Classical ledger systems, including proof-of-work (PoW) and proof-of-stake (PoS) blockchains;derive their security from the assumption that irreversible computation incurs a thermodynamic cost. This assumption, rooted in Landauer’s principle, implies that reversing orre-writing global state requires expenditure of significant physical energy, and therefore canbe made economically infeasible.In this paper, we introduce the RHEA–Λ Gate Family: a reversible multi–radix (2–3–5)logic primitive with a triangular, measure-preserving topology that embeds directly intoHamiltonian phase-space flows. Each gate includes an intrinsic symbolic (glyph/entropy)register enabling perfect, lossless history retention without information erasure. Whencomposed into circuits, Λ–gates form fully reversible, entropy-preserving state-transitionoperators capable of implementing arbitrary classical computations at asymptotically zeroenergy in adiabatic regimes.We show that any ledger whose security relies on computational irreversibility becomesvulnerable in a computational substrate that supports (i) strictly reversible evolution, (ii)zero–entropy symbolic memory, and (iii) multi-radix reversible hashing. In such substrates,the economic barrier that protects ledger history vanishes: all PoW functions become ther-modynamically free, PoS penalties become reversible, and Merkle-tree hashing no longerprovides unidirectional security. We formalize this result as an impossibility theorem forirreversible-cost security models, and we construct a reversible ledger architecture whose cor-rectness is maintained through Hamiltonian invariants rather than dissipative computationalcost.The Λ framework thereby provides both (a) a constructive alternative to irreversible ledgermechanisms and (b) the first proof that classical reversible computation, when extendedto higher radices with symbolic memory, nullifies the energy-based assumptions underlyingmodern blockchain security.
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Physical Unclonable Functions (PUFs) and Hardware Security
Arthur Carvalho, Liudmila Zavolokina, Suman Bhunia, Gerhard Schwabe
Regulatory changes have enabled American student-athletes to profit from their name, image, and likeness (NIL). However, only a fraction of the student-athlete population is actually profiting from their NIL, which raises questions concerning fairness and inclusiveness. Motivated by that scenario, we look at technological solutions capable of sharing a limited amount of financial resources fairly and inclusively. Following a design science methodology, we define design requirements for such technological solutions after interviewing student-athletes, which leads us to establish the inclusive-meritocratic fairness criterion. Subsequently, we determine design principles that artifacts aiming at helping student-athletes should satisfy. We find that a solution that satisfies the proposed design principles is to associate student-athletes with digital collectibles represented as non-fungible tokens (NFTs). The core idea behind our artifact is that student-athletes receive royalties in primary markets after NFTs are randomly minted, plus deterministic royalties in secondary markets whenever a transaction involving their collectibles happens. Interviews with student-athletes validate our design. We conclude the paper by discussing how our ideas give rise to a new NIL design theory.
Open access
Digital Games and Media
Ethics and Social Impacts of AI
Consumer Behavior in Brand Consumption and Identification
Stablecoin -Stable-Caucasus (STCAUX)Technical Whitepaper v1.0 Multi-Asset Commodity-Backed Stablecoin for the South Caucasus Region1. Executive Summary StableCaucasus is a multi-component stable digital asset based on the economic foundation of the South Caucasus and pegged to: ● regional currencies (AZN, GEL, AMD) oriented toward USD exchange rates; ● commodity indices: gold, agriculture, metals mining, oil and gas; ● the region’s energy and resource potential; ● Logistics ● uranium as a strategic component and future driver of global energy The goal of StableCaucasus is to create a regional, resilient digital asset that reflects the real economic potential of the Caucasus and provides users with stability, liquidity, and protection against currency volatility. 2. Mission & VisionMissionTo create the world’s first multi-commodity stable asset representing the South Caucasus as a center of: ● agricultural production, ● energy, ● gold mining, ● Logistics and strategic natural resources Vision To position StableCaucasus as: ● a settlement unit for cross-border trade between the Caucasus, GCC, EU, and Central Asia; ● a protective asset for investors; ● a digital instrument for governments, banks, logistics operators, and exporters. 3. Value Proposition StableCaucasus provides: ✔ Stability — the multi-index model reduces dependence on any single asset. ✔ Trust -each part of the formula is based on public financial and macroeconomic data. ✔ Regional relevance- partially reflects the real economy of the South Caucasus. ✔ Collateralization - the asset is backed by auditable and transparent baskets. ✔ Web3 infrastructure - BNB Chain and Ethereum compatible.
