Systematic Review Reveals Gaps in AI and Distributed Ledger Technology Convergence
A new systematic literature review published on arXiv analyzes the convergence of Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) between 2020 and 2025. The study addresses the lack of comprehensive understanding regarding the architectural interplay between these technologies by classifying research into two directions: AI-enhanced DLT and DLT-enhanced AI. The authors examine improvements across multiple layers, including consensus, execution, data, and model layers. Key findings indicate that current research is heavily concentrated on specific subsets, such as execution and consensus for AI-enhanced DLT, while other layers remain neglected. Crucially, the analysis reveals that no existing studies demonstrate deployment at production scale, with unresolved challenges in scalability, interoperability, and verifiable execution. The authors argue that future progress requires cross-layer co-design and empirical validation in real-world settings rather than controlled environments. This work highlights significant gaps in the field and provides a structured framework for future interdisciplinary research.
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Systematic Review Reveals Gaps in AI and Distributed Ledger Technology Convergence
A new systematic literature review published on arXiv analyzes the convergence of Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) between 2020 and 2025. The study addresses the lack of comprehensive understanding regarding the architectural interplay between these technologies by classifying research into two directions: AI-enhanced DLT and DLT-enhanced AI. The authors examine improvements across multiple layers, including consensus, execution, data, and model layers. Key findings indicate that current research is heavily concentrated on specific subsets, such as execution and consensus for AI-enhanced DLT, while other layers remain neglected. Crucially, the analysis reveals that no existing studies demonstrate deployment at production scale, with unresolved challenges in scalability, interoperability, and verifiable execution. The authors argue that future progress requires cross-layer co-design and empirical validation in real-world settings rather than controlled environments. This work highlights significant gaps in the field and provides a structured framework for future interdisciplinary research.
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