Automated market makers (AMMs) quote prices from pool state rather than from a limit order book. AMM pools often stay close to a reference price because arbitrageurs correct profitable mispricing. A large part of decentralized finance therefore relies on a simple economic premise: once the AMM price drifts away from the reference price, arbitrage incentives push it back. This paper studies when that premise is strong enough to guarantee block-scale stability. We model the gap between the reference price and the AMM price as a stochastic tracking error, treat arbitrage as the corrective input, and place blockchain execution inside the loop through fees, discrete blocks, transaction ordering, delays, and transaction failure. The detailed execution layer is reduced to the total successful correction confirmed in each block. Under a block-level correction condition, we prove geometric ergodicity of the tracking error and obtain explicit one-step bounds that connect tracking quality to liquidity and execution quality. We also show in a constant-product example how fees, fixed execution costs, and local liquidity map into the no-trade band and the optimal corrective trade. Finally, we build empirical proxies for the theorem quantities from realized block data and use them to organize reduced and mechanism-focused simulations whose comparative statics are consistent with the theory. The contribution is to turn a basic economic intuition behind decentralized finance into a quantitative stability statement together with a tractable calibration interface.
Introduction: The integration of blockchain technology with Internet of Things (IoT) devices in smart city deployments presents significant technical challenges, particularly regarding consensus mechanism design. Traditional blockchain consensus protocols such as Proof of Work (PoW) and standard Proof of Stake (PoS) impose computational, energy, and latency requirements that exceed the capabilities of resource-constrained IoT devices, motivating the development of lightweight alternatives. Materials and methods: This paper examines lightweight consensus mechanisms specifically designed for edge-optimised blockchain deployments, evaluates hybrid on-chain/off-chain architectural patterns, and presents a comparative analysis of emerging solutions. We propose four research hypotheses (H1–H4) regarding hierarchical consensus performance and validate them through extensive discrete-event simulation across five smart city domains, using a 500-node testbed calibrated to representative urban workloads. A novel Business Process Model and Notation (BPMN)-based process model formalises the three-tier consensus workflow, and security analysis quantifies Byzantine fault tolerance guarantees. Results: The proposed three-tier framework achieves a 94.7% ± 2.3% reduction in on-chain transactions while maintaining cryptographic auditability, with consensus latency under 500 ms for district-level operations (p < 0.001 vs. single-tier baseline). District-level throughput reaches 1247 ± 89 transactions per second (TPS), representing an 8.0× improvement over city-wide consensus, with energy consumption per transaction at the edge tier 95% lower than single-tier implementations. The simulation of 10,000 Byzantine attack attempts yielded a 99.97% detection rate. All improvements are statistically significant (p < 0.001, Cohen’s d > 0.8). Conclusions: Hierarchical consensus architectures, combined with selective off-chain processing, offer the most promising pathway towards scalable, secure blockchain–IoT integration in smart city contexts. Through an examination of five practical smart city use cases, we demonstrate that tailored consensus approaches can achieve the security guarantees necessary for critical urban infrastructure while respecting the computational limitations of deployed sensor networks. Remaining challenges in dynamic validator management, cross-chain interoperability, quantum resistance, and regulatory compliance are identified as priorities for future work.
This chapter focuses on Non-Fungible Token (NFT) forensics and investigation, covering ownership attribution, metadata analysis, copyright infringement, and marketplace analysis. It also addresses Initial Coin Offering (ICO) and cryptocurrency exchange forensics, including the investigation of fraudulent ICOs and behavioural clustering for exchange forensics.