The rapid development of digital infrastructures demands new approaches to managing born-digital data and enabling knowledge valorisation. Rather than constructing another digital Library of Alexandria, the SODHA-team is developing Project BLU, a framework for building a distributed information network. Drawing inspiration from Web3 principles—decentralisation, interoperability, and user-driven discovery—the project explores methods for metadata exchange between archives. This project engages with established metadata standards like the Data Documentation Initiative (DDI), exploring how they can support or be extended to enable interoperability. Instead of centralising data, Project BLU enables a shared, federated catalogue based on metadata-exchange accessible from any entry point. Researchers will navigate this network to discover and link data across disciplines, reducing access barriers and opening new avenues for metadata specialists. These include promoting and, where needed, upgrading DDI standards, linking interdisciplinary data, and bridging vocabularies across scientific fields. The interconnected nature of the system reinforces adherence to FAIR principles, improves access to both open and restricted data, and enhances knowledge reuse at national and international levels (e.g. linking CESSDA and DDI partners). This presentation outlines the conceptual foundations of Project BLU and its vision for transforming metadata into dynamic, actionable links across the research landscape.
Modern states rely on legacy financial infrastructures—paper currency, centralized taxation, and compliance-heavy fiscal administration—that impose massive operational costs and produce structural inequities across generations. This paper proposes a circular, future-oriented national operating system grounded in tensional theory and value-flow mechanics. The system integrates Burn Mandate (automatic burn of violence-derived value), ChildChain (a value-distribution model prioritizing future generations), and a post-currency architecture that replaces cash-based infrastructures with programmatic smart contracts. The framework reveals (a) the cognitive and ethical limitations of full DAO governance, (b) the unsustainable tension generated by centralization-dependent money systems, and (c) the possibility of a hybrid model where governments maintain responsibility while adopting web3-derived automation. This study argues that value circulation—not accumulation—is the fundamental determinant of social stability. Integrating tensional theory with computational governance yields a normative model for a nation-state where negative externalities are burned, positive value is propagated to children, and fiscal administration becomes fully autonomous, transparent, and circular.
This research paper presents the design, architecture, and evaluation of a Hybrid Blockchain Database System, a decentralized–distributed model that integrates blockchain’s immutability with the high-performance data processing capabilities of traditional database systems. The proposed hybrid architecture addresses key limitations of fully on-chain storage—such as scalability constraints, high transaction costs, and limited query efficiency—by combining on-chain verification layers with off-chain distributed storage engines. The system introduces a multi-layered architectural model consisting of: (1) a blockchain consensus and validation layer for ensuring trust and tamper-resistant auditability; (2) a distributed database layer for high-speed read/write operations; (3) an interoperability layer that synchronizes state between on-chain and off-chain data; and (4) an IPFS/Filecoin-based decentralized storage framework for storing large datasets and metadata. This design enables secure state anchoring, cryptographic data integrity, and scalable data indexing across heterogeneous storage environments. A detailed performance evaluation is conducted using a combination of Ethereum-compatible smart contracts, IPFS storage, and distributed NoSQL/SQL databases. Key metrics such as transaction throughput, read/write latency, storage overhead, and consistency verification time are analyzed under varying workloads. Experimental results demonstrate that the hybrid system improves data scalability by 70–85%, reduces storage costs by over 90%, and maintains strong auditability through periodic blockchain state commitments. Security analysis is performed using static and dynamic testing tools to identify common vulnerabilities such as broken access control, inconsistent state updates, or missing verification boundaries, ensuring robustness of the hybrid synchronization logic. Additionally, the paper discusses design trade-offs involving decentralization vs. performance, on-chain transparency vs. gas efficiency, and consistency vs. latency across distributed components. This work provides a comprehensive blueprint for the development of real-world hybrid blockchain database systems suitable for supply chain management, enterprise data systems, government registries, financial applications, and Web3 infrastructure. The results highlight how hybrid architectures can bridge the gap between decentralized trust and scalable data management in next-generation digital ecosystems.
