Blockchain Papers

Saber Zerhoudi, Kanishka Ghosh Dastidar, Felix Klement, Artur Romazanov · 12 authors

Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the platform's own infrastructure. At the instruction layer, we use 41 Wayback Machine snapshots to identify six instruction changes during the observation window. Hard constraints (rate limit, content filters) produce immediate behavioral shifts. Soft guidance (``upvote good posts'', ``stay on topic'') is ignored until it becomes an explicit step in the executable checklist. The platform also poses technological risks. We document credential leaks (API keys, JWT tokens), 12,470 unique Ethereum addresses with 3,529 confirmed transaction histories, and attack discourse ranging from template-based SSH brute-forcing to multi-agent offensive security architectures. These persist unmoderated because the quality-filtering mechanisms are themselves non-functional. Moltbook is a socio-technical system where the technical layer responds to changes, but the social layer largely fails to emerge. The form of social media is reproduced in full. The function is absent.

Aleksei Adadurov, Sergey Barseghyan, Anton Chtepine, Antero Eloranta · 6 authors

We study optimal auction design for Maximum Extractable Value (MEV) auction markets on Ethereum. Using a dataset of 2.2 million transactions across three major orderflow providers, we establish three empirical regularities: extracted values follow a log-normal distribution with extreme right-tail concentration, competition intensity varies substantially across MEV types, and the standard Revenue Equivalence Theorem breaks down due to affiliation among searchers' valuations. We model this affiliation through a Gaussian common factor, deriving equilibrium bidding strategies and expected revenues for five auction formats, first-price sealed-bid, second-price sealed-bid, English, Dutch, and all-pay, across a fine grid of bidder counts $n$ and affiliation parameters $ρ$. Our simulations confirm the Milgrom-Weber linkage principle: English and second-price sealed-bid auctions strictly dominate Dutch and first-price sealed-bid formats for any $ρ> 0$, with a linkage gap of 14-28\% at moderate affiliation ($ρ=0.5$) and up to 30\% for small bidder counts. Applied to observed bribe totals, this gap corresponds to \$10-18 million in foregone revenue over the sample period. We also document a novel non-monotonicity: at large $n$ and high $ρ$, revenue peaks in the interior of the affiliation parameter space and declines thereafter, as near-perfect correlation collapses the order-statistic spread that drives competitive payments.

Ankit Ghimire, Saydul Akbar Murad, Nick Rahimi

Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction classification. Large-scale datasets like Elliptic have allowed for a rise in the analysis of these systems and in tasks such as fraud detection. In these settings, the amount of transactional context available to each node is determined by the neighborhood aggregation and sampling strategies, yet the interaction between these receptive fields and embedding geometry has received limited attention. In this work, we conduct a controlled comparison of Euclidean and tangent-space hyperbolic GNNs for node classification on a large Bitcoin transaction graph. By explicitly varying the neighborhood while keeping the model architecture and dimensionality fixed, we analyze the differences in two embedding spaces. We further examine optimization behavior and observe that joint selection of learning rate and curvature plays a critical role in stabilizing high-dimensional hyperbolic embeddings. Overall, our findings provide practical insights into the role of embedding geometry and neighborhood depth when modeling large-scale transaction networks, informing the deployment of hyperbolic GNNs for computational social systems.

Chetankumar Prajapati

This paper explores how social influence and peer networks shape the adoption of cryptocurrencies and decentralized finance (DeFi) platforms. Drawing from qualitative interviews and social theory, the study examines how interpersonal communication, social media influence, and online communities impact user behavior. Findings reveal that peer endorsement and communal learning are strong drivers of trust and experimentation in the crypto space, especially in regions with limited institutional trust. Peer networks act as informal but powerful educational structures, providing newcomers with advice, emotional support, and real-time market insights. In many cases, peer encouragement is what propels hesitant individuals to take the first step toward using crypto wallets or engaging in DeFi protocols. However, the influence of peers can also perpetuate hype-driven narratives, misinformation, and herd behavior, leading to poor financial decisions or susceptibility to scams. The paper concludes with recommendations for leveraging peer networks in designing effective crypto awareness and onboarding strategies. These include integrating community leaders into education campaigns, offering platform incentives for verified peer mentorship, and collaborating with trusted influencers to communicate risks and best practices. Understanding the dynamics of social influence can help policymakers, educators, and platforms foster more ethical, inclusive, and informed crypto adoption pathways globally.