Fraud detection on Ethereum is challenging because of the anonymity, speed and graph structure of blockchain transactions. While prior research has proven the effectiveness of using machine learning classifiers, Graph Neural Networks (GNNs) and behavioural heuristics to detect fraudulent transactions, most systems are offline and fail to consider real-world deployment challenges for real-time blockchain analytics. Here, we present DeTrust ETH, an operational fraud intelligence system for real-time tracking of Ethereum transactions on the Sepolia testnet. DeTrust ETH aims for the integration of five operational considerations: (1) real-time blockchain ingestion with Web3.py, (2) explainable machine learning with XGBoost and SHAP, (3) light-weight graph-based transaction tracing and risk propagation, (4) temporal trust decay and behavioural anomaly detection, and (5) tamper-resistant on-chain persistence of trust scores using Solidity smart contracts. The system maintains an in-memory directed transaction graph for real-time edge insertion and updating, circular-flow tracing, funding pattern tracing and fast path tracing, avoiding the retraining overhead of Graph Neural Networks (GNNs). Our experimental results demonstrate the median graph-query time is less than 20 ms, the system can handle 222.82 requests per second with a concurrent load, and 93.44% fraud recall with a recall-favouring threshold. Unlike prior research that mostly focuses on accuracy on historical data, DeTrust ETH focuses on real-time deployment. The novelty of this work lies in the design of a real-time, low-latency fraud intelligence architecture that integrates explainable machine learning, temporal trust modeling, and lightweight graph analytics under streaming blockchain constraints.
Leon Witt, Togrul Abbasli, Kentaroh Toyoda, Wojciech Samek · 5 authors
We introduce Knowledge-Free Correlated Agreement (KFCA) to reward client contributions in federated learning (FL) without relying on ground truth, a public test set, or distribution knowledge. Under categorical reports and an honest majority, KFCA is strictly truthful, addressing the label-flipping vulnerability of Correlated Agreement (CA). We evaluate KFCA on federated LLM adapter tuning and a real-world PCB inspection task, showing efficient real-time reward computation suitable for decentralized and blockchain-based incentive designs.
\abstract{\textbf{Purpose:} This study addresses the lack of trust in ethical product labels by designing a blockchain platform grounded in the TAFES principles (Transparency, Accountability, Fairness, Ethics, Safety). It aims to bridge the gap between blockchain's theoretical transparency and a responsible, real-world implementation for certification ecosystems. \textbf{Design/Methodology/Approach:} Using Action Design Research, we developed a proof-of-concept platform for label authentication. A hybrid architecture records critical events on an Ethereum Layer-2 network for security, while supporting evidence is stored off-chain via IPFS and linked via content identifiers. The solution was validated through a coffee supply chain scenario. \textbf{Findings:} The proof of concept demonstrates how a TAFES-aligned blockchain platform can support verification of label claims without requiring trust in a single intermediary by creating tamper-evident provenance records and auditable certification evidence across multiple stakeholders. The design supports low-cost, near-real-time anchoring of supply chain events while mitigating adoption barriers related to scalability, privacy, and operational viability. \textbf{Originality/Value:} This research contributes an integrated ethical and technical blueprint for trustworthy label authentication systems by translating TAFES into implementable design requirements and evaluation checks, and validating them through an ADR driven proof of concept. It advances prior work by moving from the question of whether blockchain can help to the question of how it should be implemented responsibly in multi stakeholder certification ecosystems.}
Taking pure elementary algebra as the research tool, this paper solves the three-layer right-associative tower exponent equation without advanced knowledge such as logarithms and calculus throughout the whole process. First, integer solutions are strictly eliminated via integer recursive scaling, zero solutions are excluded by analyzing domain boundaries, and combined with the growth law of power operations, it is predicted that the solution is in the form of positive real numbers with fractional exponents. Based on the expression form of positive real powers, variable substitution and derivation are directly carried out to obtain the particular solution , which is verified by substitution. Rigorous demonstrations are conducted on negative real solutions and interval monotonicity: it is directly proved that the equation has no solution when , and strict monotonicity is proved by definition only in the interval , completing the proof of uniqueness of real solutions. Meanwhile, the definitional contradictions of negative base numbers in the real number field are clarified, negating the existence of negative real solutions. This paper retains the intuitive and original reasoning logic, revises the connection defects of argumentation, and forms a complete, rigorous and self-consistent research compatible with the elementary mathematics system. 本文以纯初等代数方法为工具,求解三层右结合指数塔方程 ,全程不使用对数、微积分等高阶知识。首先通过整数递推放缩排除整数解,补充定义域边界排除零解,结合幂运算增长规律预判解为正实数分数指数形式;基于正实数幂的表达形式直接设元推导,求得特解 并代入验证。针对负实数解、区间单调性等问题展开严谨论证:直接证明 时方程无解,仅在 区间用定义法证明严格单调性,完成实数解唯一性证明;同时厘清负数底数的实数域定义矛盾,否定负实数解存在性。全文保留直观原生推理逻辑,修正论证衔接问题,形成严谨、自洽、适配初等数学体系的完整研究。