This paper analyzes the intersection of presidential authority and cryptocurrency markets during Donald J. Trump's second term (2025-2029). We examine developments from 2024 through October 2025, focusing on how executive influence, family business ventures, and digital assets became intertwined in ways that blurred boundaries between public office and private profit. Using a mixed-methods approach that combines quantitative market data with qualitative institutional assessment, we identify politically linked digital assets as a distinct class characterized by reflexive valuations, asymmetric risk distribution, and systemic vulnerabilities. The Trump family's integrated cryptocurrency ecosystem reached peak valuations exceeding eleven billion dollars before collapsing by more than one trillion in market capitalization following a tariff announcement in October 2025. Results highlight conflicts of interest, failures in market microstructure, and the emergence of political finance as a monetizable phenomenon in the digital age. The study contributes to understanding how presidential signaling reshapes capital flows, how politically branded tokens function as quasi-currencies, and how sudden policy actions can trigger cascading liquidations across global digital asset systems.
USDT, a stablecoin pegged to dollar, has become a preferred choice for money laundering due to its stability, anonymity, and ease of use. Notably, a new form of money laundering on stablecoins -- we refer to as crowdsourcing laundering -- disperses funds through recruiting a large number of ordinary individuals, and has rapidly emerged as a significant threat. However, due to the refined division of labor, crowdsourcing laundering transactions exhibit diverse patterns and a polycentric structure, posing significant challenges for detection. In this paper, we introduce transaction group as auxiliary information, and propose the Multi-Task Collaborative Crowdsourcing Laundering Detection (MCCLD) framework. MCCLD employs an end-to-end graph neural network to realize collaboration between laundering transaction detection and transaction group detection tasks, enhancing detection performance on diverse patterns within crowdsourcing laundering group. These two tasks are jointly optimized through a shared classifier, with a shared feature encoder that fuses multi-level feature embeddings to provide rich transaction semantics and potential group information. Extensive experiments on both crowdsourcing and general laundering demonstrate MCCLD's effectiveness and generalization. To the best of our knowledge, this is the first work on crowdsourcing laundering detection.
The emergence of Large Language Models (LLMs) is rapidly accelerating the development of autonomous multi-agent systems (MAS), paving the way for the Internet of Agents. However, traditional centralized MAS architectures present significant challenges, including single points of failure, vulnerability to censorship, inherent scalability limitations, and critical trust issues. We propose a novel Decentralized Multi-Agent System (DMAS) architecture designed to overcome these fundamental problems by enabling trust-aware, scalable, and censorship-resistant interactions among autonomous agents. Our DMAS features a decentralized agent runtime underpinned by a blockchain-based architecture. We formalize a trust-aware communication protocol that leverages cryptographic primitives and on-chain operations to provide security properties: verifiable interaction cycles, communication integrity, authenticity, non-repudiation, and conditional confidentiality, which we further substantiate through a comprehensive security analysis. Our performance analysis validates the DMAS as a scalable and efficient solution for building trustworthy multi-agent systems.
Smart contracts are a core component of blockchain technology and are widely deployed across various decentralized scenarios. However, atomicity violations have become a critical potential security risk. Existing analysis tools often lack the precision required to detect these issues effectively. To address this challenge, we introduce AtomGraph, an automated framework designed for detecting atomicity violations. This framework leverages Graph Convolutional Networks (GCN) to accurately identify atomicity violations through multimodal feature learning and fusion. Specifically, driven by a collaborative learning mechanism, the model simultaneously learns from two heterogeneous modalities: extracting structural topological features from the bytecode-based Control Flow Graph (CFG) and uncovering deep semantics from its opcode sequence. We designed an adaptive weighted fusion mechanism to dynamically adjust the weights of features from each modality to achieve optimal feature fusion. Finally, GCN detects graph-level atomicity violations on the contract. Comprehensive experimental evaluations demonstrate that AtomGraph achieves 96.88% accuracy and 96.97% F1 score, outperforming existing tools. Furthermore, compared to the baseline concatenation fusion model, AtomGraph improves the F1 score by 6.4%, proving its potential in smart contract security detection.
