The rapid advancement of generative AI systems has collapsed the credibility landscape for photographic evidence. Modern image generation models produce photorealistic images undermining the evidentiary foundation upon which journalism and public discourse depend. Existing authentication approaches, such as the Coalition for Content Provenance and Authenticity (C2PA), embed cryptographically signed metadata directly into image files but suffer from two critical failures: technical vulnerability to metadata stripping during social media reprocessing, and structural dependency on corporate-controlled verification infrastructure where commercial incentives may conflict with public interest. We present the Birthmark Standard, an authentication architecture leveraging manufacturing-unique sensor entropy from non-uniformity correction (NUC) maps and PRNU patterns to generate hardware-rooted authentication keys. During capture, cameras create anonymized authentication certificates proving sensor authenticity without exposing device identity via a key table architecture maintaining anonymity sets exceeding 1,000 devices. Authentication records are stored on a consortium blockchain operated by journalism organizations rather than commercial platforms, enabling verification that survives all metadata loss. We formally verify privacy properties using ProVerif, proving observational equivalence for Manufacturer Non-Correlation and Blockchain Observer Non-Identification under Dolev-Yao adversary assumptions. The architecture is validated through prototype implementation using Raspberry Pi 4 hardware, demonstrating the complete cryptographic pipeline. Performance analysis projects camera overhead below 100ms and verification latency below 500ms at scale of one million daily authentications.
Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, trained collaboratively across multiple retailers without direct data sharing. Our preliminary results show that FL models have performance almost equivalent to the ideal setting in which parties share data with each other, and are notably superior to models built by individual parties without sharing data, cutting waste and boosting efficiency.
Do cryptocurrency markets process infrastructure failures differently from regulatory shocks? We study both moments of the return distribution on one shared sample (50 events, six assets, 2019-2025), fitting a GJR-GARCH-X model under matched dependence-robust inference. We treat event inclusion as a measured design parameter: rather than asserting the selection-on-the-dependent-variable objection away, we trace the variance differential across the inclusion screen and measure the selection bias directly. The result is a scope condition -- under curated, high-salience identification the differential is sizeable ($4.88\times$) but selection-conditional: a mechanical impact filter on a broad reconstructed pool collapses it to $1.3$-$1.6\times$. Identification is half the story; inference is the other. The curated multiplier is not distinguishable from zero once cross-asset dependence and heavy tails are respected: a Student-$t$-copula CCC-GARCH-X bootstrap (our inference of record) returns $p \approx 0.32$, and because the six per-asset coefficients are strongly cross-correlated the contrast's effective sample size is nearer three than six (design-effect $p \approx 0.07$-$0.15$). A naive i.i.d. test had reported an apparently decisive fivefold effect, but that significance was an artefact: pseudoreplication across correlated assets compounded by a heavy-tail-misspecified bootstrap. The first moment tells the same story -- a $+7.19$ pp cumulative-abnormal-return difference a block bootstrap cannot distinguish from zero ($p = 0.283$). Under correct inference the asymmetry is directional but unresolved. The contribution is a portable inference toolkit -- an inference ladder and a Monte-Carlo size study -- for diagnosing how cross-asset event studies in heavy-tailed markets manufacture significance, demonstrated where it dissolves a fivefold result the author had himself published.
Aqsa Rashid, Raja Wasim Ahmad, Mirna Nachouki, Atta Ur Rehman Khan
Ensuring food safety and traceability in fruit supply chains (FSC) remains a critical concern, as traditional centralized methods often suffer from data manipulation, lack of transparency, and delayed responses during contamination events. These challenges lead to reduced consumer trust and inefficiencies in monitoring product integrity throughout the supply network. To address these limitations, this paper presents a blockchain-based framework that leverages cryptographic protocols and smart contracts to secure, automate, and validate traceability processes across all stages of the fruit supply chain. The proposed FSC_SDG system enforces trusted data recording, real-time provenance verification, and autonomous policy execution, while aligning with the United Nations Sustainable Development Goals (UN-SDGs). A proof-of-concept prototype was implemented on the Ethereum blockchain to assess performance. Experimental evaluations demonstrate reduced latency in traceability verification, improved data integrity, and enhanced resistance to tampering compared with existing approaches. These results confirm the effectiveness of the proposed framework in strengthening food safety, transparency, and trust within fruit supply chains.