While the iteration of digital technology encourages mass creation, it also poses challenges to the protection of digital copyright, such as difficult rights confirmation, unfair distribution, and high costs of rights protection. Traditional governance technologies represented by DRM and cloud computing are poorly integrated with legal norms, while emerging blockchain smart contracts face high technical barriers and institutional adaptation difficulties. Based on this, the digital RMB smart contract, which is positioned as an "institutional technical tool", inherits the programmability advantage of blockchain smart contracts, has the dual endorsement of central bank technology and credit, and can also achieve wide reach through the operation of commercial banks. It is expected to bridge the gap between technology and ordinary users and solve the problem of the connection between technology and law. Based on the specific practice of the Yuan Guanjia module built into the digital RMB smart contract, the technology is expected to improve the traditional subscription model, guarantee the stepwise release of crowdfunding publishing, realize the "per-use settlement" of prepaid authorization, and build a return mechanism for the interruption of serialized works, thereby promoting the full-chain governance of digital copyright.
Alessio Basti, Rikkert Hindriks, Ruggero Freddi, Gian Luca Romani · 7 authors
Cross-frequency interactions are fundamental brain mechanisms for integrating information across temporal scales. However, accurate identification of these couplings is hindered by complex multi-frequency nonlinearities and by spurious, zero-lag artifacts caused by volume conduction. To our knowledge, conventional metrics lack a robust framework to characterize genuine interactions among multiple time series where a frequency of interest $f_N$ arises from the combination of $N-1$ components such that $f_N = \sum_{i=1}^{N-1} f_i$. We introduce a general family of antisymmetric cross-polyspectral indices designed to quantify these harmonic dependencies while being intrinsically robust to instantaneous mixing. We derive the theoretical properties of these quantities and validate them through simulations of cubic nonlinearities. As a proof of concept, we apply the indices to empirical EEG recordings; the results reveal significant higher-order dependencies that elude standard analytical approaches. We further discuss how these indices can inform novel, personalized multi-site transcranial magnetic stimulation (mTMS) protocols by enabling the selective monitoring and modulation of specific multi-frequency network interactions.
The rise of cryptocurrency has revolutionised value exchange and contractual execution in international trade. This article assesses whether existing legal and arbitral frameworks adequately ensure certainty, fairness, and enforceability in international cryptocurrency contracts. Employing doctrinal and comparative legal analysis, it examines the legal and regulatory framework of crypto-assets and smart contracts across jurisdictions in both the Global North (including the UK and the EU) and the Global Majority (such as India and Iran). Within some evaluations of regulatory approaches, such as UNIDROIT and MiCA, the research finds that while some jurisdictions in the Global North have made progress in establishing regulatory frameworks that recognise crypto-assets as property and promote the enforceability of smart contracts, they still face significant challenges. These obstacles are even more pronounced in Global Majority countries, where regulatory strategies tend to be prohibitive or fragmented. Decentralised innovations like Decentralised Finance (DeFi) and Decentralised Autonomous Organisations (DAOs) further complicate governance and cross-border recognition. The article also investigates the impact of international arbitration, particularly under the New York Convention and UNCITRAL Model Law. The findings reveal that, while these instruments support cross-border enforcement, they face challenges inherent to cryptocurrency, including issues with arbitration agreements, pseudonymity, public policy objections, and the volatility of damages. Blockchain’s decentralised structure further complicates the determination of an arbitral seat. Although crypto arbitration platforms are emerging as an innovative potential, their lack of connection to national legal systems renders them largely unenforceable. Integrating digital currencies into domestic legal frameworks may mitigate these concerns; however, effectiveness hinges on global acceptance, which remains fragmented across jurisdictions. In conclusion, the findings submitted that, despite notable progress, considerable gaps remain in both legal and arbitral frameworks concerning cryptocurrency. The key recommendations are to harmonise international legal and arbitral frameworks, establish more explicit rules for blockchain evidence, enhance privacy protections, and adopt hybrid arbitration methods that combine decentralised approaches with established practices. These measures aim to strengthen regulatory cooperation and ensure stability in cross-border cryptocurrency transactions.
