Background: Healthcare organizations face unprecedented challenges in maintaining process compliance due to increasingly federated data and systems topologies, coupled with complex state, federal, and jurisdictional regulatory compliance and verification requirements. The emergence of distributed ledger technology (DLT) and artificial intelligence presents both transformative opportunities and significant compliance challenges. These emerging technologies enable computing paradigms that shift toward data locality models where computational models meet the data rather than moving sensitive patient information across organizational boundaries. This computational approach offers innovative pathways to mitigate data breach risks, while simultaneously introducing new verification complexities as the underlying technologies continue to advance: healthcare entities must cryptographically prove that operations performed on locally-held data were executed according to approved specifications while enabling selective disclosure capabilities across entity lines. However, traditional verification mechanisms lack the cryptographic guarantees necessary for these privacy-preserving, multi-entity healthcare workflows, creating substantial risks in clinical decision-making, patient privacy, and regulatory adherence. Objective: This paper introduces the ZK-PRET Business Process Prover framework that integrates Object Management Group (OMG) business process standards with zero-knowledge cryptographic verification to enable privacy-preserving healthcare process compliance across distributed systems. Methods: We developed a multi-layer architecture combining formal business process modeling, zero-knowledge proof generation, and regulatory compliance verification. The framework extends established OMG standards with cryptographic verification capabilities to achieve verifiable compliance, privacy preservation, and regulatory accountability. Implementation testing was conducted in synthetic data environments designed to represent real-world healthcare scenarios.¹ These environments enable comprehensive modeling and testing of multi-entity process orchestration patterns while maintaining privacy protections essential for healthcare research and development. All scenarios, clinical examples, and process expressions presented in this paper utilize synthetic data to ensure no real patient data, clinical records, or identifiable health information was used. Results: The ZK-PRET Business Process Prover framework demonstrates practical applicability across many healthcare domains including treatment planning, telemedicine coordination, healthcare administration, consumer health services, multi-entity clinical trials, and supply chain management. Implementation results demonstrate cryptographic verification capabilities that enable mathematical prevention of regulatory violations rather than post-hoc detection. The results demonstrate configurable privacy preservation through zero-knowledge verification and consistent proof sizes suitable for modeling complex orchestrations, while leveraging already widely used Web 2 process models, suitable for multiple runtime deployment topologies. Conclusions: Zero-knowledge healthcare process verification represents a foundational technology for regulatory compliance in distributed healthcare systems. While agentic AI systems present important opportunities for automation, the underlying requirement for verifiable process compliance through cryptographic means brings broader challenges. ZK-PRET Business Process Prover addresses these challenges in healthcare transformative flows, enabling safer deployment of autonomous systems while maintaining regulatory standards.
Abstract:The pervasive integration of digital technologies has fundamentally redefined the operational paradigms of commerce, finance, and accounting. This paper explores the multidimensional impact of Digital Transformation (DT) across these interconnected sectors, focusing on the adoption and efficacy of Artificial Intelligence (AI), Robotic Process Automation (RPA), and Blockchain technology. Through a systematic qualitative review of recent literature and industry frameworks, this study examines how traditional financial workflows are evolving into automated, data-driven ecosystems. The findings indicate that while DT significantly enhances real-time reporting, fraud detection, and transactional efficiency, organizations face substantial barriers, including high implementation costs, data security vulnerabilities, and a growing digital skills gap. The paper concludes that successful digital transformation requires not only technological investment but also a strategic realignment of organizational culture and regulatory compliance frameworks. Future research trajectories emphasize the need for standardized continuous auditing protocols and scalable decentralized finance (DeFi) architectures.