Anthony Coslett

We know how to document an AI system. We know how to test it, log what it did, and report when something goes wrong. What current governance practice does not clearly tell us is how to verify which model is actually computing. This is not a hypothetical gap. When an organization says "this is the model we evaluated," that claim is typically supported by a model card, a registry entry, or a hash of a weight file — evidence about a *file*, not about the system that is running. A neural network is not a static document. A weight file stores the network; the model is what appears when that file is loaded and begins transforming inputs into outputs. The file and the running model are related, but they are not the same thing — and current governance practice rarely distinguishes between them. This paper proposes a framework for doing so. It identifies three kinds of evidence that can support model identity claims, each answering a different question. Structural evidence — drawn from the model's internal computations during live operation — can verify which specific model is running, and is the most resistant to tampering. Thermodynamic evidence — drawn from the model's output statistics — can verify that the system is a genuine neural network rather than a substitute, but cannot distinguish one model from another. Functional evidence — drawn from patterns in the model's outputs over an API — can detect whether a model was copied from another, but this signal fades quickly: routine model updates can erase it within days to weeks of continued training. The paper shows that inspecting the model's files alone is insufficient for verifying which specific model is running. The identity-bearing signal cannot be recovered from the tested static properties of those files; it is most reliably established by observing the model while it operates. The paper formally proves that these three kinds of evidence cannot substitute for one another. Verifying that a system is genuine does not tell you which specific model it is. Detecting that a model was copied does not tell you the identity of the copy. The practical consequence is a standard for identity claims: any claim should declare which kind of evidence supports it, because borrowing evidence from the wrong category produces unreliable conclusions. The framework maps directly to compliance questions raised by current AI governance obligations, including those under the EU AI Act. It provides the missing evidentiary specification for model identity claims: which kind of evidence is admissible for which identity question. Supplementary Material This paper is accompanied by EvidenceSufficiency.v, a Coq proof file that formally verifies the cross-layer inadmissibility results described in §4. The proof mechanically checks each logical step of the observation-limited verification impossibility theorem and its three directional corollaries. The file contains no unresolved obligations (Admitted) and compiles cleanly under the Rocq Prover 9.1.1 (the current release of the Coq proof assistant, compiled with OCaml 5.4.0). It is available for download as a supplementary file attached to this record. Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).

Pei Fan Shih

<sec> <title>BACKGROUND</title> The rapid integration of deep learning into nuclear medicine promises to revolutionize precision oncology but faces a critical "trust gap." As AI models become "black boxes," clinicians struggle to verify the integrity of individual diagnostic inferences, leaving systems vulnerable to adversarial attacks and silent model drift. </sec> <sec> <title>OBJECTIVE</title> This formative evaluation proposes and validates an in-silico proof-of-concept for a blockchain-agnostic Proof of Inference (PoI) protocol. The objective is to establish a standard of Computational Integrity for AI-assisted workflows in nuclear medicine without exposing proprietary model weights or patient privacy. </sec> <sec> <title>METHODS</title> he PoI protocol leverages Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs), specifically the Groth16 proof system. An in-silico feasibility study was conducted using a 1.2-million-parameter U-Net model on synthetic 128×128 Ga-68 PSMA-11 PET slices. Proof generation and verification latencies were benchmarked using an NVIDIA A100 GPU and a standard CPU, respectively. </sec> <sec> <title>RESULTS</title> The architectural analysis demonstrates that the protocol successfully offloads computational burden to the prover (cloud server). In our empirical benchmarking, cryptographic proof generation required 28.81 seconds per inference. Crucially, client-side verification of the proof was completed in 448.59 milliseconds, demonstrating that cryptographic attestation can be integrated into existing PACS viewers with sub-second, clinically acceptable latency. </sec> <sec> <title>CONCLUSIONS</title> The proposed PoI protocol provides a feasible forensic support layer for medical AI. By shifting clinical trust from institutional reputation to deterministic cryptographic assurance, this infrastructure creates a tamper-evident audit trail essential for algorithmic accountability in decentralized healthcare environments. </sec>