Ulugmurodov Farkhod Fakhriddinovich, Hasanov Anvar Erkinovich, Abduvakhobov Feruzbek Abdurakhmonovich
This article comprehensively analyzes the formation, stages of development and the impact of cryptocurrencies on the modern economy. In particular, the transformation processes that have occurred in the financial system with the emergence of digital assets such as Bitcoin and Ethereum are studied. The study highlights the role of blockchain technology in transparency, security and reducing transaction costs. It also assesses the role of cryptocurrencies as an investment tool, their impact on monetary policy, and their impact on stability and risk factors in global financial markets. The article also examines the mechanisms for regulating cryptocurrencies based on the experience of different countries, and substantiates their positive and negative effects on economic development. The results of the study serve to draw scientific conclusions on the effective use of cryptocurrencies in the digital economy.
Modern banking systems simultaneously maintain monetary values across computation (8-10 decimal places), ledger posting ($\mathbf{4}-\mathbf{8}$decimal places), and customer presentation (2 decimal places) scales. In distributed microservice and event-driven architectures, unmanaged transitions between these scales introduce rounding drift, ledger divergence, reconciliation breaks, and non-deterministic replay risks that threaten audit compliance and regulatory reporting accuracy. This paper investigates how distributed banking systems can manage multi-scale monetary precision while preserving deterministic balances and auditability. The proposed Multi-Scale Monetary Precision Model (MSMP) defines three scale classes, three explicit precision boundaries, and four correctness invariants governing deterministic posting, ledger conservation, presentation consistency, and fractional carry forward. A taxonomy of three canonical failure modes, namely early rounding, boundary truncation, and nondeterministic aggregation, is presented together with four architectural patterns designed to ensure precision-safe monetary propagation. Evaluation on a synthetic bankingrealistic workload (106accounts over 365 days) demonstrates that MSMP reduces cumulative monetary drift by up to 99.9 %, eliminates reconciliation breaks entirely, and achieves 100 % replay determinism, with computational overhead of approximately 18 %. These results establish monetary precision governance as a first-class architectural control for audit-ready distributed financial systems.
Abstract The relationship between Fintech and Financial inclusion has emerged dramatically in the last five years as this study presents detailed bibliometric research on the interactions between Fintech and financial inclusion. The major goal of this study was to map the intellectual trends, influential work, and current research topics in this fast-developing field. Based on the data obtained from the Scopus database (2020–2025) and processed using VOSviewer, this study elaborates on descriptive, keyword co-occurrence, and bibliographic coupling analyses. The most important findings are that there has been immense growth in Fintech-FI research since 2020, and the research is mainly concentrated in China, India, and the USA, where most research and articles have been published. This study identified nine thematic clusters such as decentralized finance and AI in banking and the significance of financial literacy. The fast increase in publication but a gap appears between the number of publications and the number of publications that are impacted, which means that there is still a necessity to make some significant, long-lasting contributions. It would be curious to explore the use of behavioral finance, regional comparisons of the regulatory environment, EFT application in empowering SMEs and embracing ESG, and the significance of ethics in the context of digital finance in improving fair and sound financial systems in the world in future.