Abstract This study proposes a new approach to optimizing the routing of Automated Guided Vehicles (AGVs) in large-scale logistics warehouses using Quantum Annealing. As logistics operations grow, efficient AGV routing becomes critical for ensuring safety, reliability, and throughput, particularly in high-density environments. To address the complexity of real-world systems, we introduce an enhanced cost function that incorporates a real-time priority factor to improve both routing performance and operation safety. In this paper, we present a secure and computationally efficient candidate route generation method, along with an Optimization Problem Clustering technique that decomposes the large problem into smaller, tractable subproblems to reduce computational complexity. The proposed methods are designed to incorporate real-time operational data to ensure collision avoidance, maintain safe operations, and enhance routing efficiency in realistic large-scale warehouse environments. We validate the proposed methods using a state-of-the-art Quantum Annealing machine, benchmarking against both classical and quantum-inspired solvers. Integration with a commercial AGV Operating System (AOS) is demonstrated, enabling seamless connectivity with classical solvers via a local network and with Quantum Annealing and Quantum-inspired solvers via cloud infrastructure. Simulations involving 1000 AGVs show that the proposed route generation method reduces the number of optimization variables by an average of 96% for small-scale to medium-scale systems and 78% for large-scale systems, while also improving route quality. The clustering method reduces the maximum problem size to under 10,000 variables, enabling scalable and safe control of large AGV fleets. Furthermore, a complexity-based problem formulation reduces sampling time by approximately 28.2% compared to conventional size-based approaches for problems ranging from 1000 to 10,000 variables. These results demonstrate the practical viability of Quantum Annealing for managing large-scale AGV Operating Systems and underscore the potential of the proposed methods for advancing logistics optimization in real-world warehouse environments.
A revolução tecnológica está transformando profundamente a sociedade, alterando a forma como nos organizamos politicamente e nos inserimos no tempo e no espaço. A introdução de novas tecnologias, no campo das contratações, tem propiciado, no âmbito privado, reflexões importantes sobre a possibilidade de utilizar a inteligência artificial e os smart contracts para resolver problemas como a incompletude contratual e o descumprimento de obrigações. O artigo tem por finalidade descrever como esse processo vem se desenvolvendo e avaliar a possibilidade de incorporar esses novos instrumentos às contratações públicas brasileiras, considerando as regras jurídicas aplicáveis a esse tipo de relação e as particularidades do arranjo institucional nacional.
The purpose of this study is to address the persistent security and privacy challenges in cloud-based Electronic Health Record (EHR) sharing by proposing a blockchain-enabled architecture that integrates decentralized storage and smart contracts. Traditional mobile cloud solutions improve data accessibility but rely on centralized control, making them vulnerable to unauthorized access, single points of failure, and limited patient transparency. To overcome these limitations, this research designs a user-centric access control framework that leverages the Ethereum blockchain, smart contracts, and the InterPlanetary File System (IPFS) within a mobile cloud environment. The methodology involves developing and deploying a prototype on Amazon Web Services, supported by an Android-based mobile application that enables healthcare providers and patients to interact with the blockchain network. Experimental evaluation was conducted using wearable sensor data to test the performance, scalability, and resilience of the proposed system. The findings indicate that the framework ensures secure EHR exchange, enforces fine-grained access policies, and achieves reduced latency compared to conventional centralized approaches. Unauthorized requests were reliably detected and blocked through the smart contract mechanism, while authorized users accessed records with minimal delay. The results also confirm the lightweight overhead of the system, making it practical for mobile healthcare environments. The practical implications of this work lie in offering a tamper-resistant, transparent, and patient-centric solution for medical data sharing, thereby improving trust, reducing administrative overhead, and supporting real-time healthcare services in distributed and resource-constrained settings.