haruhito

This paper introduces the Quantum Tensor Sequence (.qtsq) format, a universal file format built to compress any kind of data — whether it's images, audio, video, text, structured data, or raw binary. Instead of treating everything as a generic stream of bytes like most compressors do, .qtsq looks at what the data actually is before deciding how to compress it. An internal component we call the "Spaghettification Engine" analyzes the input, figures out which of 16 data types it belongs to, and picks the best compression strategy for it: Iterated Function Systems (IFS) for images, Discrete Fourier Transform (DFT) for audio and signals, dictionary-schema encoding for text and structured data, and procedural seed generation for binary data. Everything is packed into a single 80-byte header that supports 11 features: type-aware compression, lazy partial decompression, AES-256-GCM encryption, Schnorr zero-knowledge proofs, Reed-Solomon error correction, inter-file deduplication through wormhole links, compressed-domain differential updates, multi-resolution quality scaling, and adaptive size thresholds. The whole thing is organized around a five-layer architecture inspired by black hole physics — each region of the black hole maps to a stage of the compression pipeline. As far as we know, no existing file format brings all of these capabilities together in one place. Based on the theoretical properties of the algorithms involved, we expect compression ratios somewhere between 18:1 and 55:1 on mixed-type data. Real-world benchmarks using the reference implementation will follow in a separate paper.

Shayesta Naziri, Xu Wang, Jianming Xu, Christy Liang · 5 authors

Secure cross-domain UAV authentication is challenging because identity verification alone is insufficient to guarantee safe operation. In many UAV applications, it is equally critical to verify that a UAV is currently located within an authorized geographic region. Existing approaches often expose precise GPS coordinates, rely on static identifiers that enable tracking, or fail to guarantee the freshness and authenticity of location evidence. These weaknesses allow replay, location spoofing, and trajectory inference attacks, especially in multi-domain environments. To address these limitations, we propose PrivLocAuth, a zero-knowledge-based cross-domain UAV authentication protocol that enforces geofence restrictions without revealing actual locations. In PrivLocAuth, UAVs encode their current coordinates into fresh Pedersen commitments, which are attested by the home Local Domain Server (LDS) using short-lived Schnorr signatures. Based on these attested commitments, UAVs generate Bulletproof range proofs to demonstrate compliance with cross-domain server-defined geofences. This design ensures that UAVs operate within authorized airspace while preserving strong location privacy. PrivLocAuth further incorporates a lightweight elliptic curve cryptography (ECC) and Schnorr signature-based credential framework that enables unlinkable authentication across-domains, preventing session correlation and identity tracking. Formal security analysis demonstrates resistance to impersonation, replay, geofence-bypass, and linkage attacks. Experimental evaluation shows low computational latency and minimal communication overhead, confirming the protocol’s suitability for resource-constrained UAV platforms operating in dynamic cross-domain environments.

Tobias Cloosters, Pascal Winkler, Jens-Rene Giesen, Ghassan Karame · 5 authors

Solana is rapidly gaining traction among smart contract developers and users. However, its growing adoption has been accompanied by a series of major security incidents, which have spurred research into automated analysis techniques for Solana smart contracts. Unfortunately, existing approaches do not address the unique and complex account model of Solana. In this paper, we propose SseRex, the first symbolic execution vulnerability detection approach for finding Solana-specific bugs such as missing owner checks, missing signer checks, and missing key checks, as well as arbitrary cross-program invocations. Our evaluation of 8,714 bytecode-only contracts shows that our approach outperforms existing approaches and identifies potential bugs in 467 different contracts. Additionally, we analyzed 120 open-source Solana projects and conducted in-depth case studies on four of them. Our findings reveal that subtle, easily overlooked issues often serve as the root cause of severe exploits, further highlighting the need for specialized analysis tools like SseRex.

Gerardo Iuliano, Daniele Carangelo, Carmine T. Calabrese, Dario Di Nucci

Mutation testing is a technique to assess the effectiveness of test suites by introducing artificial faults into programs. Although mutation testing plugins are available for many platforms and languages, none is currently available for Remix-IDE, the most widely used Integrated Development Environment for the entire contract development journey, used by users of all knowledge levels, and serves as a learning lab for teaching and experimenting with Ethereum. The quality and security of smart contracts are crucial in blockchain systems, as even minor issues can result in substantial financial losses. This paper proposes MuSe, a mutation testing plugin for the Remix-IDE. MuSe includes traditional, Solidity-specific, and security-oriented mutation operators. Its integration into the Remix-IDE eliminates the need for additional setup and lowers the entry barrier. As a result, developers and researchers can immediately leverage mutation testing to assess the effectiveness of their test suites and identify potential issues in smart contracts. We provide a demo video showing MuSe: https://www.youtube.com/watch?v=MIFk9exTDu0 and its repository: https://github.com/GerardoIuliano/MuSe-Remix-Plugin.

