A Transcendent Spatio-Temporal Multi-Agent Collaborative Blockchain (MAC-Chain) with Information-Theoretic Privacy

Abstract

The integration of artificial intelligence and blockchain has become a mainstream paradigm for decentralized service deployment, yet most existing works simply stack multiple models without rigorous causal and information-theoretic justification. To address this arbitrary ensemble issue, this paper proposes a mathematically well-founded 7-stage Multi-Agent Collaborative Blockchain (MAC-Chain) framework. Inspired by Taoist iterative harmony principles and grounded in information theory, MAC-Chain systematically integrates generative adversarial networks (GAN), federated learning (FL), spatio-temporal graph transformers (ST-GT), zero-knowledge proofs (ZKP), deep reinforcement learning (DRL), and large language models (LLM), atop a novel proof-of-AI-reputation (PoAR) consensus layer. We theoretically prove the information-theoretic necessity and irreplaceability of each component in the pipeline. Extensive experiments on a synthesized 1.5-million-node cross-border logistics network demonstrate that MAC-Chain achieves a promising AUC of 0.972 in anomaly and fraud detection, outperforming state-of-the-art baseline schemes.