The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to cluster around specific application domains or examine only one direction of the integration, leaving the broader architectural interplay between the two technologies poorly understood. This work addresses that gap through a structured, bidirectional review of peer-reviewed studies published between 2020 and 2025. We classify contributions along two directions: AI-enhanced DLT, and DLT-enhanced AI. In the first case, we examine how AI techniques improve DLT systems across five layers: data, network, consensus, execution, and application layers. In the second case, we analyse how DLT supports AI systems across five layers: infrastructure, data, model, inference, and application layers, with particular attention to federated learning, model evaluation, and multi-agent coordination. The analysis reveals that most works concentrate on a small subset of layers: execution and consensus for AI-enhanced DLT, data and model for DLT-enhanced AI. Other layers remain comparatively neglected. Despite reported improvements in controlled settings, no study demonstrates deployment at production scale, and the field has not yet offered satisfying answers to fundamental questions around scalability, interoperability, and verifiable execution. We argue that progress will require cross-layer co-design and empirical validation in real-world settings.

翻译：人工智能（AI）与分布式账本技术（DLT）的融合已成为一个日益增长的研究领域，然而现有贡献往往集中于特定应用领域或仅考察单一方向的融合，导致对两种技术之间更广泛的架构互动关系认识不足。本文通过一项结构化的双向综述来弥补这一空白，研究对象为2020年至2025年间发表的同行评审论文。我们沿两个方向对贡献进行分类：AI增强型DLT与DLT增强型AI。在前者中，我们从数据层、网络层、共识层、执行层和应用层五个层面探讨AI技术如何改进DLT系统；在后者中，我们从基础设施层、数据层、模型层、推理层和应用层五个层面——特别关注联邦学习、模型评估与多智能体协调——分析DLT如何支持AI系统。分析显示，大多数工作集中于少数子层：AI增强型DLT中的执行层与共识层，以及DLT增强型AI中的数据层与模型层。其他层面则相对被忽略。尽管在受控环境下取得了改进报告，但尚无研究展示生产规模的部署，且该领域尚未就能扩展性、互操作性与可验证执行等基本问题提供令人满意的答案。我们认为，进展将需要跨层协同设计与真实世界环境下的实证验证。