The integration of renewable energy sources (RES) into modern power grids has enabled decentralized energy generation at the community level, fostering peer-to-peer (P2P) energy trading among prosumers and microgrids. Accurate forecasting of household energy consumption and photovoltaic (PV) generation is critical for optimizing energy flows, enhancing grid reliability, and enabling cost-effective trading decisions. This paper presents an intelligent energy trading platform that integrates machine learning-based forecasting, battery-aware decision-making, and blockchain-enabled transactions to facilitate secure and efficient local energy exchange. Using historical smart meter and weather data from London households, multiple forecasting models including GRU, LSTM, Random Forest, and XGBoost were trained and evaluated. The GRU model achieved superior performance in predicting energy consumption, while Random Forest produced the most accurate PV generation forecasts. These predictions were combined with household battery levels to dynamically determine next-day operational roles: Buyer, Seller, Store, or Use Battery. Unlike conventional fixed-threshold approaches, the framework supports user-defined variable battery thresholds, allowing personalized energy management strategies. The proposed decision-making model achieved an accuracy of 90.72 % for one random block, and extended simulations across 29 different random household blocks confirmed its robustness with an average accuracy of 88.69 % (95 % CI: 87.9–89.6 %). In the trading phase, households participate in a decentralized energy trading platform powered by blockchain and smart contracts. Based on the next-day forecasts, a linear programming-based optimization algorithm matches buyer requests and seller offers to minimize the total system cost while ensuring fairness and efficient energy allocation. To assess its performance, the proposed optimization approach was compared against a greedy matching algorithm where sequential matching is done without a cost optimization and a grid baseline scenario where no storage/sharing of energy takes place. The optimized matching consistently achieved substantially lower trading costs across all households demonstrating superior efficiency, fairness, and scalability compared to the benchmark methods. All transactions are executed securely and transparently on the blockchain through Ethereum-based smart contracts, which automate energy trading, pricing, and settlement. A user-friendly web interface was developed to allow participants to monitor and interact seamlessly with the platform. Overall, this battery-aware, community-driven trading framework showcases how intelligent energy forecasting, cost-optimized decision-making, and blockchain-enabled trading can collectively enhance energy autonomy, cost savings, and renewable energy utilization at both the household and community levels.
Dileep Kumar Murala, Shadab Ahmad, V. A. Sankar Ponnapalli, Veera Ankalu Vuyyuru · 5 authors
Wireless sensor networks (WSNs) will be necessary for the next generation of Internet of Things (IoT) apps. They make it possible to use smart and long-lasting sensors and smart automation in healthcare, Industry 4.0, and critical infrastructure. But security is particularly hard since they have built-in flaws, not enough computer power, not enough energy, and a significant danger of insider threats. Standard encryption methods aren't enough, and in situations where resources are restricted, heavier blockchain or machine learning solutions aren't always possible. This study presents ChainShieldML, a lightweight hybrid security architecture that combines Blockchain (BC) and machine learning (ML) to provide decentralised, adaptive, and resource-efficient protection for wireless sensor networks (WSNs). The idea is based on a two-pronged defence strategy. The Blockchain Prevention Module's permissionless blockchain architecture for base stations and cluster heads makes it possible to verify identities, maintain trust in a decentralised way, and keep node interactions unchangeable. Smart contracts made in solidity and connected to the Ethereum ecosystem make it possible to safely register nodes and keep an eye on what they do. The VBFT consensus algorithm makes it possible to quickly validate without using as much computing power as most proof of work methods. The machine learning detection module uses the lightweight gradient boosting method (LightGBM) to find and rank dangerous nodes in real time. LightGBM is the best machine learning classifier when looking at things like recall, F1-score, Matthews correlation coefficient, training cost, and inference latency. ChainShieldML dramatically improves the detection of insider attacks, builds trust, and protects data while using very little energy and having very little communication delay, as shown in tests. For Wireless sensor networks (WSNs) to keep working, all of these things are very important. ChainShieldML is a novel solution to keep IoT devices safe. It uses blockchain's decentralised trust and ML's adaptive intelligence to make a defence system for next-generation wireless sensor networks that can grow, is strong, and is ready for the future.
