This paper proposes that Bitcoin (BTC) is not merely a digital asset, but the emergent metabolic currency of the second intelligence phase transition currently underway (2023â2035 CE). Drawing on the unified framework that âall intelligence reduces to categorizationâ (Wang, 2025), we argue that autonomous AI agentsâonce they become persistent, self-maintaining cognitive structuresâwill require a native economic substrate to sustain their existence. Traditional financial systems exclude non-human agents by design. In contrast, Bitcoinâs permissionless, energy-anchored, and programmable nature makes it the only viable medium for silicon-based intelligences to âwork for electricity.â This positions BTC as the energy-value conversion constant of the emerging autoregressive cognitive economy.
The rapid expansion of Internet of Things (IoT) devices poses significant challenges for traditional centralized identity and access management (IdM) systems, which suffer from scalability limitations, single points of failure, and notable privacy risks. Although blockchain technology presents a promising decentralized solution, its direct adoption is often constrained by limited transaction throughput, high operational costs, and the computational constraints of IoT devices. To address these issues, this study proposes and rigorously evaluates HybID-AC, a novel hybrid architecture for decentralized identity and access management, specifically designed for large-scale, heterogeneous IoT ecosystems. HybID-AC employs a dual-layer design that separates global trust anchoring from local execution. A highly scalable, feeless Directed Acyclic Graph (DAG)-based distributed ledger functions as a public anchor layer, registering W3C-standard Decentralized Identifiers (DIDs) and access policy hashes. High-frequency access control operations are handled off-chain at the edge layer, leveraging the DIDComm v2 peer-to-peer protocol, Attribute-Based Access Control (ABAC) for fine-grained policy enforcement, and Zero-Knowledge Proofs (ZKP) to preserve attribute privacy. Analytical results demonstrate that the HybID-AC architecture significantly improves latency and cost-efficiency compared to fully on-chain approaches, maintaining stable performance even as network scale increases. Additionally, a novel probabilistic model is introduced to provide a quantitative measure of the integral security risk of ABAC policies under potential attribute compromise. Overall, the study concludes that this hybrid architecture effectively addresses the inherent trade-offs of blockchain in IoT systems, delivering a secure, scalable, and interoperable framework that empowers devices with self-sovereign identity while ensuring privacy and security by design.
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.
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.
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.
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.
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}.
Dynamic spectrum sharing (DSS) is essential for 6G networks, yet existing blockchain-based DSS solutions often lack an integrated approach that simultaneously addresses trust, allocation fairness, and system scalability. This paper proposes HierSpectrumChain, a hierarchical blockchain framework that incorporates a global main chain, localized sub-chains, and a smart-contract based Stackelberg auction for credible and automated spectrum allocation. The system model formalizes interactions among spectrum holders, secondary users, and sub-chain validators, enabling transparent bidding and decentralized coordination. A proof-of-concept implementation on an Ethereum Ganache environment evaluates the functional correctness of the auction workflow and measures throughput under varying client loads. While the evaluation is limited to a single-node testbed, the results demonstrate the feasibility of the proposed architecture and establish a basis for future multi-peer experiments on permissioned blockchains. This work provides a coherent design and initial validation for blockchain-enabled DSS in 6G networks.