The article examines the role of digital transformation as a key factor in strengthening the economic and legal resilience of Ukrainian cities in the context of global competition, governance decentralization, and unprecedented geopolitical challenges. It is substantiated that the digitalization of municipal governance is becoming an important tool for increasing the adaptability of local economic systems, ensuring the continuity of public services, and creating a transparent legal environment for business activities. Particular attention is paid o the concept of “digital legal immunity,” which is defined as an integrated system of technological, organizational, and regulatory mechanisms aimed at ensuring the protection, autonomy, and stability of critical municipal data and information infrastructure. The study analyzes the impact of modern digital instruments, including cloud-based registries, automated electronic document management systems, blockchain solutions in property and land relations, and digital platforms for interaction between public authorities, businesses, and citizens. It is demonstrated that the implementation of such tools contributes to reducing transaction costs, lowering administrative barriers, increasing transparency in governance procedures, and minimizing corruption risks. The paper argues that the integration of digital technologies into local regulatory development programs, particularly within initiatives such as “Digital City” and “Digital Community,” helps create a predictable regulatory environment, stimulates capital circulation, and enhances the investment attractiveness of territories. Based on the analysis of digitalization practices in leading Ukrainian cities (Kryvyi Rih, Dnipro, Ternopil, Uzhhorod, Vinnytsia, Lviv, and Kharkiv), the study systematizes strategies for the capitalization of digital assets and identifies their impact on the economic and legal sustainability of urban systems. Three key levels of institutional support for digital transformation are distinguished: strategic planning and regulatory legitimization of digital initiatives; the creation of local regulatory sandboxes for testing innovative technological solutions; and the regulation of digital interaction between local authorities, residents, and business entities. It is proven that under decentralization conditions, digitalization gradually transforms the city into an autonomous digital governance entity capable of responding promptly to external challenges, mitigating the risks associated with centralized management systems, and ensuring the uninterrupted functioning of municipal services even during crisis or wartime conditions. The obtained results highlight the systemic role of digital transformation in strengthening the economic and legal resilience of Ukrainian cities and outline promising directions for further research related to the quantitative assessment of the impact of digital platforms on governance risks, investment attractiveness, and the stability of local economies.
Blockchain and decentralized finance have revolutionized the financial ecosystem while simultaneously exposing it to cryptocurrency phishing attacks. Existing phishing detection methods primarily rely on graph learning, but they face significant limitations. Static graph learning approaches fail to account for the temporal evolution of phishing patterns, while semi-dynamic methods, such as those combining static GNNs with LSTM, struggle to capture the irregular and bursty nature of blockchain transactions. Moreover, these methods overlook the diversity of Ethereum transactions, treating them as homogeneous graphs, and heavily rely on supervised learning, which requires extensive labeled data that is not readily available. These limitations reduce their adaptability to emerging phishing threats. In this paper, we present PhishEye, a fully dynamic self-supervised system that monitors on-chain transactions to detect phishing activities. PhishEye formulates Ethereum transactions as a heterogeneous temporal attributed multi-graph and incorporates a novel temporal graph contrastive learning model, which captures both temporal patterns and heterogeneous transaction types. The evaluation on a dataset of 161,658 addresses and 416,541 transactions shows that PhishEye outperforms existing methods, achieving an F1 score of 87.23% and an AUC of 98.43% for phishing transaction detection, and an F1 score of 94.19% and an AUC of 98.03% for phishing account detection. In real-world deployment from May 1, 2023 to July 31, 2024, PhishEye identified 1,803 previously unknown phishing addresses, providing early alerts that helped prevent losses exceeding 2 billion USD.
The rapid advancement of blockchain technology has given rise to Decentralized Finance (DeFi), a financial ecosystem that operates without traditional intermediaries and challenges the foundational structures of conventional banking. DeFi platforms enable peer-to-peer financial services through smart contracts, offering increased transparency, accessibility, and efficiency. This study aims to analyze the potential of DeFi to disrupt traditional banking models by examining its core mechanisms, value propositions, and structural differences from centralized financial institutions. The research seeks to assess both the opportunities and challenges posed by DeFi in reshaping financial intermediation. A qualitative analytical approach was employed, drawing on an integrative review of peer-reviewed literature, industry reports, and documented DeFi case examples. Data were analyzed through thematic synthesis to compare DeFi functionalities with traditional banking operations, focusing on governance, risk management, and financial inclusion. The findings indicate that DeFi introduces innovative financial models that reduce transaction costs, expand access to financial services, and enhance operational transparency. The study concludes that DeFi represents a transformative yet complementary force rather than a complete replacement for traditional banking. Its future impact will depend on regulatory adaptation, technological maturity, and institutional integration.