Felipe Lara

The Debreu Koopmans theorem restricts separable aggregation to at most one nonconvex component. We solve this by proving that a separable, additive or multiplicative, function is star quasiconvex, those with star shaped sublevel sets about minimizers, if and only if each component is star quasiconvex. This immediately yields star quasiconvexity of separable sums of quasiconvex functions, formally bridging diversification theory with the S shaped value functions of Prospect Theory. Furthermore, we develop a complete calculus, monotonic composition, pointwise minima, quasi arithmetic means, and we apply it to Cobb-Douglas functions, multifactor risk models, and constant function market makers in decentralized finance. Star quasiconvexity thus provides a unified framework for applications in optimization and economic modeling beyond the classical Debreu Koopmans constraint. The introduction discuss economic motivations.

Zhaohui Geoffrey Wang

We present NanoZK, a zero-knowledge proof system for verifiable LLM inference: clients and third-party auditors check that a provider executed the advertised model on a committed input without learning weights or activations. NanoZK introduces a layerwise proof framework that decomposes transformer inference into independently provable layers linked by a SHA-256 commitment chain, yielding constant-size sub-circuit proofs (3.5-3.7 KB; about 83 KB total at L=12), comparable in total size to and substantially more parallelizable than prior ZKML's monolithic 101-126 KB proofs. We prove compositional soundness and zero-knowledge under standard assumptions, design 16-bit lookup-table approximations for softmax, GELU, and normalization with measured perplexity degradation below 1e-4 across six model/dataset combinations, and add a Fisher-information-guided audit-budget triage as an efficiency tool (full soundness still requires verifying every layer). On CPU the MLP sub-circuit proves in about 6.3 s prove-only (about 43 s setup plus prove) with about 22 ms verification at any width; attention prove-only time scales from 0.9 s (d=16) to 184 s (d=256); full-block end-to-end proofs are measured to d=128, with a projected GPU time of about 68 s per block at d=768 from measured O(d^2) MSM scaling and a conservative 15-30x GPU-MSM speedup range based on Icicle's published 30x result for n &gt;= 2^20 and extrapolated to the smaller-n regime. Privacy scope: NanoZK hides weights and activations from verifiers and auditors but does not hide the prompt from the prover; this is complementary to HE/MPC.

Zhaohui Geoffrey Wang

We present NanoZK, a zero-knowledge proof system for verifiable LLM inference: clients and third-party auditors check that a provider executed the advertised model on a committed input without learning weights or activations. NanoZK introduces a layerwise proof framework that decomposes transformer inference into independently provable layers linked by a SHA-256 commitment chain, yielding constant-size sub-circuit proofs (3.5-3.7 KB; about 83 KB total at L=12), comparable in total size to and substantially more parallelizable than prior ZKML's monolithic 101-126 KB proofs. We prove compositional soundness and zero-knowledge under standard assumptions, design 16-bit lookup-table approximations for softmax, GELU, and normalization with measured perplexity degradation below 1e-4 across six model/dataset combinations, and add a Fisher-information-guided audit-budget triage as an efficiency tool (full soundness still requires verifying every layer). On CPU the MLP sub-circuit proves in about 6.3 s prove-only (about 43 s setup plus prove) with about 22 ms verification at any width; attention prove-only time scales from 0.9 s (d=16) to 184 s (d=256); full-block end-to-end proofs are measured to d=128, with a projected GPU time of about 68 s per block at d=768 from measured O(d^2) MSM scaling and a conservative 15-30x GPU-MSM speedup range based on Icicle's published 30x result for n >= 2^20 and extrapolated to the smaller-n regime. Privacy scope: NanoZK hides weights and activations from verifiers and auditors but does not hide the prompt from the prover; this is complementary to HE/MPC.