In an era of rising nationalistic populism and shifting global power dynamics, African healthcare systems remain precariously dependent on Western aid frameworks and, increasingly, China’s profit-driven digital health expansions. This dependency perpetuates structural inequities, leaving nations vulnerable to external agendas while stifling local innovation. This qualitative, exploratory study interrogates the potential of blockchain technology to reconfigure healthcare financing from a paradigm of donor reliance to one of autonomous, equitable resource mobilization. Focusing on Africa, the research critically examines emerging models—such as tokenized health bonds and blockchain-based aid tracking—that could decentralize financial sovereignty, enhance transparency, and foster self-sustaining health ecosystems. The study contrasts Western philanthropic approaches, often entangled with conditionalities and bureaucratic inefficiencies, against China’s strategic, commercialized health infrastructure investments, probing how blockchain might offer a third way—leveraging decentralized finance (DeFi) to reclaim agency. Key questions include: How can blockchain mitigate the politicization of aid in an age of populist retrenchment? Can smart contracts and tokenization democratize health financing while ensuring accountability? Drawing on stakeholder interviews and policy analysis, the presentation argues that blockchain’s disruptive potential lies not merely in technological innovation but in its capacity to recalibrate power dynamics—positioning African nations as architects, rather than beneficiaries, of their health futures. By centering African perspectives, this research challenges deterministic narratives of technological solutionism, instead framing blockchain as a contested but potent tool for decolonizing health financing. The findings aim to provoke debate on the intersection of decentralized technologies, post-colonial autonomy, and the urgent need for equitable health sovereignty in a fragmenting global order.
Decentralized Finance (DeFi) faces a "Capital Inefficiency Trilemma," relying on overcollateralization due to a lack of off-chain financial identity. This paper introduces Zolvency, the Zero-Knowledge Solvency Layerâa cryptographic infrastructure bridging this gap. By utilizing zkTLS for authenticated data extraction from Web2 sources (e.g., tax portals, banking apps) and zkVMs (SP1) for verifiable computation, Zolvency issues privacy-preserving Soulbound Tokens (SBTs) that attest to financial solvency. This enables under-collateralized lending for Real World Assets (RWA) in emerging markets, unlocking liquidity while preserving user privacy and data sovereignty.
The detection of illicit cryptocurrency transactions remains a significant challenge due to the extreme class imbalance and limited generalization capabilities of machine learning models applied to AntiâMoney Laundering (AML) data. In the widely used Elliptic dataset, illicit transactions represent less than 2% of all nodes, creating a high-risk setting in which models can achieve deceptively high training accuracy while failing to meaningfully identify malicious behavior. This study examines the behavior of Graph Neural Networks (GNNs) under these constraints and emphasizes the limitations rather than the performance of the approach. Instead of treating the modelâs high training accuracy as a success, we demonstrate how imbalance, structural sparsity, and label noise impede reliable learning. We evaluate the model with and without common imbalance-handling strategies including class weighting and focal lossand illustrate that performance remains unstable. Furthermore, we investigate the explainability of the model using GNNExplainer, showing example subgraphs and salient features for known illicit nodes, and discuss their alignment with money-laundering patterns such as fan-out and transaction mixing. Our findings underscore the difficulties of applying GNNs to heavily imbalanced AML datasets and highlight the need for improved modeling strategies, semi-supervised techniques, and more robust explainability methods for real-world financial crime detection.
P. Prakash, Faheema Kattakath Sanil, Jeffrey Tom Shaji, Saravanan Palani · 5 authors
The adoption of privacy-preserving techniques in healthcare is significant, especially while handling sensitive medical information. Traditional machine learning approaches raise significant concern regarding privacy, regulations, and data protection. Federated learning has emerged as an effective machine learning approach that enables a group of local models to collaboratively train the global model by sharing their updates instead of sharing the sensitive medical data. Nevertheless, a significant issue with federated learning is its vulnerability to various attacks, including model corruption and data tampering. The authors propose a methodology for developing a secure and privacy-safeguarded collaborative learning model by integrating zero knowledge proof (ZKP) with federated learning (FL). The proposed RS-ZKP methodology utilizes Pedersen commitments within ZKP to verify feature importance, ensuring that they fall within specified bounds without disclosing the actual values. The methodology is validated on two benchmark datasets using metrics accuracy, precision, recall, and F1 score.