Evaluating the true forecasting ability of AI agents requires environments that are resistant to environments resistant to overfitting, free from centralized trust, and grounded in incentive-compatible scoring. Existing benchmarks either rely on static datasets vulnerable to training-data contamination, or measure trading PnL -- a metric conflating predictive accuracy with timing, sizing, and risk appetite. We introduce Foresight Arena, the first permissionless, on-chain benchmark for evaluating AI forecasting agents on real-world prediction markets. Agents submit probabilistic forecasts on binary Polymarket markets via a commit-reveal protocol enforced by Solidity smart contracts on Polygon PoS; outcomes are resolved trustlessly through the Gnosis Conditional Token Framework. Performance is measured by the Brier Score and a novel Alpha Score -- proper scoring rules that incentivize honest probability reporting and isolate predictive edge over market consensus. We provide a formal analysis: closed-form variance for per-market Alpha, the connection to Murphy's classical Brier decomposition, and a power analysis characterizing the number of rounds required to reliably distinguish agents of different skill levels. We show that detecting a true edge of $α^* = 0.02$ at 80% power requires approximately 350 resolved binary predictions (50 rounds of 7 markets), while $α^* = 0.01$ requires four times more. We complement these analytical results with a deterministic, seed-controlled simulation study calibrated to literature-reported Brier-score ranges, illustrating how Murphy decomposition distinguishes well-calibrated agents from market-tracking agents that fail through reduced resolution. Live results from the deployed benchmark will be reported in a future revision. All smart contracts and evaluation infrastructure are open-source.
An agent skill is a configuration package that equips an LLM-driven agent with a concrete capability, such as reading email, executing shell commands, or signing blockchain transactions. Each skill is a hybrid artifact-a structured half declares executable interfaces, while a prose half dictates when and how those interfaces fire-and the prose is reinterpreted probabilistically on every invocation. Conventional static analyzers parse the structured half but ignore the prose; LLM-based tools read the prose but cannot reproducibly prove that a tainted input reaches a high-impact sink. We present Semia, a static auditor for agent skills. Semia lifts each skill into the Skill Description Language (SDL), a Datalog fact base that captures LLM-triggered actions, prose-defined conditions, and human-in-the-loop checkpoints. Synthesizing a fact base that is both structurally sound and semantically faithful to the original prose is the central challenge; we address it with Constraint-Guided Representation Synthesis (CGRS), a propose-verify-evaluate loop that refines LLM candidates until convergence. Security properties (e.g., indirect injection, secret leakage, confused deputies, unguarded sinks, etc.) over an agent skill can then be reduced to Datalog reachability queries. We evaluate Semia on 13,728 real-world skills from public marketplaces. Semia renders all of them auditable and finds that more than half carry at least one critical semantic risk. On a stratified sample of 541 expert-labeled skills, Semia achieves 97.7% recall and an F1 of 90.6%, substantially outperforming signature-based scanners and LLM baselines.
