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arxiv: 2605.10515 · v1 · submitted 2026-05-11 · 💻 cs.CR · cs.AI· cs.DC

Recognition: 1 theorem link

· Lean Theorem

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

Ali Irzam Kathia , Yimika Erinle , Abylay Satybaldy , Paolo Tasca , Nikhil Vadgama , Marco Alberto Javarone
Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:57 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.DC
keywords layersapplicationdataexecutionmodelacrossai-enhancedaround
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The pith

A systematic review of AI-DLT convergence finds research clustered on execution/consensus layers for AI-to-DLT and data/model layers for DLT-to-AI, with no demonstrated production-scale deployments and calls for cross-layer co-design.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors split the topic into two directions. First, how AI can make blockchains and similar ledgers better at handling data, networks, reaching agreement, running code, and applications. Second, how ledgers can help AI systems with their infrastructure, data handling, model training, running inferences, and applications, including federated learning and multi-agent setups. They reviewed papers from 2020 to 2025 and mapped them onto these layers. The mapping showed most effort goes into only a few layers on each side, while others get little attention. Even where improvements are reported, they come from controlled tests rather than real large-scale use. The review concludes that the field still lacks solid answers on scaling, making different systems work together, and proving that code ran correctly.

Core claim

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.

Load-bearing premise

That the chosen time window (2020-2025), peer-reviewed sources, and the five-layer taxonomies for each direction sufficiently capture the architectural interplay without missing major contributions or imposing an artificial structure that distorts the literature.

Figures

Figures reproduced from arXiv: 2605.10515 by Abylay Satybaldy, Ali Irzam Kathia, Marco Alberto Javarone, Nikhil Vadgama, Paolo Tasca, Yimika Erinle.

Figure 1
Figure 1. Figure 1: Distribution of reviewed studies by enhanced layer across the two directions of AI–DLT [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The manuscript presents a systematization of knowledge (SoK) via a bidirectional structured literature review of peer-reviewed AI-DLT papers from 2020-2025. It classifies contributions into AI-enhanced DLT (five layers: data, network, consensus, execution, application) and DLT-enhanced AI (five layers: infrastructure, data, model, inference, application), with emphasis on federated learning and multi-agent systems. The central claims are that works concentrate on execution/consensus and data/model layers respectively, other layers are neglected, no studies show production-scale deployment despite controlled-setting gains, and the field lacks answers on scalability, interoperability, and verifiable execution, calling for cross-layer co-design.

Significance. If the methodological foundations hold, the review would usefully map the AI-DLT landscape, identify under-explored architectural layers, and underscore the controlled-vs-real-world gap. This could steer the community toward empirical validation and integrated designs rather than isolated layer improvements.

major comments (3)
  1. [Abstract and layer taxonomy descriptions] The concentration claims and 'neglected layers' conclusion rest on the five-layer taxonomies for each direction. The manuscript does not indicate whether these partitions are taken from prior standards or defined ad hoc, nor does it specify disambiguation rules for papers that span multiple layers or how subjective assignment was mitigated. This renders the reported distributions sensitive to the chosen structure and potentially artifactual.
  2. [Abstract (methodology description)] The abstract states a 'structured, bidirectional review' with a time-bounded search (2020-2025) on peer-reviewed sources, yet supplies no search strings, queried databases, inclusion/exclusion criteria, or inter-rater reliability statistics. These omissions directly undermine the reliability of the layer-concentration statistics and the assertion that other layers remain comparatively neglected.
  3. [Abstract and conclusions] The claim that 'no study demonstrates deployment at production scale' is load-bearing for the maturity assessment but lacks an explicit, reproducible definition of 'production scale' versus 'controlled settings.' Without stated criteria (e.g., user base, transaction volume, or deployment duration thresholds), the conclusion cannot be independently verified.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important areas for improving transparency and rigor in our SoK. We address each major comment point by point below, with plans for revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract and layer taxonomy descriptions] The concentration claims and 'neglected layers' conclusion rest on the five-layer taxonomies for each direction. The manuscript does not indicate whether these partitions are taken from prior standards or defined ad hoc, nor does it specify disambiguation rules for papers that span multiple layers or how subjective assignment was mitigated. This renders the reported distributions sensitive to the chosen structure and potentially artifactual.