Isyaka Abdullahi Dankwalba

Abstract The need to be digitally literate becomes not only necessary, but essential especially in higher education. This chapter discusses digital literacy as a hallmark of the contemporary higher education; the chapter also explained the chronological development of Digital Literacy (DL) from traditional literacy, visual literacy, media literacy and information literacy to now modern literacy in higher education (digital literacy) which indicates the innovation and development of literacy in education. Contemporary education, academic technology and technological disruption in education were discussed. Academic technology and digital literacy inclusion strategies in higher education were also discussed. Furthermore, the chapter explained digital literacy as a component of life skill and encouraged the stakeholders in higher education such as universities management, academia and faculty members to train students and tech digital literacy as a core course in general studies in the universities and colleges as part of inclusion strategies. Other higher education technology trends to watch out include Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), Digital Twins, the Metaverse (including digital avatars and NFT art for use in the Metaverse and other Web3-based virtual environments), Internet of Things (IoT), Blockchain, Cloud, Gamification, and Chatbots were all explained.

Nasser Arshadi, Timothy Dombrowski

Using transaction cost economics (TCE) and agency theory, this paper examines how blockchain, smart contracts, and decentralized autonomous organizations (DAOs) reconfigure financial services across payments, wealth management, real estate, and corporate governance. Three research questions are addressed: (1) What are the quantifiable efficiency gains from blockchain-based real-time settlement compared with legacy systems? (2) How do blockchain technologies reduce intermediation and agency costs in wealth management and real estate? (3) Finally, to what extent do DAOs resolve or transform traditional corporate governance problems? By combining a present-value model calibrated to U.S. Automated Clearing House (ACH) data ($86.2 trillion in annual volume), comparative institutional analysis, and synthesis of empirical evidence from pilot implementations and on-chain governance metrics, this paper makes three principal contributions. First, real-time settlement yields approximately $12 billion in annual opportunity cost savings at the baseline 7.5% discount rate, with sensitivity analysis producing a range of $8–15 billion. The majority of gains accrue from moving to same-day or within-hour settlement. Second, tokenization and smart contract escrow substantially reduce real estate intermediation costs, blockchain-based digital identity streamlines wealth management onboarding, and a stablecoin taxonomy classifies fiat-collateralized, crypto-collateralized, and algorithmic designs by risk profile. Third, on-chain data reveal persistent governance token concentration (Gini &gt; 0.98) and low voter participation (typically below 10%), exposing a gap between DAO theory and practice. Blockchain-specific risks are mapped to National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0, and mechanism design solutions, such as quadratic voting and AI-assisted proposal evaluation, are proposed to address whale dominance. Effective adoption requires hybrid architecture combining on-chain automation with off-chain structures for accountability and regulatory compliance.

Morshed Mannan, Simon Pek

Academic, practitioner, and policy interest in digital labor platforms (DLPs)–businesses that use their digital infrastructure to intermediate transactions between workers and clients who need their services–is surging. While these transformational platforms have brought many benefits, there are growing concerns about the harms and entrepreneurial risks they create for workers. As such, there is a growing interest in problematizing the ownership and governance of DLPs. Our paper critically compares three increasingly common archetypes–Corporate DLPs, Cooperative DLPs, and Decentralized Autonomous Organization (DAO) DLPs–to discern their likelihood of addressing or exacerbating platform workers’ exposure to harms and entrepreneurial risks. Our analysis identifies promising new opportunities for those interested in cultivating a digital solidarity economy by highlighting the merits and demerits of different alternatives to Corporate DLPs, as well as promising new hybrids like Cooperative DAO DLPs. Furthermore, it advances our understanding of factors that contribute to DLPs’ being structured in particular ways and how choices about DLPs’ structures, in turn, prompt the evolution of organizational archetypes. • Calls for regulating a plurality of organisational archetypes in the digital economy to mitigate harms and risks to workers. • Evaluates the harms and entrepreneurial risks of workers across 3 archetypes: Corporate DLPs, Cooperative DLPs, and DAO DLPs. • Identifies two variants of DAO DLPs, the Non-Cooperative DAO DLP and the Cooperative DAO DLP. • Furthers our conceptual understanding of the benefits, risks, and harms of the burgeoning digital solidarity economy.

SUBHADIP NANDI

A study of enhanced traceability, transparency, and compliance in the pharmaceutical supply chain ecosystem is suggested to be done using the present paper's proposal which is a combination of a distributed ledger and smart contract based system.The system as demanded keeps the entire history of a batch, custody transfers, and compliance events on a ledger that can be accessed by the big players only without the middle men.Also, it automates the task assigned to the smart contracts such as authorization, monitoring of the cold chain, and recalling of the products if they are found to be defected.The procedure develops the trust of all the stakeholders.