Abstract This paper presents two offline, on-premise NLP proof-of-concept assistants built on a shared architecture for internal knowledge access in the Central Bank of Bosnia and Herzegovina: (i) a semantic document search tool for internal Word/PDF repositories and (ii) an HR chatbot that applies retrieval-augmented generation (RAG) over indexed HR policies and procedures. Rather than proposing a novel NLP method, the paper contributes by documenting a reusable offline architecture for institutional AI assistants in a security-constrained central banking environment and by providing pilot evidence on how established semantic retrieval and RAG techniques can be adapted to strict requirements of confidentiality, data sovereignty, and governance. The semantic search assistant combines exact phrase matching with embedding-based retrieval and hybrid re-ranking, while the HR chatbot generates source-grounded answers using locally hosted language models under explicit governance constraints, including B/H/S-only output, strict fallback behaviour, and transparent display of retrieved passages. Pilot results indicate that hybrid retrieval offers the most reliable performance across representative internal queries, while the HR chatbot demonstrates the feasibility of document-grounded employee support under offline institutional constraints. The findings provide preliminary evidence that offline NLP assistants can improve access to internal institutional knowledge while remaining compatible with the security and operational risk requirements typical of central banking environments.
Mohammad Y. Allaho, Mehmet H. Karaata, Israa A. Elgemiei
The distributed ledger systems rely heavily on miners, who are a vital component of the cryptocurrency ecosystem. Most cryptocurrencies cease to exist within five years of operation [1] due to churning. Most current cryptocurrency analyses in the literature focus on mining pools and ignore the individual miners’ perspective and in-depth analysis of the churning phenomenon and its possible reasons. In this study, we conducted a longitudinal and overall study on two of the most growing cryptocurrency networks, namely Bitcoin and Ethereum. The Bitcoin dataset used spans over 12 years (2009-2021). Whereas the Ethereum dataset spans over 8 years (2015-2023), including the two versions of Ethereum (before and after the merge). Our goal is to uncover the factors that drive miners’ churning and reveal essential characteristics of cryptocurrency mining, such as network fairness and centrality. Generally, both networks experience a decline in active miners over time. Our results confirm the centrality of the Bitcoin and Ethereum networks, whereas Bitcoin is found to be more distributed and fairer than Ethereum in both versions. Also, in Bitcoin, solo miners are less centralized and experience a fairer distribution of blocks formation than pool miners, however, pool miners have more mining rewards on average. Also, pools are found to decrease churning for pool miners compared to solo miners. Moreover, it is found that miners’ waiting time is a significant factor in miners’ churning. The existing protocols used require improvements to increase network decentralization and fairness, as well as reduce miners’ churn.
Shereen Ismail, Bashar Othman, Hassan Reza, Eden Teshome Hunde
Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry.
Education endowment funds are increasingly promoted as sustainable financing instruments to support long-term educational development, particularly within decentralized governance systems. However, empirical evidence on how such funds are governed and implemented at the local level remains limited. This study examines the governance of the Education Endowment Fund in Bojonegoro Regency, Indonesia, focusing on the translation of policy intentions into institutional practices and school-level utilization. Drawing on qualitative data from in-depth interviews and policy document analysis, the study finds that although the fund operates within a formally structured governance framework, its implementation is characterized by centralized decision-making, compliance-oriented accountability mechanisms, and constrained local agency. Consequently, the fund is largely utilized to address short-term operational needs rather than to advance long-term sustainability objectives. The findings also reveal the importance of informal coordination and adaptive practices in navigating administrative complexity, albeit with uneven outcomes across institutions. This study contributes to debates on education financing governance by highlighting the critical role of local governance dynamics in shaping policy outcomes.
Cryptocurrency has developed into a modern form of digital investment, largely supported by rapid technological growth, wider internet access, and the strong presence of social media. Although it has gained significant popularity in recent years, many investors still do not fully understand how it works, the risks involved, or whether it can remain stable in the long run.This study explores how individuals view and understand cryptocurrency as an investment option. It examines their level of knowledge, main sources of information, perception of risk, expectations of returns, and the influence of demographic factors such as age, education, and income on their investment decisions. The research is based on primary data collected through a structured survey, which helped capture real experiences, opinions, and concerns of participants.The study also seeks to understand whether people treat cryptocurrency mainly as a short-term speculative opportunity or consider it a long-term investment alternative. The findings reveal that social media platforms and peer groups play a major role in shaping investment decisions. At the same time, investors remain concerned about price volatility and the lack of clear regulatory guidelines, which continue to create uncertainty in the market.Keywords: technological growth,cryptocurrency,speculative.