The centrosome, classically defined as the primary microtubule-organizing center of the animal cell, is here reconceptualized as a critical organelle for non-genomic cellular memory. We propose the Centrosomal Ledger hypothesis, which posits that the mother centriole encodes a high-dimensional molecular state vector. This distributed memory integrates proteomic composition, post-translational modification (PTM) landscapes, and macromolecular stoichiometries accumulated over a cell’s history. Rather than being a passive structural hub, the centrosome actively utilizes this integrated record to guide future cell fate decisions, such as the choice between symmetric and asymmetric division. Crucically, this hypothesis is untestable by traditional bulk-cell molecular biology, as it requires the discrimination of centriole-age-specific molecular signatures. Its rigorous falsification necessitates centrosome-resolved, multi-omic approaches. Furthermore, we argue that dysregulation of this ledger—through corruption or erosion—constitutes a fundamental mechanistic axis underlying oncogenic transformation, where fate instruction is scrambled, and age-associated stem cell decline, where instructive fidelity is lost. This reframing of the centrosome from a cytoskeletal architect to an information-processing device opens novel translational avenues for diagnosing and treating cancer and degenerative diseases by targeting organelle memory.
Background Cross-domain federated learning is an innovative machine learning paradigm that allows data owners from different domains to collaboratively train a shared model while preserving data privacy. However, cross-domain federated learning also faces numerous challenges, such as data and system heterogeneity, client reputation management, and potential threats from malicious attackers. Methods To address these issues, this article proposes a secure cross-domain federated learning scheme based on blockchain fair payment. The proposed scheme effectively evaluates and updates the reputation of each client through a reputation management mechanism and allocates fair rewards based on their contributions. Additionally, the scheme employs advanced cryptographic technologies such as blockchain and zero-knowledge proofs to ensure the security and fairness of data and transactions. A series of experiments are conducted to evaluate the performance and fairness of the proposed scheme on multiple datasets and models, and comparisons are conducted with other mainstream federated learning algorithms. MNIST Dataset is available at: https://www.kaggle.com/datasets/hojjatk/mnist-dataset . Fashion-MNIST Dataset is available at https://github.com/zalandoresearch/fashion-mnist . CIFAR-10 Dataset is available at https://www.cs.toronto.edu/~kriz/cifar.html . Results The experimental results demonstrate that the proposed scheme ensures the performance of federated learning while also maintaining its fairness and security. Specifically, the method achieves a test accuracy of 97% on the MNIST dataset, outperforming Federated Averaging (FedAvg) (95%) and Stochastic Controlled Averaging for Federated Learning (SCAFFOLD) (96%). On the FEMNIST dataset, it attains 89% accuracy. In terms of convergence speed, the proposed optimization-based reputation method converges in 26 rounds, which is faster than baseline methods (28–32 rounds). Under data tampering attacks (50-client scenario), the accuracy drop is less than 3%, showing strong robustness. For fairness, the trust difference and reward difference are reduced to 0.10 and 0.08, respectively. The proposed scheme significantly improves the accuracy, convergence speed, robustness, and fairness of cross-domain federated learning, advancing its practical deployment in real-world scenarios. The experimental data is available at: https://zenodo.org/records/15210778 .
This manuscript presents a conceptual and ideological-social framework for a cryptocurrency token denoted as $Rupert (or $Rupert), positioned as an innovative fusion of decentralized finance (DeFi) mechanisms and political advocacy aligned with the policy agenda of British politician Rupert Lowe MP and his associated movement, Restore Britain.
Ensuring the reliable, auditable, and privacy-oriented distribution of donations in disaster logistics constitutes a critical challenge due to multi-stakeholder coordination difficulties and the risk of misuse. This study presents a modular architecture, named SecureRelief, operating on a permissioned Hyperledger Fabric platform. The architecture integrates authentication based on Self-Sovereign Identity (SSI), Decentralized Identifiers (DID), and WebAuthn, together with Attribute-Based Access Control (ABAC), and enables the verification of delivery evidence through privacy-preserving validation using zero-knowledge proofs (ZKP). Documents are stored off-chain on the InterPlanetary File System (IPFS), while only cryptographic summary (hash) values sufficient for integrity verification are maintained on-chain. In scenario-based laboratory experiments, the blockchain layer demonstrated low latency (p95 < 16 ms) and stable transaction throughput, confirming its scalability. While the API layer handled high burst request loads with a 0% error rate, the additional computational overhead introduced by the integrated privacy-preserving (ZKP) mechanisms kept the end-to-end transaction latency within acceptable limits for disaster management applications (3.5–4.5 s).
