SoK: A Systematic Bidirectional Literature Review of AI & DLT Convergence

TL;DR

This paper systematically reviews how AI and Distributed Ledger Technology (DLT) mutually enhance each other across various layers, highlighting current gaps and the need for real-world validation.

Contribution

It provides a structured, bidirectional classification of recent studies on AI-DLT convergence, identifying neglected layers and emphasizing the importance of empirical, cross-layer research.

Findings

Most studies focus on execution and consensus layers for AI-enhanced DLT.

DLT-enhanced AI research mainly targets data and model layers.

No study demonstrates deployment at production scale or fully addresses scalability and interoperability.

Abstract

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.