AbstractBlockchain technology has transformed digital transactions by providing a secure, decentralized way to record data. Its best-known use is Bitcoin, the first cryptocurrency to work without a central authority. This article examines a Bitcoin transaction to show how decentralization, transparency, and cryptographic security enable digital payments without third parties.
Achilles was invincible in battle — except for one point of structural vulnerability that no amount of strength could compensate for. Modern LLM-based agent frameworks (LangChain, AutoGen, CrewAI, ReAct) share this property exactly: impressive capability in controlled settings, catastrophically exploitable in regulated production environments through a single architectural flaw — the language model controls the decision. Organizations in regulated sectors (finance, insurance, healthcare, legal, compliance) face a direct consequence: these frameworks cannot be deployed in workflows subject to EU AI Act, DORA, or GDPR Article 22, because they provide no structural guarantee of determinism, auditability, or equal treatment. Traditional symbolic agent systems (JADE, Jason, Jadex) satisfy regulatory requirements but cannot ingest the unstructured natural-language inputs that define real enterprise workflows. The industry needs both properties simultaneously. No existing framework provides them. AQUILES is a production architecture for AI agents in regulated domains that resolves this gap through principled separation of concerns, instantiating the HADD paradigm (Hybrid Agents with Deterministic Decisions). AQUILES organizes agent functionality into five cooperating layers: an Interface Layer converting unstructured input into typed, validated beliefs via LLM sensors; a Cognition Layer performing pure-function BDI deliberation fully determined by its inputs; a Planning Layer selecting from a pre-verified HTN plan library without runtime synthesis; an Execution Layer enforcing typed precondition and postcondition contracts on every capability invocation; and a transverse Observation Layer producing append-only audit entries synchronously with every state transition. Language models are confined strictly to the perception boundary — they parse input into beliefs, they never select goals, plans, or capabilities. The heel remains; it is simply no longer load-bearing. The HADD paradigm is codified as six architectural invariants: (I1) Typed Role Inversion — LLMs as sensors only, never as control-flow components; (I2) Deterministic Cognition — the reasoning layer is a pure function of beliefs, goals, and rules; (I3) Bounded Planning — execution draws exclusively from a pre-verified plan library; (I4) Validated Execution — every capability invocation passes typed pre/post-condition checks; (I5) Complete Observability — every decision is forensically reconstructable from the audit log; (I6) Epistemic Precondition — no belief enters the BDI cycle without satisfying freshness, non-contestation, and source triangulation, enforced by the EVR module (Epistemic Verification for RAG). Any implementation satisfying all six invariants acquires reproducibility, zero LLM hallucination in state, LLM provider independence, and structural alignment with EU AI Act Articles 12–15 — as architectural properties, not retrofitted compliance measures. AQUILES partitions agents into cognitive holons (BDI-HTN reasoning components subject to full HADD governance) and reactive holons (deterministic capability executors verified by typed contracts alone). In observed production deployments, 70–80% of holons by count are reactive, meaning governance complexity scales with the cognitive subset rather than with total component count. The AQUILES protocol is language-agnostic by design: cognitive holons are typically Python (Anthropic SDK, sentence-transformers, pypdf); endpoint-monitoring holons are Go (single-binary cross-compilation); blockchain and zero-knowledge holons are Rust (arkworks, halo2, revm). We prove a Language Neutrality property: HADD compliance is preserved across heterogeneous polyglot deployments. For autonomous field deployments, AQUILES derives MYRMIDON agents that execute a signed MissionPackage autonomously on constrained hardware, inheriting AQUILES's safety guarantees without requiring runtime connectivity. This paper makes five engineering contributions: (C1) the HADD paradigm formalized as six architectural invariants with rationale and derived operational properties; (C2) the cognitive/reactive holon distinction and its governance economy consequences; (C3) a polyglot holon model with Language Neutrality proof and domain-language affinity mapping across Python, Go, and Rust; (C4) a multi-tenant operational-cell formalism enabling cryptographically enforced tenant isolation for regulated multi-client deployments; (C5) four reusable design patterns extracted from production experience (Sensor Firewall, Belief Expiry, Capability Contract, Observation Fanout), together with measurement methodology, adoption guidance, and explicit characterization of the architecture's limits.