The conventional online recruitment systems depend heavily on centralized systems which often create problems in transparency, resume fraud, data monopoly, and low levels of trust between employers and applicants. As a solution to such shortcomings, this paper considers a blockchain-based decentralized recruitment system, combining verifiable credentials, decentralized identity (DID), and hiring technology that respects privacy. The system suggested uses smart contracts to make the recruitment process a fully automated procedure (job posting, application, credential verification, and hiring decisions) that does not rely on centralized intermediaries. Candidate credentials (including education, certifications, and work experience) are cryptographically signed by authorized issuers, and will be stored as hashes on the blockchain, which cannot be altered, whereas documentation is stored in the decentralized storage (IPFS). To enhance privacy, the platform uses selective disclosure and zero-knowledge proofs (ZKP) features that also allow applicants to reveal their knowledge of skills without revealing sensitive personal data. The architecture also introduces DAO-based governance and score of reputation to increase responsibility of employers, credit issuers and applicants. Experimental testing using synthetic recruitment data proves that it has higher reliability in recruiting, with 91% accuracy in catching resume fraud compared to 78% with conventional applicant tracking system and has reasonable transaction latency and growth capability with a hybrid on-chain off-chain architecture. The findings indicate that the use of blockchain in the recruitment systems can significantly positively contribute to the transparency, authenticity of credentials, and trust in the modern hiring ecosystems.
The European Commission's April 2026 age verification framework, built on software-based Zero-Knowledge Proofs (ZKP) atop the European Digital Identity (EUDI) Wallet, fails to achieve its stated privacy guarantees due to a structural enrollment binding problem: any ZKP scheme whose trust root is a government identity credential inherits that credential's linkability at the point of issuance. This paper proposes a replacement architecture based on hardware bearer credentials — physically issued FIDO2 tokens whose identity binding is discarded immediately after issuance — combined with an anonymous hardware-handle revocation list, offline kiosk enrollment, and a self-funding economic model. The proposal is technically feasible with current production technology, financially viable at EU procurement scale, and operationally self-sustaining through a €10 citizen co-payment at issuance plus a €30 replacement fee. A cost model for national deployment using Italy as a case study demonstrates that the system requires near-zero net public expenditure. The primary novel contribution is a game-theoretic mechanism embedded in the replacement fee structure that renders secondary market trading of credentials economically irrational without requiring any surveillance of credential holders.
The rapid proliferation of generative AI has collapsed the scarcity-based filters of digital trust, enabling synthetic identities, algorithmic fraud, and systemic epistemological uncertainty. Conventional authentication methods—passwords, cloud-stored biometrics, and external hardware tokens—are inherently fragile, centralized, and vulnerable to coercion or theft. This article proposes a paradigm shift toward bodily cryptographic sovereignty: a dental implant-based digital anchor that derives a Physically Unclonable Function (PUF) key from the unique acoustic resonance and microarchitecture of the human jawbone. The architecture integrates Zero-Knowledge Proofs (ZKP) for privacy-preserving verification, hardware zeroization to neutralize extraction attacks, and a configurable duress protocol for coercion scenarios. By leveraging globally established dental infrastructure and informed-consent medical protocols, the model bypasses the regulatory resistance and psychological friction associated with centralized biometric registries. It enables graded identity, scoped AI-agent delegation, and origin-verified digital communication. The proposed framework transitions digital trust from fragile external devices to an inseparable biological-cryptographic symbiosis, offering a scalable, voluntary, and regulatorily aligned architecture for the post-AI trust economy.
Open access
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Physical Unclonable Functions (PUFs) and Hardware Security
Nour Kattawi, Farah Abu-Dabaseh, Mohammed H M Qeshta, Hadeel Alfreihat
The rise of blockchain technology usage has been a fundamental revolution to the FinTech environment, allowing the provision of secure, transparent, and decentralized financial services. Although there is an increased concern with implementing blockchain-based FinTech solutions, there is little empirical research on how blockchain technology potentials can be converted into working decentralized financial services. This paper examines how Blockchain Technology Capability (BTC) can be utilized in making decentralized financial services more effective, especially the moderating effect of trust and transparency. It was based on the Resource-Based View and the Trust Theory, which is the reason why a quantitative research design was adopted, with data gathered among the users of blockchain-based FinTech applications, such as digital wallets, platforms of blockchain payments, and decentralized finance apps. 300 valid responses were obtained. The analysis was performed with the help of Partial Least Squares Structural Equation Modeling (PLS -SEM). The findings indicate that there is a strong positive impact of Blockchain Technology Capability on trust and transparency that subsequently positively affect the effectiveness of decentralized financial services. Moreover, the ability of Blockchain Technology has a direct positive effect on the service effectiveness. The mediation analysis has shown that trust and transparency mediate the relationship between blockchain capability and decentralized financial service effectiveness to some extent. The results add to the literature in the FinTech domain because they explain how blockchain potential generates value in decentralized financial systems and offer practical implications to FinTech companies that seek to promote trust, transparency, and service delivery.
