Image Name
Blockchain · Decentralized AI · Generative AI · Large Language Models · Web3
The integration of artificial intelligence (AI) and decentralization is reshaping application and system design across industry, government, and academia. Existing surveys typically examine blockchain, Web3, or generative models independently, which obscures the cross-layer dependencies that govern verifiability, privacy, coordination, and governance in decentralized systems. This survey bridges that gap by introducing a unified trust-to-augmentation framework that organizes the convergence into four interdependent layers: trust-based execution, privacy-preserving interoperable middleware, collaborative learning mesh, and generative augmentation. Unlike prior work that addresses these domains in isolation or in limited binary pairings, the survey explains how advances in one layer alter the requirements and surfaces of the others and identifies research gaps that arise from the integration of decentralized and generative AI. We map representative systems to the four layers and consolidate a taxonomy of enabling techniques, evaluation metrics, and layer-specific comparison tables to support consistent positioning of novel contributions. The synthesis clarifies how the convergence mitigates key limitations of centralized AI, including opacity and single points of failure. It enables automated governance, intelligent consensus, and adaptive user interfaces that preserve fault tolerance and data sovereignty. The analysis also highlights deployment challenges, including scalability bottlenecks, privacy protection under transparent ledgers, cross-chain interoperability, model interpretability, and incentive alignment. The survey identifies barriers to widespread adoption and provides strategic guidance for researchers, practitioners, and policymakers through analysis of real-world applications and deployment methodologies.
This paper presents a comprehensive survey on the convergence of decentralized and generative intelligence, introducing a unified trust-to-augmentation framework that organizes blockchain, Web3, decentralized AI (DeAI), and generative AI (GenAI) into four interdependent layers: trust-based execution, privacy-preserving interoperable middleware, collaborative learning mesh, and generative augmentation. The novelty of this study lies in its cross-layer perspective. Unlike prior surveys that treat these domains in isolation or in limited binary pairings, it explains how advances in one layer alter the requirements and surfaces of the others, thereby revealing research gaps that arise specifically from integration rather than from any single technology.
The survey systematically maps representative systems to each framework layer and consolidates a taxonomy of enabling techniques, evaluation metrics, and layer-specific comparison tables. It identifies novel architectural patterns including intelligent consensus mechanisms (Proof-of-Intelligence, Proof-of-AIGC), privacy-preserving middleware protocols (ZKPs, SMPC, cross-chain communication), collaborative learning infrastructures (federated learning meshes, Byzantine fault-tolerant aggregation), and generative augmentation capabilities that enable automated governance and adaptive user interfaces.
In terms of contributions, the paper follows PRISMA-inspired methodology screening 532 candidate publications down to 223 selected studies, offering a reproducible and transparent corpus. It provides a strategic research roadmap addressing scalability bottlenecks, semantic consistency across heterogeneous representations, economic incentive alignment, and governance coordination. The synthesis demonstrates how convergence mitigates key limitations of centralized AI, including opacity and single points of failure, while enabling practical deployments across finance, healthcare, and governance domains. Finally, the survey identifies barriers to widespread adoption and provides actionable guidance for researchers, practitioners, and policymakers.
M. M. Karim, S. Khan, Q. Qu, M. Muzammal, K. Sharif, and S. Biswas, "From Trust to Augmentation: A Comprehensive Survey on Synergistic Integration of Decentralized and Generative Intelligence," Computer Science Review, vol. 61, p. 100936, 2026. https://doi.org/10.1016/j.cosrev.2026.100936
Karim, M. M., Khan, S., Qu, Q., Muzammal, M., Sharif, K., & Biswas, S. (2026). From Trust to Augmentation: A Comprehensive Survey on Synergistic Integration of Decentralized and Generative Intelligence. Computer Science Review, 61, 100936. https://doi.org/10.1016/j.cosrev.2026.100936
@article{karim2026trust,
title={From Trust to Augmentation: A Comprehensive Survey on Synergistic Integration of Decentralized and Generative Intelligence},
author={Karim, Md Monjurul and Khan, Sangeen and Qu, Qiang and Muzammal, Muhammad and Sharif, Kashif and Biswas, Sujit},
journal={Computer Science Review},
volume={61},
pages={100936},
year={2026},
doi={10.1016/j.cosrev.2026.100936},
publisher={Elsevier}
}