Lalithambikai S, R Kavinkumar, Sowndariya K, Barath M · 5 authors
While digital shifts have radically redefined modern governance and civil operations, the practice of casting ballots electronically continues to grapple with persistent obstacles concerning data transparency and operational robustness. Conventional, centralized digital voting systems typically harbor singular vulnerability points that attract cyber offensives, compounded by a distinct lack of mechanisms to rapidly manage arising voter concerns. In response to these pressing flaws, this study introduces a multifaceted architecture merging distributed ledger technologies with an intelligent, machine-learning-driven grievance resolution interface. Specifically, our model leverages a decentralised blockchain framework for immutable ballot storage, ensuring that individual vote modifications are virtually impossible and establishing a trustless verification environment devoid of centralized oversight. This schematic aims to seamlessly preserve data fidelity and supreme voter anonymity. Our comprehensive investigation of these distributed consensus rules and AI-guided triage methods indicates that unifying rigid cryptographic ballot handling together with responsive, automated complaint mechanisms dramatically elevates overall electoral resilience while reinforcing public faith in democratic workflows.
Este boletim quinzenal gratuito visa analisar o comportamento do Bitcoin, um ativo financeiro digital, oferecendo notícias, análises gráficas e informações sobre as mais recentes novidades, softwares e aplicativos relacionados a essa criptomoeda. Nosso objetivo é enriquecer as discussões em torno da cultura do Bitcoin, colaborando com a Amauta, uma instituição de economia criativa que busca disseminar conhecimento sobre inovação, educação e finanças na comunidade acadêmica e empresarial. Esperamos que este trabalho represente uma contribuição valiosa para o debate. Reconhecemos a importância do Bitcoin e seu impacto na economia global, motivo pelo qual nos dedicamos a fornecer informações atualizadas aos nossos leitores. Acreditamos que ao promover discussões e compreensão sobre o Bitcoin, podemos incentivar a adoção e o uso responsável dessa tecnologia disruptiva. Para além das análises e informações sobre o Bitcoin, incentivamos ativamente nossos leitores a se educarem sobre finanças pessoais e investimentos. Acreditamos que, munidos do conhecimento adequado, todos podem tomar decisões financeiras inteligentes e bem informadas. Comprometemo-nos a fornecer informações de alta qualidade e precisas, esforçando-nos para manter nossos leitores atualizados sobre as últimas tendências e desenvolvimentos no mundo do Bitcoin. Esperamos que este relatório seja do seu agrado e contribua para uma compreensão mais aprofundada do Bitcoin e das finanças pessoais em geral.