Decentralized storage platforms and blockchain systems offer novel opportunities for data exchange; however, they also present significant challenges in safeguarding sensitive visual information. The Interplanetary File System (IPFS) offers efficient distributed storage, but it lacks built-in confidentiality mechanisms, making additional security layers necessary. This work proposes a security-oriented framework that integrates (k,n) threshold visual cryptography (shamir secret ), LSB-based image steganography, and blockchain-based ownership management using non-fungible tokens (NFTs). Sensitive images are divided into multiple visual shares using a threshold scheme so that no useful information can be obtained unless enough shares are available. Each share is then hidden inside a cover image using a simple LSB-based steganography method and stored on IPFS. Instead of storing the data itself on the blockchain, NFTs are used only to reference the stored content and record ownership in an immutable manner. Experimental results are evaluated using common image quality and statistical metrics, including PSNR, SSIM, correlation, and entropy. With PSNR = Inf dB for all images, Entropy analysis shows that the entropy values of the original cover images are approximately 7.0865, while the entropy values of the stego-images after embedding range between 7.0907 and 7.0954, indicating only a slight increase in randomness. This minimal change confirms that the LSB-based steganographic embedding does not significantly alter the statistical properties of the cover images. The findings show that the original images can be reconstructed with acceptable visual quality while preserving the statistical characteristics of the cover images. The proposed approach demonstrates that combining visual cryptography with decentralized storage and blockchain-based ownership can offer improved confidentiality compared to direct on-chain image storage, without introducing excessive system complexity.
Open access
Advanced Steganography and Watermarking Techniques
The virtual economy has rapidly evolved alongside advances in digital technologies, including the integration of blockchain and interactive media that enable novel experiences and business opportunities. A notable development is the trading of non-fungible tokens (NFTs), where users participate as buyers, owners, sellers, and investors. This multi-role context, coupled with individual differences, adds complexity to understanding consumer motivations for trading and recommending NFTs. Focusing on NFT art as a representative type of NFTs, this study identifies 14 value dimensions from NFT technology-related, art-related, and product-related perspectives. Based on a large-scale international survey, the research examines how these value perceptions influence purchase and recommendation intention, and how these relationships are moderated by cultural factors (uncertainty avoidance and long-term orientation) and prior purchase experience. The findings indicated that product-related values exerted the strongest influence on consumer behavior, while technology-related values played a lesser role. Cultural and experiential factors showed limited moderating effects.
We present SPEAR, a multi-agent coordination framework for smart contract auditing that applies established MAS patterns in a realistic security analysis workflow. SPEAR models auditing as a coordinated mission carried out by specialized agents: a Planning Agent prioritizes contracts using risk-aware heuristics, an Execution Agent allocates tasks via the Contract Net protocol, and a Repair Agent autonomously recovers from brittle generated artifacts using a programmatic-first repair policy. Agents maintain local beliefs updated through AGM-compliant revision, coordinate via negotiation and auction protocols, and revise plans as new information becomes available. An empirical study compares the multi-agent design with centralized and pipeline-based alternatives under controlled failure scenarios, focusing on coordination, recovery behavior, and resource use.
Gradient boosted decision trees, particularly XGBoost, are among the most effective methods for tabular data. As deployment in sensitive settings increases, cryptographic guarantees of model integrity become essential. We present ZKBoost, the first zero-knowledge proof of training (zkPoT) protocol for XGBoost, enabling model owners to prove correct training on a committed dataset without revealing data or model parameters. Naively re-executing XGBoost training in ZK would incur prohibitive costs, primarily due to the oblivious partitioning of training samples and unknown tree splits. Moreover, previous work on ZKP of training and inference had subtle security issues, such as leakage of tree topology and soundness gaps allowing cheating model providers to deviate from the correct execution of training and inference. We make two key contributions to address these challenges: (1) a generic zkPoT template for XGBoost that can be instantiated with any general-purpose ZKP backend, significantly improving prover costs compared to naive re-execution of the training process; and (2) a VOLE-based instantiation that overcomes the security issues of previous ZK proofs of training at minimal costs. To maximize efficiency, we develop a fixed-point version of XGBoost, which is particularly well suited for efficient instantiation of ZKP, and show it matches standard XGBoost accuracy to within 1\% on real-world datasets.