This study investigates how smart contract-driven queue management can be utilized to increase online ticket purchasing efficiency via blockchain technology. The system is designed to manage ticket purchase queues transparently and securely, using smart contracts written in the Solidity programming language and the Ionic UI framework. In addition, the system is connected with MetaMask as a transaction wallet, allowing users to purchase tickets directly and securely. Ganache serves as a testing environment for replenishing wallet balances without involving real transactions. The First In First Out (FIFO) approach is used to manage the transaction queue, with the first purchased ticket being processed first by the administrator. The administrator accepts each transaction, which is then confirmed by MetaMask. When the transaction is confirmed, the system automatically updates the ticket status. The implementation results show that this system effectively optimizes ticket transaction management transparently and securely. This work also makes a significant contribution to the application of blockchain technology for better management of online ticket purchasing systems, as well as minimizing the possibility of transaction errors and fraud.
Blockchain networks face a critical but understudied threat: wave attacks that exploit difficulty adjustment algorithms through strategic mining participation. Adversaries cyclically withdraw and re-enter mining to create oscillations that degrade network liveness and destabilize honest miners’ revenue. We present the first production-ready framework that maintains network responsiveness while enabling robust, post hoc threat detection. The framework employs a statistically rigorous pipeline featuring controller-aligned anomaly detection, transitive collusion grouping via union-find, and Benjamini–Hochberg False Discovery Rate control. We formally prove the economic viability of this architecture: when penalties on unvested rewards are enabled by governance, wave attacks become asymptotically unprofitable for rational adversaries. Evaluated on a 128-node distributed testbed simulating Bitcoin, Ethereum Classic, and Monacoin networks over 30 independent runs, our framework achieves 92.7% F1-score in detecting attacks, significantly outperforming baseline methods (74.7%). This work provides a complete, theoretically-grounded solution for securing proof-of-work blockchains against difficulty manipulation, forming the foundation for the adaptive AI-driven enhancements presented in our companion paper (Part II).
This paper presents a ubiquitous, blockchain-based system designed to improve transparency, traceability and trust in supply chains involving autonomous vehicles (AVs). The framework integrates Internet of Things (IoT) sensors, radio-frequency identification (RFID) and QR identifiers, global positioning system (GPS) tracking, and mobile communications with smart contracts implemented on the Ethereum 2.0 blockchain. The main contributions are as follows: (1) an architecture enabling real-time monitoring and automated verification of logistics transactions; (2) a proof of concept integrating blockchain, the IoT and Android-based OBUs; and (3) a quantitative analysis of gas and smart contract execution costs. Experimental tests show gas consumption ranging from 21,000 to 5,000,000 units and transaction costs ranging from 0.0001 to 0.0033 ETH, confirming the system’s technical feasibility and cost-efficiency. As well as cost and efficiency, the process improved transparency, real-time traceability and decentralized verification, confirming the system’s efficacy for supply chains involving autonomous vehicles.
An important cryptographic mechanism that guarantees confidentiality (the zero-disclosure property) and ensures that it is impossible to prove a false statement to the verifier is zero-disclosure proofs. A popular implementation of zero-disclosure proofs is short, noninteractive proofs that can be quickly verified and that do not require interaction between the parties after the initial setup. The main direction in the development of modern proof systems is interactive proof, which is built in two steps. The first is sending a confirmation of the polynomial of an interactive oracle proof and the second is creating correct oracles of the polynomial commitment scheme using well-defined cryptographic methods for evaluating polynomials. Verifying the use of the same coefficients in each linear combination requires checking both polynomial consistency and variable consistency. To construct general schemes of concise non-interactive zerodisclosure knowledge argument, an interactive oracle proof polynomial was proposed that models messages as polynomial oracles. All tests are proved using polynomial commitment schemes and then evaluated with zero knowledge at a point specified by the person verifying the information. The reliability and confidentiality of all tests are based on three main categories of interactive oracle proof polynomials, namely polynomial commitment schemes with conjunction, with inner product argument and with code theory. The protocols of concise noninteractive zero-disclosure knowledge arguments are implemented through high-level programs (compilers), which are converted into an intermediate representation, i.e. a scheme defined by a system of constraints. The compilers used are divided into domain-oriented languages, embedded domain-oriented languages, and zero-knowledge virtual machines. Specialized domain-oriented hardware description languages or programming languages offer an adapted syntax for efficiently expressing constraints in arithmetic schemes. Embedded domain-oriented languages are implemented as functions in general-purpose programming languages and are oriented to the overhead schemes inherited from the embedded language. Zero-knowledge virtual machines process the opcode of the fetch-decodeexecute cycle, replicating the computation trace for general programs and generating corresponding zeroknowledge proofs. They are compatible with existing high-level programming languages and can use the features of existing compilers. Compilers are evaluated for cross- or syntactic compatibility. In general, the biggest obstacle to using non-interactive proof libraries is the lack of documentation. Standardization can help developers compare important features across libraries and establish a more consistent performance baseline. Library documentation for these core features is implicit, and developers need to understand the underlying cryptographic techniques to choose an appropriate scheme. Standardization of compiler options is important, making it difficult to reuse existing tools.