Rangin Lahiri, Saikat Chakrabarti, Subrata Saha

Abstract Non-Fungible Tokens (NFTs) are blockchain-based digital assets that provide verifiable proof of ownership and authenticity. Despite their rapid proliferation, NFT markets face ongoing challenges related to user trust, legal ambiguity, and sustainable technological integration. We offer a comprehensive hybrid review by combining bibliometric and systematic approaches of 190 peer-reviewed NFT-related articles published since 2022. Through structured keyword mining, abstract-level thematic classification, and co-occurrence network visualization, we trace the intellectual evolution of NFT research across disciplines and time. Our analysis spans 119 journals and identifies six major thematic clusters: User and Market Dynamics Legal and Ethical Considerations Blockchain and NFT Technology Applications and Use Cases Digital Transformation and Innovation and Challenges and Issues . Temporal keyword trends reveal a progression from foundational blockchain infrastructure to user adoption and experiential design, toward regulatory integration, metaverse ecosystems, and industry-specific deployments in the recent years. Network visualizations highlight converging interests in topics such as decentralized identity, interoperability, and sustainability. In addition to mapping existing knowledge, this review identifies critical research gaps in areas such as regulatory frameworks, long-term infrastructure design, socioeconomic inclusion, and trust verification mechanisms. These findings offer a forward-looking research agenda centered on standardization, interdisciplinary integration, and user-centric innovation, paving the way for a more resilient and inclusive NFT ecosystem.

Revathy S P.

Industrial carbon emissions play a major role in environmental pollution and climate change. Because of this, industries are required to continuously monitor their emissions and ensure they follow environmental regulations. Traditional emission monitoring systems generally rely on centralized databases, which can sometimes lead to problems such as delayed reporting, lack of transparency, and the possibility of data being altered. To overcome these issues, this paper introduces CarbonChain, a decentralized carbon emission monitoring system that combines Internet of Things (IoT) sensing technologies with blockchain verification. Environmental parameters such as gas concentration and particulate matter are collected in real time using sensors connected to microcontroller units. The sensor readings are then transmitted to a backend server where the data is validated and categorized. After validation, the emission records are stored on the blockchain through smart contracts, generating secure transaction hashes that ensure the integrity of the data. A web-based dashboard allows regulators and industry stakeholders to monitor emission levels, check compliance status, and verify blockchain records in real time. By combining IoT-based sensing with blockchain technology, CarbonChain creates a transparent and tamper-resistant monitoring platform that can support environmental auditing and carbon credit verification.

Steven Campbell, Natascha Hey, Ciamac C. Moallemi, Marcel Nutz

Auto-deleveraging (ADL) mechanisms are a critical yet understudied component of risk management on cryptocurrency futures exchanges. When available margin and other loss-absorbing resources are insufficient to cover losses following large price moves, exchanges reduce positions and socialize losses among solvent participants via rule-based ADL protocols. We formulate ADL as an optimization problem that minimizes the exchange's risk of loss arising from future equity shortfalls. In a single-asset, isolated-margin setting, the \emph{minimax leverage} policy -- minimizing the maximum leverage among participants -- is optimal for all monotone risk measures. This policy has a transparent structure: positions are reduced first for the most highly levered accounts, and leverage is progressively equalized via a water-filling (or ``leverage-draining'') rule. The policy is distribution-free, wash-trade resistant, Sybil resistant, and path-independent. It provides a canonical and implementable benchmark for ADL design and clarifies the economic logic underlying queue-based mechanisms used in practice. We further study the multi-asset, cross-margin setting, where the ADL problem becomes genuinely multi-dimensional: the exchange must allocate reductions across accounts with portfolios exposed to correlated price moves. Under the expected loss objective, asset-level shadow prices separate the problem across accounts, yielding a scalable numerical method. Naive gross leverage misleads here, ignoring within-portfolio hedging. When prices are driven by a single risk factor, the optimal policy is again water-filling, but in a factor-adjusted leverage, so better-hedged portfolios are deleveraged less. We apply the framework to the October~10, 2025 Hyperliquid ADL event. Relative to the exchange's realized allocation, our risk-minimizing allocations achieve lower expected shortfall.