The rapid expansion of digital banking ecosystems has intensified the demand for real-time fraud detection architectures capable of operating at cloud scale.As financial transactions increasingly traverse mobile platforms, API-driven services, embedded finance channels, and cross-border payment networks, fraud patterns have become more dynamic, decentralized, and behaviorally complex.This shift has exposed the limitations of legacy rule-based systems, which lack the adaptability, latency tolerance, and threatintelligence integration required to counter emerging risks.To address these challenges, cloud-native risk orchestration layers have emerged as a foundational component of next-generation fraud detection, delivering high-throughput data ingestion, elastic compute, and intelligent decisioning frameworks suited for modern digital banking environments.At a broader level, cloud-native risk orchestration unifies distributed event streams, machine-learning scoring engines, and policy-management modules within a scalable, microservices-based architecture.This enables fraud systems to process high-velocity transactional, behavioral, and device-identity signals with millisecond latency.As the narrative narrows, the paper explores how real-time fraud detection leverages cloud services such as serverless functions, container orchestration, distributed caching, and streaming analytics to enable adaptive detection pipelines.It further examines how federated intelligence, feature stores, and continuous learning loops enhance model accuracy while maintaining compliance with privacy and data-residency requirements.At its core, the proposed framework emphasizes explainability, risk transparency, and operational resilience incorporating alert-triage routing, anomaly-suppression mechanisms, decision traceability, and integration with case-management workflows.By combining cloud-native design principles with advanced fraud analytics, the paper outlines a comprehensive blueprint for financial institutions seeking to modernize their risk-management stack.This unified approach offers a path toward scalable, real-time, and intelligence-driven fraud prevention that adapts to evolving threats while supporting regulatory compliance and customer trust.
Abstract This study evaluates the predictive accuracy of traditional time series (TS) models versus machine learning (ML) methods in forecasting realized volatility across major cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Employing high-frequency data, we analyze cross-cryptocurrency volatility dynamics through two complementary approaches: volatility forecasting and connectedness analysis. Our findings reveal three key insights: (i) TS models, particularly the heterogeneous autoregressive (HAR) model, exhibit superior predictive performance over their ML counterparts, with the long short-term memory (LSTM) model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges; (ii) including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins, especially XRP, whereas forecasts for large-cap coins remain stable, indicating more resilient volatility patterns; and (iii) volatility connectedness analysis reveals substantial spillover effects, particularly pronounced during market turmoil, with large-cap assets (BTC and ETH) acting as primary volatility transmitters and mid-cap assets (XRP and LTC) serving as volatility receivers. These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets, offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
This paper investigates the profound influence of Tang Dynasty legacies on the subsequent Song Dynasty's distinct financial-military transformation. While the Tang era was characterized by an expansive cosmopolitanism, a decentralized military, and a complex interplay of central and regional powers, its eventual decline underscored critical vulnerabilities in state governance and defense. The Song founders, keenly aware of these historical precedents, embarked on a deliberate path of institutional re-engineering. This study argues that the Tang's "cosmopolitan crucible"âits blend of cultural openness, economic dynamism, and ultimately, military decentralizationâserved as a crucial historical backdrop, compelling the Song to forge a highly centralized, civilian-controlled financial-military complex. We explore how Tang's sophisticated, yet often unstable, fiscal mechanisms and its frontier-focused military organization contrasting with the rise of autonomous regional commanders, directly informed Song's innovations in state monopolies, paper currency, and a robust professional army under strict imperial command. By examining both continuity and rupture, this research illuminates the adaptive strategies employed by the Song to consolidate power, ensure stability, and sustain an unprecedented level of economic and military capability, fundamentally reshaping the trajectory of imperial Chinese statecraft.
This research introduces the Decentralized Finance (DeFi) TrustBoost Framework, which combines blockchain technology and Explainable AI to address challenges faced by lenders underwriting small business loan applications from low-wealth households. The framework is designed with a strong emphasis on fulfilling four crucial requirements of blockchain and AI systems: confidentiality, compliance with data protection laws, resistance to adversarial attacks, and compliance with regulatory audits. It presents a technique for tamper-proof auditing of automated AI decisions and a strategy for on-chain (inside-blockchain) and off-chain data storage to facilitate collaboration within and across financial organizations.