Abstract This study examines the dynamic connectedness that the innovative natural disasters index displays with major cryptocurrencies, decentralized finance assets (DeFi) and non-fungible tokens (NFTs) during the Russia-Ukraine conflict under intense inflationary pressures. Data spanning from 14 December 2021 to 31 January 2025 and three specifications of the Quantile Vector Autoregressive (Q-VAR) methodology at lower, middle and upper quantiles are adopted. Results indicate that natural disaster uncertainty has a larger footprint on DeFi assets in bear markets but is more influential on the NFTs in bull markets. So it acts as a hedge against medium risk digital currencies when pessimism prevails and motivates for investing in riskier assets in elevated investor optimism. The Ripple, Synthetic and Gala assets are the most tightly linked with natural disasters’ sentiment. Higher levels of geopolitical and monetary uncertainties fuel the switch of investors’ decision-making criteria. This study provides valuable insights for the potential of modern cryptocurrencies to survive during crises when conventional currencies devaluate and offers a compass for monetary authorities and investors.
Open access
Market Dynamics and Volatility
Blockchain Technology Applications and Security
Environmental and Biological Research in Conflict Zones
Block space on the blockchain is scarce and must be allocated efficiently through block building. However, Ethereum's current block-building ecosystem, MEV-Boost, has become highly centralized due to integration, which distorts competition, reduces blockspace efficiency, and obscures MEV flow transparency. To guarantee equitability and economic efficiency in block building, we propose $\mathrm{Boost+}$, a system that decouples the process into collecting and ordering transactions, and ensures equal access to all collected transactions. The core of $\mathrm{Boost+}$ is the mechanism $\mathit{M}_{\mathrm{Boost+}}$, built around a default algorithm. $\mathit{M}_{\mathrm{Boost+}}$ aligns incentives for both searchers (intermediaries that generate or route transactions) and builders: Truthful bidding is a dominant strategy for all builders. For searchers, truthful reporting is dominant whenever the default algorithm dominates competing builders, and it remains dominant for all conflict-free transactions, even when builders may win. We further show that even if a searcher can technically integrate with a builder, non-integration combined with truthful bidding still dominates any deviation for conflict-free transactions. We also implement a concrete default algorithm informed by empirical analysis of real-world transactions and evaluate its efficacy using historical transaction data.
Arivarasan Karmegam, Lucianna Kiffer, Antonio Fernández Anta
Blockchain validators can reduce block processing time by exploiting multi-core CPUs, but deterministic execution must preserve a given total order while respecting transaction conflicts and per-block runtime limits. This paper systematically examines how validators can exploit multi-core parallelism during both block construction and execution without violating blockchain semantics. We formalize two validator-side optimization problems: (i) executing an already ordered block on \(p\) cores to minimize makespan while ensuring equivalence to sequential execution; and (ii) selecting and scheduling a subset of mempool transactions under a runtime limit \(B\) to maximize validator reward. For both, we develop exact Mixed-Integer Linear Programming (MILP) formulations that capture conflict, order, and capacity constraints, and propose fast deterministic heuristics that scale to realistic workloads. Using Ethereum mainnet traces and including a Solana-inspired declared-access baseline (Sol) for ordered-block scheduling and a simple reward-greedy baseline (RG) for block construction, we empirically quantify the trade-offs between optimality and runtime.
Future Tech Wisdom Research Institute of Interstellar Age (FTWRIIA) - Shuiquan System
This document presents the Haiyue AI System as the irreplaceable core underlying technical cornerstone that empowers three pivotal global initiatives—Global Social Reform, Global Unified Governance Framework, and Earth Civilization’s Fair & Free System (where everyone can be president). Designed to address the technical bottlenecks of these reform agendas, the system integrates multi-agent collaboration, quantum-secure identity authentication, adaptive evolution, intelligent resource allocation, and blockchain traceability to deliver stable, efficient, and secure technical support, ensuring the feasibility, fairness, and scalability of the reform plans. The system’s core value in supporting the three initiatives is reflected in four critical dimensions aligned with their core goals: 1) Quantum-Secure Identity & Rights Protection: Built on W3C DID/SSI standards with Dilithium-5 signature and Kyber-1024 key encapsulation, it enables tamper-proof global identity verification and interoperability—laying the technical foundation for borderless mobility, inclusive participation, and anti-corruption supervision in global unified governance; 2) Intelligent & Fair Resource Allocation: Its three-layer AI engine (assurance-optimization-learning) guarantees 99.5% basic needs satisfaction and a Gini coefficient ≤0.2, directly supporting social reform’s objectives of labor rights protection, balanced cultural industry development, and inclusive finance; 3) Transparent Governance & Supervision: Leveraging blockchain traceability and zero-knowledge proof, it realizes real-time monitoring of policy execution, fund flows, and violation detection, empowering cross-border law enforcement, whistleblower protection, and algorithmic audit in global social reform; 4) Universal Participatory Democracy: Through multi-agent consensus algorithms and AI proxy voting (supporting special groups via brain-computer interfaces), it lowers participation thresholds to achieve 100% inclusive decision-making—fulfilling the "everyone can be president" vision of the fair & free system. Validated through rigorous reproducible experiments (successfully upgraded to L3, zero-fusion latency 76.81ms, agent success rate 97.6%), the system supports phased rollout of the three reform plans—from small-scale pilots to global deployment. As the technical backbone integrating efficiency, fairness, and security, it bridges abstract reform visions with practical implementation, turning goals of social equity, unified governance, and universal democracy into actionable reality.