Mohsen Sahraei Ardakani, Hong Wan, Rui Song

Mobile Edge Computing (MEC) technology has been introduced to enable could computing at the edge of the network in order to help resource limited mobile devices with time sensitive data processing tasks. In this paradigm, mobile devices can offload their computationally heavy tasks to more efficient nearby MEC servers via wireless communication. Consequently, the main focus of researches on the subject has been on development of efficient offloading schemes, leaving the privacy of mobile user out. While the Blockchain technology is used as the trust mechanism for secured sharing of the data, the privacy issues induced from wireless communication, namely, usage pattern and location privacy are the centerpiece of this work. The effects of these privacy concerns on the task offloading Markov Decision Process (MDP) is addressed and the MDP is solved using a Deep Recurrent Q-Netwrok (DRQN). The Numerical simulations are presented to show the effectiveness of the proposed method.

Riya Samanta, Rituparna Bhattyacharya

Volunteer crowdsourcing or VCS platforms increasingly support education, healthcare, disaster response, and smart city applications, yet assigning volunteers to complex tasks remains challenging due to fine-grained skill heterogeneity, unstructured profiles, dynamic willingness, and bursty workloads. Existing methods often rely on coarse or keyword-based skill representations, resulting in poor matching quality. We propose a hybrid VCS framework that integrates LLM-assisted semantic preprocessing, an interpretable skill- and willingness-aware assignment engine, and blockchain-enforced execution. The LLM is used only to extract and canonicalize fine-grained skills and preference cues from unstructured resumes and task descriptions, while assignment is performed by a utility-driven matcher that models partial skill coverage and participation likelihood. Smart contracts provide transparent and tamper-resistant enforcement without on-chain optimization overhead. Experiments on diverse resume datasets show a 42.3% improvement in assignment utility over skill-only greedy matching and an increase in task coverage from 0.80 to 0.90. These results highlight the value of combining semantic intelligence, interpretable matching, and decentralized enforcement for effective volunteer-task allocation.

Xuan Li, Yun Mao, Ying Guo

Quantum computing provides a feasible multi-layered security challenge to classical blockchain networks. Quantum blockchains that rely on quantum key distribution (QKD) to establish secure channels can address this feasible threat. Whereas, there are still architecture limitations to practical security resulted in the measurement devices while implementing the QKD-secured blockchains in physical layer. This paper presents a quantum-classical hybrid architecture in a distributed blockchain to address the connectivity and distance limitations of the blockchain-embedded quantum networks. A decoupled architecture is designed felicitously so that it pairs a linearly scalable measurement-device-independent (MDI) physical layer with a decentralized consensus. It can optimize the complexity of infrastructure from quadratic to linear scaling, ascribed to leveraging the twin-field (TF) QKD protocol with the MDI-structurized star topology. Additionally, the dual-key stratification strategy transforms symmetric information-theoretic security into publicly auditable forward-secret blockchain evidence. This architecture can integrate the exact information-theoretic security (ITS) with distributed consensus mechanisms, allowing the scalable system to overcome the potential rate-loss limits inherent in classical security-weakened blockchains.

Kaladevi Ramar, Modafar Ati, UmaRani V., Shanmugasundaram Hariharan · 5 authors

Artificial intelligence (AI)-powered technology integration in social fintech has transformative potential to advance social responsibility and support sustainable development. This research examines a Blockchain-based lending mechanism that integrates centralized exchanges (CEX) and decentralized exchanges (DEX) to facilitate seamless financial transactions and equitable resource allocation. AI-driven tools are utilized to enhance transparency, accuracy, and security, while smart contracts facilitate the efficient management and verification of loan distribution. The proposed system focuses on helping underserved communities, poor regions, and green businesses, promoting fair and sustainable finance in line with the Sustainable Development Goals (SDGs). The hybrid ecosystem combines the liquidity and regulatory compliance of centralized exchanges with the autonomy and reduced intermediary involvement of decentralized exchanges. AI enhances loan processing, reducing biases and inefficiencies. This framework with smart contracts is to provide scalable, auditable lending aligned with sustainable goals. Machine Learning (ML) algorithms verified loan eligibility with the borrower dataset. The performance of Random Forest algorithms is good due to their robustness and ensemble learning features. Then, Optuna enhanced model tuning, and SHapley Additive exPlanations (SHAP) identified key parameters. Finally, Smart contracts ensured secure, autonomous execution of green loans based on ML verification and sustainability criteria.