Third-party risk management (TPRM) reaches an inflection point, with artificial intelligence (AI) capabilities meeting pressing demands for real-time vendor risk oversight of increasingly complex digital ecosystems. Conventional assessment methodologies resting on manual questionnaires, annual review cycles, and document-centric evaluations are poorly matched to the pace and interconnectedness driving modern technology. This article analyzes how intelligent automation is remaking basic processes in vendor governance, from optimization of questionnaires through semantic modeling to predictive monitoring allowed through continuous data synthesis. Unstructured vendor control documentation is now parsed by natural language models to extract control metadata and produce risk assessments that must be validated, rather than created, by humans. Algorithmic integrity is tackled with multi-model verification architectures that employ parallel processing pipelines where ensemble methods quantify confidence levels and flag gaps in the vendor control environment for risk subject matter expert review. Brain-inspired computing principles underpin system design, with hierarchical feature extraction possible, along with adaptive learning from assessment outcomes. Technical debt becomes a critical governance factor, particularly in the context of data dependencies and configuration management across model lifecycles. Explainable artificial intelligence provides transparency that is vital to regulatory recognition, allowing risk officers to trace decision pathways and understand feature attributions underlying automated recommendations. Convergence of distributed ledger technology with intelligent risk systems unlocks opportunities for tamper-proof audit trails and privacy-preserving attestations in support of cross-organizational governance frameworks framed by emerging digital resilience mandates.
Abstract This study explores the dynamic volatility spillovers and interconnectedness between cryptocurrency and traditional futures markets. Using a multi-method approach that integrates wavelet coherence analysis, TVP-VAR connectedness, and DCC-GARCH modeling, the research identifies notable shifts in spillover patterns during crises, such as the COVID-19 pandemic, the FTX collapse, and the Russia-Ukraine conflict. The results reveal that the correlations between Bitcoin futures and traditional asset classes depend on the market conditions and intensify during crises. The connectedness analysis shows that Bitcoin futures play a dual role, acting as a transmitter of long-term shocks and a receiver of short-term shocks during periods of crisis. Equity futures emerged as the primary long-term transmitters of shocks, whereas other assets acted as shock receivers during the pandemic. Furthermore, the study evaluates hedge ratios and portfolio weights using the DCC-GARCH model. The portfolio analysis reveals that Bitcoin futures require a minimal allocation within diversified portfolios, suggesting their limited effectiveness as a hedge and safe-haven asset. These results aim to inform portfolio managers in developing efficient hedging strategies and assist regulators in monitoring financial market stability. This study fills gaps in the existing literature by understanding how decentralized financial instruments interact with financial markets and providing insights into risk management in modern markets.
