Ledger-native payment systems introduce a radically new interaction paradigm at the point of sale. Rather than relying on legacy card-based processing networks, these systems enable merchant devices and user devices to collaboratively perform the construction, authorization, signing, and broadcasting of a transaction directly to a digital ledger. Once biometric authentication is performed on the userâs device, the remaining steps of the payment flow may be distributed flexibly between the devices. This shift allows the point-of-sale environment to evolve into an expressive, adaptive, and deeply interactive interface layer. This white paper presents a comprehensive exploration of the experiential landscape surrounding ledger-native payments, mapping the full set of user-experience, sensory, identity, environmental, and data-driven capabilities that emerge once retail transactions operate directly on a cryptographic substrate. The document is fully self-contained and articulates the future UX domain rather than any specific implementation.
Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro momentum indicators with microstructure dynamics through unified feature engineering. Building on established selective classification principles, the framework separates directional prediction from execution decisions through confidence-based thresholds, enabling explicit optimization of precisionârecall trade-offs for decentralized financial applications. Unlike traditional three-class approaches that simultaneously learn direction and execution timing, our framework uses post-hoc confidence thresholds to separate these decisions. This enables systematic optimization of the accuracy-coverage trade-off for blockchain-integrated trading systems. We conduct comprehensive experiments across 11 major cryptocurrency pairs representing diverse blockchain protocols, evaluating prediction horizons from 10 to 600 min, deadband thresholds from 2 to 20 basis points, and confidence levels of 0.6 and 0.8. The experimental design employs rigorous temporal validation with symbol-wise splitting to prevent data leakage while maintaining realistic conditions for blockchain-integrated trading systems. High confidence regimes achieve peak profits of 167.64 basis points per trade with directional accuracies of 82â95% on executed trades, suggesting potential applicability for automated decentralized finance (DeFi) protocols and smart contract-based trading strategies on similar liquid cryptocurrency pairs. The systematic parameter optimization reveals fundamental trade-offs between trading frequency and signal quality in blockchain financial ecosystems, with high confidence strategies reducing median coverage while substantially improving per-trade profitability suitable for gas-optimized on-chain execution.
Ivan S. Lapshin, Shorena S. Shushania, Alexandra Anisimova
The article is devoted to a comprehensive analysis of cryptocurrencies as an object of legal regulation in the Russian Federation. The relevance of the study is determined by the rapid integration of digital assets into the economy amid the persistent legal uncertainty regarding their legal nature. The aim is to trace the evolution of the Russian legislator's approach from a lack of regulation to the formation of an experimental legal regime. The methodology includes formal legal analysis of legislation, generalization of judicial practice, and a comparative legal approach. The authors thoroughly examine the legal definitions of digital currency and digital financial assets, identifying their key differences. The paper substantiates the classification of cryptocurrency as "other property," analyzes the tax regulations introduced in 2025 that recognize it as property for tax purposes, and identifies related problematic aspects (confirmation of expenses, classification of income). Based on the analysis of court practice, the absence of a uniform approach to the legal qualification of cryptocurrencies is stated. In conclusion, forecasts are made regarding the implementation of an experimental legal regime for qualified investors, and specific measures for legislative improvement are proposed, including the adoption of a framework federal law and amendments to codified acts. It is emphasized that the implementation of these proposals will create a balanced legal environment conducive to the development of the digital economy and the minimization of associated risks.
Imiefoh, Andrew Ikhayere, Andrew-Imiefoh, Ihuoma Joy
This paper examines how traditional property law concepts are being reconceptualized to address the challenges of digital assets and environments. As property rights shift from tangible objects to code-based digital assets, fundamental tensions emerge between established legal frameworks and technological realities. Digital assets challenge core property assumptions of rivalry, excludability, and persistence, requiring courts and legislators to adapt centuries-old principles to novel contexts. The analysis explores how diverse legal systems respond to specific digital property types, including intellectual property in non-rivalrous environments, data ownership disputes, cryptocurrency classification, non-fungible tokens, and virtual real estate. Through examination of landmark judicial decisions, emerging legislation, platform governance mechanisms, and technical standards, the paper identifies promising pathways for reconciling traditional property functions with digital innovation. Drawing on comparative approaches from multiple jurisdictions, the research proposes a balanced framework that acknowledges the cultural significance of property institutions while adapting their implementation for digital contexts. The recommendations emphasize flexible, context-sensitive approaches that can maintain essential property functions such as resource allocation, coordination, security, and exchange facilitation, while accommodating the unique characteristics of digital environments.