Non-Fungible Tokens (NFTs) are unique digital assets stored on a blockchain that certify ownership of an item, giving rise to NFT art, where digital creations are tokenized to provide verifiable scarcity and provenance. To clarify the polarized debate surrounding this phenomenon, this paper conducts a systematic literature review of 18 academic articles (2020–2025) to synthesize the current state of research on its aesthetics. The review first maps the field’s methodological and typological landscape, and then presents three core thematic findings: the reconfiguration of value from intrinsic visual merit to social factors like community and identity; the evolution of the artist’s role from creator to system designer and community manager; and the adaptation of traditional art world frameworks. The study’s primary contribution is the articulation of a new theoretical framework, “decentralized aesthetics,” which posits that the value of NFT art is derived from the holistic, participatory experience it generates across perceptual, cognitive, emotional, and socio-cultural dimensions, rather than from its isolated visual properties. Finally, the paper identifies critical research gaps, such as a lack of longitudinal and cross-cultural studies, and proposes an agenda for future inquiry into digital creativity and ownership.
This study investigates the rapid centralization of the Ethereum builder market under the Proposer-Builder Separation (PBS) architecture. We argue that existing research, by focusing predominantly on influential order flows, lacks a comprehensive evaluation of order flow behavioral patterns and economic purposes. To address this gap, we analyze Ethereum transactions from September 2023 to August 2025 to characterize Exclusive Order Flows (EOFs) and non-atomic Maximal Extractable Value (MEV) -- the missing components corresponding to these behavioral and economic dimensions, respectively. We introduce a novel exclusivity metric based on Kullback-Leibler divergence and employ supervised learning to identify 75 EOFs and 322 non-atomic MEV flows, which account for 71\% and 23\% of trading-related builder revenue. A longitudinal analysis of builder strategies across these dimensions delineates the market's evolution into four distinct eras, revealing that while EOFs were instrumental in establishing early dominance, incumbents have since decoupled market share from immediate EOF dependency by leveraging entrenched network effects. Ultimately, we conclude that builder centralization is an emergent property of the PBS framework itself, as the architecture systematically violates the fundamental prerequisites of a competitive market.
Paul Gerhart, Jay Taylor, Sri Aravinda Krishnan Thyagarajan
Atomic swaps are a fundamental primitive for the trustless exchange of digital assets across blockchains: they guarantee that either both parties receive the agreed assets or neither party transfers. While this all-or-nothing guarantee is powerful, it also imposes an inherent determinism that rules out exchanges whose intended outcome is probabilistic. As a result, existing atomic swaps cannot realize trustless exchanges in which one party pays for a fixed chance of receiving a larger asset or reward, as in lotteries, randomized allocation mechanisms, and probabilistic cross-chain trades. We introduce probabilistic swaps, a new cryptographic primitive that extends atomic swaps to the probabilistic setting. In a probabilistic swap, one party's transfer is executed with a fixed, publicly specified probability embedded in the protocol and cannot be biased by either party. This yields a trustless mechanism for randomized exchange with verifiable odds and no trusted intermediary. Our construction combines adaptor signatures with oblivious pseudorandom functions (OPRFs) to realize the desired probabilistic outcome while ensuring that neither party can predict or bias it in advance. Along the way, we introduce a new mechanism for the atomic exchange of OPRF evaluations for payments, which may be of independent interest. A key feature of our approach is that it preserves the minimal on-chain footprint of modern atomic-swap protocols. The protocol relies only on standard Bitcoin scripts, such as digital signatures and timelocks, and is deployable on any blockchain that already supports atomic swaps. Consequently, probabilistic swaps are indistinguishable from ordinary on-chain transactions, which helps preserve privacy and fungibility. We provide formal security foundations and demonstrate practicality through a probabilistic swap in the Bitcoin testnet and in the Lightning Network.