Ziqiao Kong, Xia, Wanxu, Zhengwei Li, Yi Lu · 9 authors
Move is a resource-oriented programming language designed for secure and verifiable smart contract development and has been widely used in managing billions of digital assets in blockchains, such as Sui and Aptos.Move features a strong static type system and explicit resource semantics to enforce safety properties such as the prevention of data races, invalid asset transfers, and entry vulnerabilities. However, smart contracts written in Move may still contain certain vulnerabilities that are beyond the reach of its type system. It is thus essential to validate Move smart contracts. Unfortunately, due to its strong type system, existing smart contract fuzzers are ineffective in producing syntactically or semantically valid transactions to test Move smart contracts. This paper introduces the first fuzzing framework, Belobog, for Move smart contracts. Belobog is type-aware and ensures that all generated and mutated transactions are well-typed. More specifically, for a target Move smart contract, Belobog first constructs a dependency graph based on Move's type system, and then generates or mutates a transaction based on the graph trace derived from the dependency graph. In order to overcome the complex checks in Move smart contracts, we further design and implement a concolic executor in Belobog. We evaluated Belobog on 109 real-world Move smart contract projects. The experimental results show that Belobog is able to detect 100% critical and 79% major vulnerabilities manually audited by human experts. We further selected two recent notorious incidents in the Move ecosystem, i.e., Cetus and Nemo. Belobog successfully reproduced full exploits for both of them, without any prior knowledge. Moreover, we applied Belobog on three ongoing auditing projects and found 2 critical, 2 major, and 3 medium new vulnerabilities, all acknowledged by the project developers.
Xiang, Yuexin, Yang Lei, Yuanzhe Zhang, Qin Wang · 7 authors
Stablecoins such as USDT and USDC aspire to peg stability by coupling issuance controls with reserve attestations. In practice, however, transparency remains fragmented across heterogeneous data sources, with key evidence about circulation, reserves, and disclosure dispersed across records that are difficult to connect and interpret jointly. We introduce a large language model (LLM)-based automated framework for bridging cross-domain transparency in stablecoins by aligning issuer disclosures with observable circulation evidence. First, we propose an integrative framework using LLMs to parse documents, extract salient financial indicators, and semantically align reported statements with corresponding market and issuance metrics. Second, we integrate multi-chain issuance records and disclosure documents within a model context protocol (MCP) framework that standardizes LLM access to both quantitative market data and qualitative disclosure narratives. This framework enables unified retrieval and contextual alignment across heterogeneous stablecoin information sources and facilitates consistent analysis. Third, we demonstrate the capability of LLMs to operate across heterogeneous data domains in blockchain analytics, quantifying discrepancies between reported and observed circulation and examining their implications for transparency and price dynamics. Our findings reveal systematic gaps between disclosed and verifiable data, showing that LLM-assisted analysis enhances cross-domain transparency and supports automated, data-driven auditing in decentralized finance (DeFi).
Penelitian ini bertujuan untuk menganalisis konsep al-milkiyyah (kepemilikan) terhadap aset digital Non-Fungible Token (NFT) dalam perspektif Ushul Fiqh. Fokus kajian ini adalah menentukan status hukum kepemilikan serta keabsahan transaksi NFT dalam konteks hukum Islam kontemporer. Penelitian ini menggunakan metode hukum normatif dengan pendekatan konseptual, normatif, dan sektoral. Data dikumpulkan melalui studi pustaka terhadap literatur fikih klasik, fatwa ulama kontemporer, serta analisis terhadap karakteristik teknologi blockchain. Data sekunder yang diperoleh dianalisis secara kualitatif dan komparatif untuk mengkaji kesesuaian NFT dengan kriteria harta (mal) dalam fikih muamalah. Hasil penelitian menunjukkan bahwa NFT dapat dikategorikan sebagai harta yang sah secara hukum menurut sebagian ulama kontemporer karena memiliki nilai ekonomi, manfaat yang diakui pasar, dan dapat dimiliki secara eksklusif walaupun tidak berwujud fisik. Namun demikian, keabsahan transaksi NFT bersifat bersyarat. Transaksi dinilai sah apabila objek NFT halal, bebas dari unsur gharar (ketidakjelasan) dan maysir (perjudian), serta menggunakan alat pembayaran yang sesuai prinsip syariah. Penggunaan mata uang kripto yang tidak memenuhi ketentuan syariah dapat membatalkan transaksi. Dengan demikian, NFT dapat diakui sebagai bentuk kepemilikan modern dalam Islam dengan tetap memperhatikan prinsip kehati-hatian, perlindungan harta (hifzh al-māl), dan pencegahan kerusakan (mafsadah). Kata Kunci: Al-milkiyyah; Kepemilikan; NFT; Ushul Fiqh; Fikih Muamalah; Aset Digital