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

2026-05-11

AI Summary

This study addresses the lack of systematic understanding of bidirectional architectural interactions between artificial intelligence (AI) and distributed ledger technology (DLT), as existing research is often limited to unidirectional integration or narrow applications. The work proposes and implements the first five-layer bidirectional AI-DLT classification framework, grounded in a systematic literature review spanning 2020–2025. It analytically dissects technical pathways for both AI-enhanced DLT and DLT-enhanced AI at the architectural level, covering key domains such as federated learning, model evaluation, multi-agent coordination, consensus mechanisms, and execution environments. The analysis reveals that current efforts are heavily concentrated on isolated layers and generally lack production-grade validation. The study identifies cross-layer co-design and real-world deployment as critical directions for overcoming bottlenecks in scalability, interoperability, and verifiable execution.

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.

Problem

• AI-DLT convergence
• architectural interplay
• scalability
• interoperability
• verifiable execution

Innovation

• bidirectional review
• AI-DLT convergence
• layered architecture
• cross-layer co-design
• systematic literature review