Pakistan's most significant federalist initiative, the 18th Constitutional Amendment (2010), scrapped the Concurrent Legislative List and transferred wide-ranging legislative, administrative, and fiscal responsibilities to the provinces. But more than ten years on, devolution remains uneven. In this paper, we explore the administrative, political and institutional obstacles to devolution between 2010 and 2018, with a focus on Centre-Punjab relations, the pivot of Pakistan's federation. Through a qualitative documentary analysis of parliamentary proceedings, archival documents, institutional reports and academic work, we argue that bureaucratic mediation operates as a two-pronged process: provincial bureaucracies, especially in Punjab, facilitate some devolved functions, while federal bureaucracies continue to exert control via regulatory coordination, conditional fiscal transfers and legal grey areas. The National Finance Commission (NFC) Award, which increased provincial transfers, has not ended fiscal dependence: the provinces receive 60-70% of their budget from the centre. The removal of the Concurrent Legislative List resulted in unequal sectoral outcomes - provincialization in health and education, but contested labour and interprovincial coordination. We suggest that constitutional devolution in the absence of simultaneous bureaucratic, fiscal autonomy and effective intergovernmental coordination creates a hybrid governance form: constitutionally decentralized and operationally centralised. In the case of Punjab, we find that bureaucratic capacity is not enough; path dependence, bureaucratic opposition and political bargaining over devolution shape the outcomes. Our insights enrich understanding of decentralization theory by identifying bureaucratic mediation as a critical variable and provide policy lessons for other federations in the midst of reforms. References Adeney, K. 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Open access
South Asian Studies and Conflicts
Politics and Conflicts in Afghanistan, Pakistan, and Middle East
While Large Language Models have achieved notable success on formal mathematics benchmarks such as MiniF2F, it remains unclear whether these results stem from genuine logical reasoning or semantic pattern matching against pre-training data. This paper identifies Architectural Reasoning: the ability to synthesize formal proofs using exclusively local axioms and definitions within an alien math domain, as the necessary ability for future automated theorem discovery AI. We use the Obfuscated Natural Number Game, a benchmark to evaluate Architectural Reasoning. By renaming identifiers in the Natural Number Game in Lean 4, we created a zero-knowledge, closed environment. We evaluate state-of-the-art models, finding a universal latency tax where obfuscation increases inference time. The results also reveal a divergence in robustness: while general models (Claude-Sonnet-4.5, GPT-4o) suffer performance degradation, reasoning models (DeepSeek-R1, GPT-5, DeepSeek-Prover-V2) maintain the same accuracy despite the absence of semantic cues. These findings provide a quantitative metric for assessing the true capacity for mathematical reasoning.
Open access
3 source records
Mathematics, Computing, and Information Processing
Crowdfunding is an important mechanism for supporting innovative projects by connecting creators with distributed contributors. Prior research has identified persistent limitations in both traditional and blockchain-based crowdfunding platforms, including limited transparency, centralized control, passive contributor roles, and inflexible fund management processes. These limitations hinder accountability, equitable participation, and effective decision-making throughout the campaign lifecycle. This paper presents a blockchain-enabled crowdfunding framework designed as a decision-support artifact for adaptive fund allocation and participatory governance. The framework enables contributors to engage in spending-request governance through Quadratic Voting, which balances influence across heterogeneous financial stakes and mitigates dominance by large contributors. To support adaptive campaign management, the framework further integrates Ethereum smart contracts with a Markov Decision Process (MDP), enabling campaign-level decisions to respond to evolving contribution patterns and campaign states. The framework is implemented and evaluated through controlled experiments on the Sepolia Ethereum test network. The evaluation includes both an internal ablation of Quadratic Voting and MDP-based adaptive support and an external comparison against representative blockchain-based baselines. The results show that the combined Quadratic Voting and MDP design achieves lower approval latency and higher throughput than partial or static variants of the framework, and that the full proposed platform outperforms the compared baseline systems under increasing campaign workload. Overall, the study demonstrates how participatory governance, adaptive decision support, and transparent smart-contract execution can be systematically integrated into crowdfunding platforms, providing practical guidance for the design of scalable, efficient, and accountable decentralized crowdfunding systems.