this study rigorously scrutinizes the revolutionary impact of artificial intelligence (AI) on banking organizations within India. It definitively analyses the transformative effects of AI on the Indian financial industry by reviewing pertinent literature, compelling case studies, and empirical data. The paper first establishes the major ways. AI unequivocally alters the financial sector. It then details how Indian Banking institutions effectively deploy AI across critical areas such as customer service, algorithmic trading, risk management, fraud detection, credit scoring, and regulatory compliance. The integration of AI into India’s financial ecosystem is highlighted through examples from major banks, fintech companies, and regulatory agencies, showcasing the methods used and the outcomes achieved. Furthermore, this study explores the impacts and challenges associated with AI implementation in the Indian banking industry [14]. It delves into the cultural factors, current regulations, data availability, talent acquisition, and regulatory frameworks that shape the application of AI in Indian banks. The combination of Decentralized finance and AI offers a revolutionary partnership that might completely change the sector, increase its flexibility, and lay the foundation for long-term viability. In recent years, AI and Decentralized finance have become prominent advances in technology that have attracted a lot of interest and acceptance. In conclusion, this study comprehensively analyses AI's effects on India’s banking sector. This research paper is based on secondary data with the help of various journal and websites. Researcher paper benefits to many Policymakers, practitioners, and scholars will find invaluable insights contributing to the growing literature on technology-driven transformations. The recommendations provided will enable stakeholders to effectively harness AI’s capabilities while proactively addressing inherent risks and challenges, thereby enhancing the resilience, efficiency, and customer-centric focus of financial institutions in India and ensuring their competitiveness in an increasingly digital landscape. This research highlights the need to adopt a-worthy strategies for the prevention of active fraud, eventually contributes to the integrity of financial systems.
Open access
Innovations and Analysis in Business and Education
Penelitian ini bertujuan untuk menganalisis pengaruh harga Bitcoin, Ethereum, indeks S&P 500, dan emas terhadap volatilitas harga Xrp. Xrp sebagai salah satu aset kripto dengan kapitalisasi pasar besar menunjukkan tingkat volatilitas yang tinggi, sehingga penting untuk memahami faktor-faktor eksternal yang memengaruhi pergerakan volatilitasnya. Penelitian ini menggunakan pendekatan kuantitatif dengan data sekunder berbentuk time series. Data yang digunakan meliputi harga Bitcoin, Ethereum, S&P 500, emas, serta harga Xrp yang diperoleh dari sumber terpercaya seperti Investing.com dan Coinglass selama periode pengamatan tertentu. Volatilitas harga Xrp dianalisis menggunakan model Multivariate Generalized Autoregressive Conditional Heteroskedasticity untuk menangkap karakteristik volatilitas yang bersifat time-varying, clustering, serta keterkaitan volatilitas antar aset. Hasil penelitian menunjukkan bahwa harga Bitcoin berpengaruh signifikan terhadap volatilitas harga Xrp, yang mengindikasikan adanya keterkaitan volatilitas yang kuat antara kedua aset kripto tersebut. Sementara itu, harga Ethereum, indeks S&P 500, dan emas tidak menunjukkan pengaruh signifikan terhadap volatilitas harga Xrp. Temuan ini mengindikasikan bahwa volatilitas Xrp lebih sensitif terhadap dinamika pergerakan Bitcoin dibandingkan dengan aset kripto lainnya maupun aset keuangan tradisional. Penelitian ini memberikan implikasi penting bagi investor dan pelaku pasar dalam pengambilan keputusan investasi, khususnya dalam mengelola risiko pada aset kripto. Selain itu, hasil penelitian ini diharapkan dapat menjadi referensi bagi penelitian selanjutnya terkait keterkaitan volatilitas antar aset kripto dan integrasinya dengan pasar keuangan global
Personal Health Records (PHRs) enable personalized and continuous healthcare services, but contain highly sensitive information, requiring strong security and privacy safeguards. Self-sovereign architectures, where individuals retain full control over their data, represent a promising model for secure PHR sharing. In our prior work, we implemented a blockchain-based system using Non-Fungible Tokens (NFTs) to represent data ownership and usage rights. While NFTs provide tamper resistance, NFT-only access control is vulnerable to wallet compromise and requires explicit user consent, making it unsuitable for emergency access when patients are unconscious or otherwise unable to consent. To address these limitations, we newly propose a hybrid PHR-sharing framework combining NFTs with Attribute-Based Encryption (ABE). Our new approach enforces cryptographic access policies beyond NFT possession and enables emergency access to predefined medical information without explicit user consent. We analyze representative attack scenarios and show that the scheme provides secure access control and rights management. We implement a prototype and evaluate its performance. For 1 MB of data, used as a practical upper bound for text-based PHR records based on wearable-device measurements, retrieval takes approximately 1 second, while registration and access granting take approximately 12 and 6 seconds on the Base testnet, a high-speed Ethereum-compatible test network. These results demonstrate practical feasibility, with further optimization possible through faster blockchain networks or reduced blockchain transactions.