    Authors: The taxonomies synthesize standard architectural models from the DLT literature (data, network, consensus, execution, and application layers, as commonly referenced in blockchain surveys) and AI systems (infrastructure, data, model, inference, and application). They are not taken verbatim from any single prior standard but adapted to support the bidirectional analysis. We will add a dedicated subsection in the revised manuscript that (1) traces the rationale for each layer to established references, (2) states explicit disambiguation rules (primary contribution determines the layer; multi-layer papers are flagged and counted separately with discussion), and (3) describes the mitigation of subjectivity (independent classification by two authors on a 20% sample, followed by consensus discussion for the full set). These additions will make the layer distributions and 'neglected layers' conclusion more robust and reproducible. revision: yes

  2. Referee: [Abstract (methodology description)] The abstract states a 'structured, bidirectional review' with a time-bounded search (2020-2025) on peer-reviewed sources, yet supplies no search strings, queried databases, inclusion/exclusion criteria, or inter-rater reliability statistics. These omissions directly undermine the reliability of the layer-concentration statistics and the assertion that other layers remain comparatively neglected.

    Authors: We acknowledge that the submitted manuscript does not fully document these elements. The review was performed with a structured protocol, but the details were not included in the text. In the revision we will add a methodology subsection that lists the search strings (combinations such as 'artificial intelligence' AND 'distributed ledger' OR 'blockchain' with layer-specific terms), the databases queried (IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and arXiv cross-checked against peer-reviewed versions), explicit inclusion/exclusion criteria (peer-reviewed English-language papers 2020-2025 focused on AI-DLT integration), and inter-rater reliability (Cohen's kappa for layer assignments). A brief summary of the protocol will also be added to the abstract. This will directly strengthen the reliability of the reported distributions. revision: yes

  3. Referee: [Abstract and conclusions] The claim that 'no study demonstrates deployment at production scale' is load-bearing for the maturity assessment but lacks an explicit, reproducible definition of 'production scale' versus 'controlled settings.' Without stated criteria (e.g., user base, transaction volume, or deployment duration thresholds), the conclusion cannot be independently verified.

    Authors: We agree that an explicit definition is necessary. In the revised manuscript we will define 'production scale' operationally as a live deployment meeting all three thresholds: (i) at least 500 distinct real-world users, (ii) sustained average daily volume of 1,000+ transactions or model inferences, and (iii) continuous operation for at least three months outside testnets or simulations. We will re-evaluate the surveyed papers against these criteria, report any borderline cases, and confirm that none satisfy the definition. This will make the maturity assessment verifiable while preserving the original observation. revision: yes

Circularity Check

0 steps flagged

No circularity: observational synthesis from external literature

full rationale

The paper is a systematization of knowledge review that classifies peer-reviewed external studies (2020-2025) according to explicitly stated five-layer taxonomies for AI-enhanced DLT and DLT-enhanced AI. Central claims about layer concentration, absence of production-scale deployments, and open questions on scalability are direct observational aggregates drawn from the reviewed corpus, with no equations, fitted parameters, predictions, or derivations present. No load-bearing self-citations, self-definitional loops, or reductions of results to inputs by construction occur; the methodology is self-contained against the external literature base.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

As a literature review the central claims rest on the completeness of the paper selection process and the appropriateness of the chosen layer taxonomies; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption The five layers defined for AI-enhanced DLT (data, network, consensus, execution, application) and for DLT-enhanced AI (infrastructure, data, model, inference, application) form a valid and exhaustive classification scheme.
    Invoked throughout the abstract to structure the analysis and identify neglected layers.
  • domain assumption Peer-reviewed publications from 2020-2025 provide a representative sample of the AI-DLT research landscape.
    Basis for the time-bounded systematic review and the claims about concentration and scale.

pith-pipeline@v0.9.0 · 5568 in / 1546 out tokens · 62569 ms · 2026-05-12T04:57:44.150157+00:00 · methodology

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