Decentralized finance (DeFi) protocols now intermediate over USD 100 billion in value, including regulated stablecoins and tokenized assets deployed as collateral, yet no widely adopted framework operationalizes risk assessment at the rigor institutional adoption demands. Existing approaches emphasize protocol-specific parameter optimization or conceptual taxonomies without providing explainable, composability-aware, and structurally independent assessment methodologies. We propose a nine-dimension DeFi risk assessment framework extending the six-dimension taxonomy introduced by Moody's Analytics and Gauntlet with three novel dimensions: composability risk, comprehension debt, and temporal risk dynamics. We additionally introduce a transparency confidence modifier separating assessment reliability from risk severity. The framework is grounded in structural analysis of protocol dependencies conducted through an ontology-based protocol intelligence infrastructure covering more than 8,000 DeFi protocols. We retrospectively analyze 12 major DeFi-related incidents from 2024-2026 representing approximately USD 2.5 billion in direct losses. Five of the 12 incidents require at least one novel dimension for complete root-cause characterization, including the two highest-systemic-impact events in the dataset.
We study permissionless spot--perpetual basis trading in decentralized finance as a collateral control problem. The strategy holds spot inventory, hedges directional exposure with a short perpetual, and allocates capital between spot inventory and derivative margin under on-chain liquidity and execution frictions. The paper delivers three results. First, it solves a static control problem for the collateral share and shows that the risk-constrained formulation provides a more robust operating benchmark relative to the economic optimum. In comparative calibration, the required collateral rises monotonically under volatility stress. The collateral is the lowest for BTC and increases significantly for long tail assets such as LINK and DOGE. Second, the paper derives an asymmetric dynamic extension in which the lower boundary of intervention is solvency driven, and the upper boundary is determined by a trade-off between carry-loss and the cost of rebalancing. Monte Carlo simulation shows that the lower boundary remains structurally relevant, whereas meaningful interior upper triggers survive mainly in the regimes with high carry and low costs. Third, the paper validates an execution-aware implementation with live routed execution and historical backtests. The execution layer shows that the realized wedges are significant, but become worse in the case of selling the basis. This justifies a minimum effective rebalancing size and a positive execution buffer. The historical validation shows that in the case of a fixed control rule the realized performance is predominantly explained by the funding environment.
Large language model (LLM) ecosystems such as Claude Code and ChatGPT increasingly rely on skills: packages of natural-language instructions and executable tools. Once in the LLM's context, skill content cannot be reliably separated from trusted instructions, and a skill's executable side can invoke privileged actions, exposing the skill supply chain to injection, tampering, and rug-pull attacks. Existing defenses are stage-bound: centralized signing, audit reports unbound from the runtime artifact, or policy engines that cannot attest to what was approved. We present SIGIL, the first framework that seals the audit-runtime gap for LLM skills. SIGIL delivers verifiable hosting through a tamper-evident, decentralized on-chain registry from which LLMs fetch skills directly. The registry admits four publication types, Transparent, Licensed, Sealed, and Committed, spanning plaintext public distribution, monetized access, custodial use, and off-chain workflows; before admission, every skill is vetted by a Decentralized Autonomous Organization (DAO) audit committee that supports pluggable auditing methods under a stake-and-slash economic model. At load time, SIGIL delivers verified loading through a skill verification protocol executed by a Skill Verification Loader (SVL) embedded as the mandatory loading path: the SVL retrieves and decrypts the skill as its type requires, verifies its integrity against the on-chain record, and enforces its permission manifest before context injection. We evaluate SIGIL on a real-world deployment against 1,023 in-the-wild skills spanning six attack types. At load time, the SVL verifies each skill's integrity against its on-chain record and enforces its approved permission manifest, completing batched verification under 86 ms. Together, these results show that LLM skills can be cryptographically bound from publication through runtime at practical cost.
Open access
3 source records
Adversarial Robustness in Machine Learning
Artificial Intelligence in Healthcare and Education