Large language models (LLMs) have been widely adopted in modern software development lifecycles, where they are increasingly used to automate and assist code generation, significantly improving developer productivity and reducing development time. In the blockchain domain, developers increasingly rely on LLMs to generate and maintain smart contracts, the immutable, self-executing components of decentralized applications. Because deployed smart contracts cannot be modified, correctness and security are paramount, particularly in high-stakes domains such as finance and governance. Despite this growing reliance, the security implications of LLM-generated smart contracts remain insufficiently understood. In this work, we conduct a systematic security analysis of Solidity smart contracts generated by state-of-the-art LLMs, including ChatGPT, Gemini, and Sonnet. We evaluate these contracts against a broad set of known smart contract vulnerabilities to assess their suitability for direct deployment in production environments. Our extensive experimental study shows that, despite their syntactic correctness and functional completeness, LLM-generated smart contracts frequently exhibit severe security flaws that could be exploited in real-world settings. We further analyze and categorize these vulnerabilities, identifying recurring weakness patterns across different models. Finally, we discuss practical countermeasures and development guidelines to help mitigate these risks, offering actionable insights for both developers and researchers. Our findings aim to support safe integration of LLMs into smart contract development workflows and to strengthen the overall security of the blockchain ecosystem against future security failures.
Jiaqi Gao, Zijian Zhang, Yuqiang Sun, Ye Liu · 8 authors
Business logic vulnerabilities have become one of the most damaging yet least understood classes of smart contract vulnerabilities. Unlike traditional bugs such as reentrancy or arithmetic errors, these vulnerabilities arise from missing or incorrectly enforced business invariants and are tightly coupled with protocol semantics. Existing static analysis techniques struggle to capture such high-level logic, while recent large language model based approaches often suffer from unstable outputs and low accuracy due to hallucination and limited verification. In this paper, we propose LogicScan, an automated contrastive auditing framework for detecting business logic vulnerabilities in smart contracts. The key insight behind LogicScan is that mature, widely deployed on-chain protocols implicitly encode well-tested and consensus-driven business invariants. LogicScan systematically mines these invariants from large-scale on-chain contracts and reuses them as reference constraints to audit target contracts. To achieve this, LogicScan introduces a Business Specification Language (BSL) to normalize diverse implementation patterns into structured, verifiable logic representations. It further combines noise-aware logic aggregation with contrastive auditing to identify missing or weakly enforced invariants while mitigating LLM-induced false positives. We evaluate LogicScan on three real-world datasets, including DeFiHacks, Web3Bugs, and a set of top-200 audited contracts. The results show that LogicScan achieves an F1 score of 85.2%, significantly outperforming state-of-the-art tools while maintaining a low false-positive rate on production-grade contracts. Additional experiments demonstrate that LogicScan maintains consistent performance across different LLMs and is cost-effective, and that its false-positive suppression mechanisms substantially improve robustness.
Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He
Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure-FM, the first time-series, graph foundation model for measuring and forecasting inter-protocol credit exposure on DeFi networks, to the best of our knowledge. Employing a graph-tabular encoder, with pre-trained weight initialization, and multiple task-specific heads, DeXposure-FM is trained on the DeXposure dataset that has 43.7 million data entries, across 4,300+ protocols on 602 blockchains, covering 24,300+ unique tokens. The training is operationalized for credit-exposure forecasting, predicting the joint dynamics of (1) protocol-level flows, and (2) the topology and weights of credit-exposure links. The DeXposure-FM is empirically validated on two machine learning benchmarks; it consistently outperforms the state-of-the-art approaches, including a graph foundation model and temporal graph neural networks. DeXposure-FM further produces financial economics tools that support macroprudential monitoring and scenario-based DeFi stress testing, by enabling protocol-level systemic-importance scores, sector-level spillover and concentration measures via a forecast-then-measure pipeline. Empirical verification fully supports our financial economics tools. The model and code have been publicly available. Model: https://huggingface.co/EVIEHub/DeXposure-FM. Code: https://github.com/EVIEHub/DeXposure-FM.
The development of blockchain technology has led to the emergence of a novel form of collaborative organization, known as Decentralized Autonomous Organizations (DAOs), which rely on internet-based communication and cryptographic mechanisms. The economic significance of DAOs has prompted legislators to consider appropriate legal frameworks. This article analyzes the legal status of DAOs in the European Union and the Republic of Armenia. While the EU adopted the Markets in Crypto-Assets Regulation (MiCA), it refrained from recognizing DAOs as distinct legal entities, despite preliminary considerations during the legislative process. Similarly, Armenia, through the Law on Crypto-Assets (HO-159-N), inspired by MiCA, does not explicitly address DAOs. Consequently, both jurisdictions exhibit a regulatory gap. The article demonstrates that, even in the absence of dedicated legislation, interpretative cues within these legal instruments can provide guidance on how DAOs may be treated under EU and Armenian law. By examining these frameworks, the study contributes to understanding the potential legal recognition and regulation of DAOs in different legal systems.
Abstract. The development of technologies for creating combat drones from civilian drones, the expansion of the practice of using these drones in ongoing military conflicts of varying intensity, as well as the development of artificial intelligence (AI) systems and the possibility of various combinations of AI with combat drones, constitute an already occurring, not yet fully understood, global security challenge that is dangerous for any existing country. The paper also examines the problem of an individual customer outsourcing the commission of an act of revenge or a terrorist attack to individual perpetrators, groups of perpetrators, as well as to AI systems that act autonomously using telecommunications networks, such as the Internet, robotics, and that conduct financing using cryptocurrencies. The security threats discussed here, in the context of the emergence of UAVs and other combat-purpose drones in private hands – supplied both from active armies and manufactured independently – represent a dual combination of threats to the established world order and opportunities for society. And it is clear that the security threat is not some ephemeral threat to the security of some ordinary voter, whose life and fate do not, in reality, interest anyone from the ruling stratum at all. The real security threat is the threat to the life, health, capital, and power of the stratum that rules society, as well as the risk that the service personnel of this stratum – in the form of intelligence services, security and judicial bodies, as well as the legislative branch – will be afraid to carry out the orders given to them, both those involving blatant violations of the law and those involving its simulated enforcement, aimed at continuing the exploitation of society under the guise of observing the constitution and other laws, conducting “a dog-and-pony version of democracy.” The other side of the coin manifests itself as “frontier justice” – the ability for an ordinary person to defend their violated rights even when the violator has an overwhelming advantage in the form of administrative, judicial, and financial resources. It should be taken into account that modern technologies – not only the combination of outsourcing with the use of public computer networks (Public Data Networks, PDN) to commit a crime, or the possibility of direct remote control of a drone, but also the possibility of using a drone with built-in AI deliberately trained to strike a target – are merely the tip of the iceberg that the “Titanic” of the established security system will collide with. Further technological development, in particular decentralized AI using Web 3.0 / Web3, will make it possible to use AI as the executor of a deceased person’s will, while transferring to the AI the necessary financial resources in cryptocurrency (including programming the AI to further criminal acquisition of funds for its activities), combined with the ability to use fab labs or to have the AI itself hire contractors, creates for the targets of an attack aimed by such an AI a situation of the inevitability of retribution. At the same time, these capabilities can be extrapolated to any life situations – for example, those involving deprivation of liberty, such as in connection with the abduction of any person following the example of the abduction of N. Maduro, or situations such as bankruptcy resulting from the bad-faith actions of counterparties. At the same time, the risk of retribution in the process of defending violated rights affects both rank-and-file executors – such as police officers and judges – and the real masters of the country in the form of the public and non-public elite. The