This paper presents LLMBugScanner, a large language model (LLM) based framework for smart contract vulnerability detection using fine-tuning and ensemble learning. Smart contract auditing presents several challenges for LLMs: different pretrained models exhibit varying reasoning abilities, and no single model performs consistently well across all vulnerability types or contract structures. These limitations persist even after fine-tuning individual LLMs. To address these challenges, LLMBugScanner combines domain knowledge adaptation with ensemble reasoning to improve robustness and generalization. Through domain knowledge adaptation, we fine-tune LLMs on complementary datasets to capture both general code semantics and instruction-guided vulnerability reasoning, using parameter-efficient tuning to reduce computational cost. Through ensemble reasoning, we leverage the complementary strengths of multiple LLMs and apply a consensus-based conflict resolution strategy to produce more reliable vulnerability assessments. We conduct extensive experiments across multiple popular LLMs and compare LLMBugScanner with both pretrained and fine-tuned individual models. Results show that LLMBugScanner achieves consistent accuracy improvements and stronger generalization, demonstrating that it provides a principled, cost-effective, and extensible framework for smart contract auditing.
This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large Language Model (LLM) agents in the uniquely demanding and fast-paced cryptocurrency domain. Unlike general-purpose agent benchmarks for search and prediction, professional crypto analysis presents specific challenges: \emph{extreme time-sensitivity}, \emph{a highly adversarial information environment}, and the critical need to synthesize data from \emph{diverse, specialized sources}, such as on-chain intelligence platforms and real-time Decentralized Finance (DeFi) dashboards. CryptoBench thus serves as a much more challenging and valuable scenario for LLM agent assessment. To address these challenges, we constructed a live, dynamic benchmark featuring 50 questions per month, expertly designed by crypto-native professionals to mirror actual analyst workflows. These tasks are rigorously categorized within a four-quadrant system: Simple Retrieval, Complex Retrieval, Simple Prediction, and Complex Prediction. This granular categorization enables a precise assessment of an LLM agent's foundational data-gathering capabilities alongside its advanced analytical and forecasting skills. Our evaluation of ten LLMs, both directly and within an agentic framework, reveals a performance hierarchy and uncovers a failure mode. We observe a \textit{retrieval-prediction imbalance}, where many leading models, despite being proficient at data retrieval, demonstrate a pronounced weakness in tasks requiring predictive analysis. This highlights a problematic tendency for agents to appear factually grounded while lacking the deeper analytical capabilities to synthesize information.
ABSTRACT The pursuit of enhanced inclusive growth, a cornerstone of the Sustainable Development Goals (SDGs), has generated extensive scholarly discourse, particularly regarding its interplay with fiscal decentralization in Africa. This study evaluates fiscal decentralization's impact on inclusive growth across 26 African nations (2002â2019) using fixed effects, DriscollâKraay, and generalized least squares (GLS) estimators, with robustness checks via Lewbel 2SLS, systemâGMM, and Kinky least squares. Three key findings emerge: first, fiscal decentralization consistently and significantly undermines inclusive growth across all specifications and metrics. Second, a Uâshaped relationship mirrors the Kuznets curve hypothesis, where initial decentralization exacerbates inequality before yielding equitable gains at higher income thresholds. Third, governance quality encompassing corruption control, regulatory efficacy, and political stability moderates this relationship, underscoring institutional frameworks' pivotal role. To mitigate disparities, policymakers must empower local authorities with greater fiscal responsibility over revenue collection and expenditure allocation, ensuring transparency and accountability. Concurrently, reforms should strengthen tax systems, optimize public spending, and enhance redistribution mechanisms, aligning decentralization strategies with broader objectives of welfare enhancement and sustainable growth. Related Articles Asongu, Simplice, and Nicholas M. Odhiambo. 2023. âThe Effect of Inequality on Poverty and Severity of Poverty in subâSaharan Africa: The Role of Financial Development Institutions.â Politics & Policy 51(5): 898â918. https://doi.org/10.1111/polp.12558 . Nchofoung, Tii, Simplice Asongu, Vanessa Tchamyou, and Ofeh Edoh. 2022. âGender, Political Inclusion, and Democracy in Africa: Some Empirical Evidence.â Politics & Policy 51(1): 137â55. https://doi.org/10.1111/polp.12505 . Asongu, Simplice A., Joseph Nnanna, and Vanessa S. Tchamyou. 2021. âFinance, Institutions, and Private Investment in Africa.â Politics & Policy 49(2): 309â51. https://doi.org/10.1111/polp.12395 .
