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

arXiv: https://arxiv.org/abs/2605.10515 · HTML: https://mlsanity.com/paper/2605.10515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 99 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 20 of 20)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Artificial Intelligence for the Metaverse: A Survey](https://mlsanity.com/paper/2202.10336/) (2022)
- [BitTensor: A Peer-to-Peer Intelligence Market](https://mlsanity.com/paper/2003.03917/) (2020)
- [Blockchain-aided Secure Semantic Communication for AI-Generated Content in Metaverse](https://mlsanity.com/paper/2301.11289/) (2023)
- [Privacy and Trust Redefined in Federated Machine Learning](https://mlsanity.com/paper/2103.15753/) (2021)
- [Consensus in the Age of Blockchains](https://mlsanity.com/paper/1711.03936/) (2017)
- [Privacy-Preserving Deep Learning via Weight Transmission](https://mlsanity.com/paper/1809.03272/) (2018)
- [Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2506.18245/) (2025)
- [Analysis of Models for Decentralized and Collaborative AI on Blockchain](https://mlsanity.com/paper/2009.06756/) (2020)
- [Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains](https://mlsanity.com/paper/2007.15145/) (2020)
- [Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage](https://mlsanity.com/paper/2404.06077/) (2024)
- [AIArena: A Blockchain-Based Decentralized AI Training Platform](https://mlsanity.com/paper/2412.14566/) (2024)
- [Architecture of Automated Crypto-Finance Agent](https://mlsanity.com/paper/2107.07769/) (2021)
- [Understanding blockchain: definitions, architecture, design, and system comparison](https://mlsanity.com/paper/2207.02264/) (2022)
- [QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market Making Protocols](https://mlsanity.com/paper/2211.14977/) (2022)
- [Drawing the boundaries between Blockchain and Blockchain-like systems: A Comprehensive Survey on Distributed Ledger Technologies](https://mlsanity.com/paper/2409.18799/) (2024)
- [COLE: A Column-based Learned Storage for Blockchain Systems](https://mlsanity.com/paper/2306.10739/) (2023)
- [A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0](https://mlsanity.com/paper/2311.13731/) (2023)
- [SuiGPT MAD: Move AI Decompiler to Improve Transparency and Auditability on Non-Open-Source Blockchain Smart Contract](https://mlsanity.com/paper/2410.15275/) (2024)

## Abstract (arXiv)

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.