challenges such as unreliable data sources, privacy breaches, and hallucinated output continue to hinder their usage. To address these shortcomings, we propose BlockQwen, a blockchain-augmented framework that integrates decentralized trust validation, role-specific access control, and verifiable audit trails into Qwen 2.5 LLM workflow. Here, blockchain not only anchors the authenticity of retrieved documents, but also enforces dynamic, tamper-proof access and authorization policies and preserves transparent, immutable records of model interactions. This decentralized infrastructure ensures that the LLM operates on verified inputs while maintaining privacy and compliance with regulatory standards. A layered security module, combined with reinforcement learning, is further tailored to detect privacy risks and to mitigate hallucinations in real time. A prototype implementation, with simulated healthcare data, achieved an $86.25 \%$ privacy preservation rate and an $88.33 \%$ hallucination mitigation rate, significantly outperforming conventional LLM deployments. These results demonstrate the potential of combining blockchain functionality with LLM, resulting in robust, secure, transparent, and trustworthy AI systems.

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