Decentralized multi-agent systems have shown promise in enabling autonomous collaboration among LLM-based agents. While AgentNet demonstrated the feasibility of fully decentralized coordination through dynamic DAG topologies, several limitations remain: scalability challenges with large agent populations, communication overhead, lack of privacy guarantees, and suboptimal resource allocation. We propose AgentNet++, a hierarchical decentralized framework that extends AgentNet with multilevel agent organization, privacy-preserving knowledge sharing via differential privacy and secure aggregation, adaptive resource management, and theoretical convergence guarantees. Our approach introduces cluster-based hierarchies where agents self-organize into specialized groups, enabling efficient task routing and knowledge distillation while maintaining full decentralization. We provide formal analysis of convergence properties and privacy bounds, and demonstrate through extensive experiments on complex multi-agent tasks that AgentNet++ achieves 23% higher task completion rates, 40% reduction in communication overhead, and maintains strong privacy guarantees compared to AgentNet and other baselines. Our framework scales effectively to 1000+ agents while preserving the emergent intelligence properties of the original AgentNet.
Decentralized Physical Infrastructure Networks (DePIN) represent an emerging organizational form for operating physical infrastructure through blockchain-based coordination. DePIN through decentralized protocols and token-based payment mechanisms incentivize independent agents to deploy, maintain, and monetize real-world infrastructure, such as wireless networks, storage units, or sensors. This article presents a first formal economic analysis of DePIN architectures, modelling investment decisions under network effects in a blockchain-native Decentralized Autonomous Organization (DAO), with protocol-defined reward schemes. It establishes the equilibrium conditions that support decentralized provision, where token prices internalize participation, service reliability, and network coverage. Furthermore, it identifies a minimum viable coverage threshold determined by costs and network effects. Through a multi-agent machine learning simulation, we confirm that decentralized provision improves efficiency compared to centralized models. The results support the economic viability of DePIN and provide design guidelines for future decentralized infrastructure protocols. Finally, we propose an DAO incentive mechanism to implement First Best provision in Decentralized Physical Infrastructure Networks.
High-stakes decision domains are increasingly exploring the potential of Large Language Models (LLMs) for complex decision-making tasks. However, LLM deployment in real-world settings presents challenges in data security, evaluation of its capabilities outside controlled environments, and accountability attribution in the event of adversarial decisions. This paper proposes a framework for responsible deployment of LLM-based decision-support systems through active human involvement. It integrates interactive collaboration between human experts and developers through multiple iterations at the pre-deployment stage to assess the uncertain samples and judge the stability of the explanation provided by post-hoc XAI techniques. Local LLM deployment within organizations and decentralized technologies, such as Blockchain and IPFS, are proposed to create immutable records of LLM activities for automated auditing to enhance security and trace back accountability. It was tested on Bert-large-uncased, Mistral, and LLaMA 2 and 3 models to assess the capability to support responsible financial decisions on business lending.
Blockchain technology, combined with smart contracts, serves as a revolutionary force in financial operations by enabling automated, transparent, and tamper-proof transaction execution. The conventional stock market infrastructure faces operational shortcomings due to multiple intermediaries, slow settlement times, and security weaknesses. The integration of deep learning models with blockchain-based smart contracts demonstrates emerging potential to enhance security while delivering precise and efficient financial ecosystem operations. The review investigates how artificial intelligence collaborates with distributed ledger technologies through adaptive deep temporal models as well as consensus optimization and secure contract execution on private Ethereum consortium blockchains. This study examines two advanced financial approaches by evaluating their performance as the Adaptive Deep Temporal Context Network (ADTCN) and the Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA). Important obstacles related to system expansion, together with system compatibility and model visibility, and on-time deployment, are examined. Future directions for creating intelligent, secure, efficient blockchain-based financial systems are established through the identification of present research gaps.