latter situation – the threat to the lives of the elite – already appears to be a real problem requiring a solution. After all, it would be extremely painful for the ruling strata of countries that have fought wars and then reconciled – the main beneficiaries of the past war – to answer to the victims for crimes committed both during the war and during mobilization, even if the terms of peace provide for full amnesty. An absolutely unfamiliar sense of danger will also emerge among the ruling strata governing states that ignite wars and create crises, since they now find themselves in a vulnerable position. This situation is further aggravated by the fact that information – both factual and conspiracy theories – is now widely accessible and can serve as grounds for attacks on representatives of well-known families, both by informed individuals and by mentally ill people. Would the issue of depriving Denmark of Greenland even be on the agenda now if, during the 2024 assassination attempts on D. Trump (AP News, 2025; Reuters, 2025), terrorists had used not firearms but a group of fiber-optic drones with centralized AI trained to recognize its target? The third side of the coin will be the need to minimize offenses in society and to introduce mechanisms of genuine democracy and accountability of the authorities for the results of their activities, when the overwhelming majority of the population is involved in decision-making – from ensuring the functioning of a city district to the election of sheriffs, judges, prosecutors, and all the way to voting on draft laws as well as federal elections (see the experience of Switzerland). This system will make it possible to reduce the number of legal violations by representatives of the ruling strata and to hold them accountable for both past and ongoing crimes without the need for extrajudicial reprisals by private individuals. Concluding the enumeration of the main aspects of changes in public life caused by the development of private combat robotics, let us also consider the fourth side of the same coin. All the technologies and capabilities discussed can be implemented by a wide range of individuals with disturbed psyches, for example religious fanatics, as well as by criminal elements, for whom new technologies present the broadest opportunities for blackmail, robberies, and extortion. And it is precisely against such individuals that it will be necessary to create a security system of a new quality – one that does not yet exist – a security system costing hundreds of billions of euros for each country deploying it, ensuring comprehensive protection of society from new types of threats. Of course, it may seem that the development of such a security system is possible without social modernization of relations in society and without the introduction of mechanisms of real democracy. It may seem that the implementation of a police state based on a digital concentration camp is more preferable. Perhaps – but this would require conducting an experiment, for example following the model of Pakistan or the DPRK, where the ruling military or party elite lives isolated from the main part of the population. In doing so, the ruling stratum would have to survive under new conditions of total war with its own population, from whom, for the sake of “security,” absolutely all remaining freedoms would be taken away, following the example of the DPRK. The application of AI that can operate in our world after the death of the person for whom the AI serves as executor proves that the empirical rule “you can’t take your money with you” is gradually losing its meaning: AI or artificial consciousness (AC) makes it possible to practically and almost inevitably implement the will of either the deceased or, say, a person who has been imprisoned or kidnapped, as well as someone who has found themselves in other situations that limit their ability to act. In this regard, the next customer ordering the next kidnapping of N. Maduro will think very hard about whether it is worth dying from retaliatory actions by AI, or from the actions of an actor who has decided that the triggering event for the AI’s predefined action cycle has occurred. At the same time, an actor in the form of decentralized AI cannot be intimidated, bought, or destroyed. In effect, new technologies put at the disposal of private individuals and organizations what previously only states had at their disposal, in particular an analogue of a system like “Perimeter” (RVSN RF index 15E601, known in journalism as “Dead Hand”) (Stilwell, 2022). Of course, the AI (or AC) systems discussed above – first and foremost decentralized AI, designed so that they cannot be influenced or have their operating order changed either by shutdown or by blackmail involving the risk of shutdown – may also inherently carry socially constructive tasks. Already now, AI systems can function as independent and autonomous executors, even though they still contain certain built-in technological limitations. Even this, however, already makes it possible to use such systems effectively both as operational AI assistants and as systems for auditing human decisions for compliance with specified goals and/or means (Gudkov, 2020; Cowger, 2023; Li, 2024; Bell, 2025; Brennan, 2025; Brown, 2025). Here and throughout, wherever AI is discussed, the possibility of using an IS is also implied – one that differs from AI by the presence of a software equivalent of will. The rate at which AI systems operating in the PDN evolve into IS systems also operating in the PDN is not considered here, nor is the time it takes for laboratory IS systems to enter the PDN. And, as practice shows, innovations are primarily directed toward the sphere of committing crimes – for example, the elimination of undesirable individuals – an activity engaged in both by independent criminals, such as roaming bandits, and by the intelligence services and ministries of defense of stationary bandits – states. In this regard, although the fully robotic technologies discussed above, which do not involve human intervention in their operation from the moment of launch, can also be used for constructive activities, their priority emergence in cri
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