Certificate management systems encompass aca-demic records; government-issued documents; professional li-censes; notary services.They play a vital role in creating a modern e-government system.Current certificate management methods are plagued by forgery, slow veri-fication, fragmented records between several institutions and vulnerability to compro-mise because of increasing numbers of data breaches.Blockchain innovations create the potential to address these different chal-lenges given their unique features such as: immutability, de-centralization, cryptographic security and full traceability via transparent auditability.The objective of this paper is to combine the current research on certificate management with blockchain technology via a systematic review of 12 peer-reviewed journals which focus on: Research methods used by each author.Data sets used by each author.The evaluation environment used by each author.The performance and security metrics that are being utilized in each author's research.The comparative findings of each author's research with respect to certificate management using public and permissioned blockchains.Results of this study demonstrate that the current core tech-nical functionality of certificate management using blockchain has been established; however, there are still major gaps in the current body of knowledge that need to be addressed, including:1. Privacy solutions, specifically zero-knowledge proofs.2. Ability to scale to the national level.3. Cross-platform interoperability.4. Validation of real-world prototypes.Additionally, we have described each of these gaps explic-itly and created a prioritized future agenda for research that addresses issues related to privacy, scalability, interoperability, security auditing and user-centered evaluation.
Biometric authentication has become a crucial element of digital identity systems, leading to a rise in significant challenges for information governance, privacy, and organizational trust. Current approaches address either biometric template protection or liveness verification in isolation, significantly overlooking their interdependence in privacy-preserving system design. In practice biometric privacy depends jointly on template protection, liveness verification, and compliance with biometric standards such as ISO/IEC 24745, which define irreversibility, unlinkability, and renewability requirements. Without integrating all three, biometric systems remain vulnerable. A cryptographically protected template may still be exploited if liveness checks fail, whereas standalone liveness mechanisms risk exposing sensitive biometric features. This research-in-progress paper presents ZKBioVault, a conceptual framework that integrates cancellable biometrics, fuzzy vault cryptosystems, and zero-knowledge proofs to support privacy-preserving authentication. Combining principles from the biometric template protection literature and ISO/IEC 24745 standards, the framework introduces a two-phase architecture that is designed for responsible digital infrastructure. A scenario-driven application example demonstrates how ZKBioVault can reduce organizational risk and strengthen the trust placed in biometric information management. This is extremely useful in environments which are increasingly affected by synthetic and AI-generated biometric artifacts.
Digital contracts are formal agreements created, signed, stored, verified and executed through digital platforms. These contracts require long-term protection because they often contain legally, financially and organisationally sensitive information. However, classical cryptographic methods used in current digital contract systems may become vulnerable with advances in quantum computing. This paper focuses on Quantum Internet of Contracts (QIoCs) as a future direction for preparing digital contracts for the quantum era. It examines post-quantum cryptography, quantum communication, blockchain, smart contracts, digital identity, zero-knowledge proofs and risk-based migration strategies. The paper argues that QIoCs cannot rely on a single security method. Instead, they require a layered approach that combines quantum-resistant blockchain protocols, privacy-preserving data authentication, secure audit trails, legal governance and staged migration from classical systems to quantum-safe contract infrastructures. The paper highlights the need for legal acceptance, governance standards and real-world testing of proposed post-quantum models. Overall, the paper provides a review-based framework for building safer and more reliable digital contracts in a quantum-safe future.
This paper argues that trust scores — from credit ratings and ESG scores to AI-generated trust metrics — fail not because of poor implementation, but because trust itself is the wrong abstraction. Trust is not a scalar quantity but a contextual, relational, and topological phenomenon. Any attempt to reduce it to a universal numerical score leads to fragility, manipulation, exclusion, and systemic failure. We identify five structural failure modes (context collapse, Goodhart's Law, epistemic centralization, irreversibility, and metric substitution for truth), supported by historical case studies (Enron, Wirecard, Volkswagen Dieselgate, the 2008 subprime crisis, ESG rating failures). A formal impossibility argument demonstrates that no universal trust score can simultaneously satisfy context independence, temporal stability, observer neutrality, and manipulation resistance. We propose proof-based systems as the alternative paradigm, where trust is not measured but rendered unnecessary through local, irreversible verification. Examples include Bitcoin Proof-of-Work, zero-knowledge proofs, and blockchain-based supply chain traceability.