Nov 28, 2025·2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies (SISIMPACT)
Vishwa Nath Sharma, Amit Chouksey, K. Pithambar, Pankaj Agarwal · 6 authors
This research describes how we implemented machine learning technology to help detect malicious participants on the Ethereum blockchain, including fake smart contracts and honeypots. Honeypots are built to fool attackers into approaching them, as they act like vulnerable smart contracts. The work unites data science, blockchain and machine learning to help the system tell apart honeypot contracts from other types. The chosen system collects all necessary information about Ethereum contracts from the Etherscan API and sorts the data by method, transaction behavior and flow of funds. These aspects are applied to create and review an XGBoost classifier model. The model is tested in three different ways: using real data as a reference, with attackers as adversarial honeypots and by looking at its cost-benefit analysis. This unique way of tracing enables greater ease of updating, fully automated analysis and stronger accuracy while watching smart contracts live. Using machine learning in honeypot detection is an important step toward protecting decentralized applications by spotting and handling threats early in the blockchain system. With XGBoost as its foundation, the suggested honeypot detection approach achieves a total accuracy of 98.78 %. The high proportion is a result of the model's impressive accuracy in identifying honeypot and non-honeypot electronic contracts.
The major focus of this research study is to understand the impact of the Russia-Ukraine crises or war on three major Crypto currencies like Bitcoin, Binance coin and Ethereum. This study also provides insight about the reaction of the Crypto market during the ongoing war situation and how the Cryptocurrencies react during the war crises, either bitcoin, ethereum, and the binance coin have the positive impact or the negative impact during the war, or the war has no impact on Cryptocurrencies. The relationship between these cryptocurrencies are also examined during this research. The major findings show that the ARCH effect exist in the Binance coin, Bitcoin, and the Ethereum market series. The research study used the GARCH methodology for analysis of results. For Bitcoin and Binance coin there is no direct impact in it, and factor of volatility exist in it. For Ethereum there is no direct impact of war, and factor of volatility does not exist in it. The research gives valuable insights to investors and policy makers.
Global supply chains are essential to world trade, but they harbor profound inequities- challenges that are manifestations of, and exacerbate, social inequalities. Lack of information, ambiguous procurement practices, and biased risk models relegate small suppliers, developing states, and underrepresented laborers. Artificial intelligence and blockchain with data governance come together in the form of AI-Driven Access and Transparency Networks (AI-ATNs), which make global value networks more equitable and accountable. Explainable AI is used together with fairness-conscious optimization and distributed ledger transparency in AI-ATNs.The outcome? Supply chain participation based on merit and need rather than location or connections. Agriculture, manufacturing, and humanitarian logistics provide real examples of AI systems turning equity into both a social goal and economic necessity. The digital revolution needs to move past efficiency targets and embrace equity intelligence, transforming global supply chains into ethical systems that balance business success with social justice.
Internet of vehicles (IoV) achieves this through the provision of easy and real time communication between vehicles, roadside infrastructure, and cloud services. However, its dynamic and heterogeneous environment presents significant authentication challenges, with conventional Public Key Infrastructure (PKI) approaches often proving unscalable, slow, and dependent on centralized authorities. This paper presents a blockchain-based authentication framework that leverages Decentralized Identity (DID), cryptographic hashing, digital signatures, and smart contracts to address these limitations. In the proposed system, each vehicle generates a unique DID, signs event data using its private key, and records authentication proofs immutably on the blockchain. The DID and the reputation that goes along with it allow verifiers to ascertain data integrity and authenticity without depending on centralized trust entities. By removing single points of failure and ensuring resistance to impersonation and data tampering, this approach delivers a low-latency, scalable, and secure authentication mechanism tailored for next-generation vehicular networks.
Abstract: The global real estate sector is currently hindered by centralized inefficiencies, opacity, and susceptibility to fraudulent activities. This paper presents a Real Estate Management System (REMS) utilizing Ethereum smart contracts and the InterPlanetary File System (IPFS) to establish a decentralized, tamper-proof registry. The system automates critical conveyancing processes, including ownership verification and funds escrow, thereby eliminating the need for traditional intermediaries such as brokers and notaries. By integrating a React.js frontend with a Node.js backend and MetaMask for non-custodial identity management, the proposed solution ensures high data integrity and operational efficiency. The study analyses the architectural implementation, security frameworks, and economic implications of transitioning from legacy databases to distributed ledger technology. Findings indicate that the proposed blockchain architecture significantly reduces transaction friction, enhances transparency, and provides a robust framework for secure property transfers. Index Terms: Blockchain, Smart Contracts, Real Estate, IPFS, Decentralization, Ethereum.
One of the most prevalent and harmful forms of cybercrime remains phishing, which is often based on email as its primary avenue of attack. Traditional detection methods apply both rule-based filtering as well as machine learning classifiers, though they often come with large false-positive rates and no defense against message authenticity. To enhance the communication trust and detection accuracy, this paper proposes a hybrid framework integrating blockchain technology with artificial intelligence (AI). The AI module utilizes machine learning models trained with a labeled dataset of phishing, spam, and legitimate emails. The high performance of classification is evidenced by the experimental results, where up to 98.4% accuracy is achieved in binary classification and 97% accuracy for multiclass detection. A Solidity-based smart contract deployed on an Ethereum testnet irrevocably keeps hashed records of authenticated emails to ensure integrity and non-repudiation. This hybrid approach compares favorably with AI-only approaches in that it reduces false positives by as much as 35% and has traceability and tamper-resistant logging that blockchain-only approaches do not have. The results show that the integration of blockchain technology and artificial intelligence (AI) is a feasible method of secure and trustworthy email communication by balancing detection efficiency and protection of privacy.
The union of Blockchain and Federated Learning (FL) technologies in the Internet of Vehicles (IoV) domain has ushered in new possibilities for privacy-preserving, decentralized, and cyber-resilient financial use cases. As autonomous and connected vehicles (CAVs) are integrated with edge computing and 5G/6G networks, these smart nodes increasingly engage in real-time financial transactions spanning from usage-based insurance, automated tolls, electric vehicle charging fee payments, to intelligent vehicle leasing and decentralized vehicular identity management. Such a dynamic scenario is best addressed by Blockchain technology, which provides a decentralized ledger system that promises immutability, transparency, and trust, which are perfect for authenticating transactions like insurance claims, micropayments, and contractual fulfilment in mobility finance. This enables IoV stakeholders to build predictive systems with data sovereignty and low latency. Even so, the joint employment of blockchain and FL in financial environments poses important regulatory and ethical challenges.
Mary Jesselyn Co, Bruce Mitchell, Lisa Jordan Powell
This paper describes the development and implementation of an innovative hotel carbon reduction simulation aimed at developing sustainability competencies in business students. Using the Harvard Business School "Net Zero" simulation, we assess how interactive simulation-based learning opportunities improve students' self-assessment of the eight sustainability competencies with a mixed-methods approach. The research examines critical gaps in knowledge of how simulation-based methods can develop integrated competency sets needed to solve complex sustainability challenges. The simulation assigns around 950 first-year management students as hotel managers tasked with achieving 50% emission reductions over seven years while maintaining financial performance. Students select from 29 sustainability initiatives across Energy, Purchasing, and Management categories, working within realistic constraints of carbon budgets, site-specific emission factors, and dynamic market conditions. Our comprehensive analytical design combines pre-post competency surveys with cluster analysis of strategic approaches, and qualitative analysis of learning reflections. Anticipated outcomes are enhanced competency development across all eight dimensions, with gains in systems-thinking, futures-thinking, and implementation competencies. The research aims to provide empirical proof for developing specific sustainability competences as well as demonstrating scalable approaches of integrating sustainability education into core business curriculum.
Owen Dugan, Garcia, Roberto, Ronny Junkins, Jerry Liu · 8 authors
The success of large language models (LLMs) can be attributed in part to their ability to efficiently store factual knowledge as key-value mappings within their MLP parameters. Recent work has proposed explicit weight constructions to build such fact-storing MLPs, providing an improved understanding of LLM fact storage mechanisms. In this paper, we introduce an MLP construction framework that improves over previous constructions in three areas: it 1) works for all but a measure-zero set of feasible input-output pairs, 2) achieves asymptotically optimal parameter efficiency matching information-theoretic bounds for some embeddings, and 3) maintains usability within Transformers for factual recall. Through our improvements, we 1) discover a metric on value embeddings that characterizes facts-per-parameter scaling for both constructed and gradient-descent-trained MLPs, 2) identify a simple encoder-decoder mechanism that empirically matches gradient-descent MLP facts-per-parameter asymptotics across all the inputs and outputs we test, and 3) uncover a fundamental tradeoff between an MLP's fact-storage capacity and its usability within Transformers. Finally, we demonstrate a proof-of-concept application of fact-storing MLPs: modular fact editing on one-layer Transformers by \textit{replacing entire MLPs at once}.