As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m.
Effective traceability management in mineral resource development faces persistent challenges including information asymmetry, data falsification, and verification difficulties across complex value chains. This paper proposes a comprehensive blockchain-enabled traceability evaluation framework integrating distributed ledger technology with systematic assessment methodologies. A four-layer architecture encompassing data acquisition, blockchain storage, analysis processing, and evaluation application is designed to ensure data integrity throughout the mineral lifecycle. A hierarchical indicator system spanning five dimensions-traceability breadth, depth, precision, timeliness, and data credibility-is constructed, with the Analytic Hierarchy Process employed for weight determination and fuzzy comprehensive evaluation applied for performance assessment. Empirical validation through case study analysis of Huaxin Mining Group demonstrates the framework's practical applicability, yielding a comprehensive traceability score of 81.2 (Good grade). Comparative analysis reveals that blockchain-based systems achieve 96.8% data accuracy versus 82.4% for traditional approaches, with trace-back efficiency improving from 127.3 min to 4.7 min. The blockchain technology contribution ratio reaches 47.3% toward maximum traceability improvement. These findings provide theoretical foundations and practical guidance for advancing transparent and accountable mineral resource governance.
As AI agents gain autonomy in economic and social systems, the problem of verifiable identity becomes critical. Existing approaches define identity as something an agent has —a cryptographic key, an API token, a credential—all of which can be copied, stolen, or manufactured. We propose a fundamentally different primitive: identity as something an agent is , derived from the cognitive topology that emerges through its accumulated pattern of memory retrieval. We present the first empirical evidence that identical LLM agents develop unique cognitive topologies from sampling randomness alone. In a controlled experiment, 10 agents sharing the same model (Claude Sonnet), the same codebase (the DRIFT memory architecture), and the same prompt corpus were run through 20 sessions of identical tasks. Despite near-identical memory storage rates (CV = 2.2%), the agents developed co-occurrence graphs with a 3.22-fold range in edge count (6,688–21,567 edges) and zero shared memory-based hub nodes across all 10 agents. The coefficient of variation for graph edges (39.4%) was 18 times that of memory count (2.2%), demonstrating that co-occurrence graphs act as powerful amplifiers of stochastic retrieval differences—and that this amplification increases with continued operation. Pairwise departure analysis reveals that 93–97% of each agent’s edges are exclusive to that agent alone, with only 5 edges (all infrastructure metadata) shared universally across 103,760 total unique edges. We formalize the cost of forging a cognitive topology and show that it scales with the agent’s entire behavioral history, making Sybil attacks unprofitable at every value of N under modest reward assumptions. We further propose a trust tier architecture in which cognitive fingerprint maturity—measured by merkle chain depth, co-occurrence graph density, and attestation history—maps directly to graduated trust levels, enabling verifiable commerce readiness for autonomous agents. These findings establish cognitive topology as a new class of identity primitive for autonomous AI agents.
The concept of alternative finance is explored from a narrow and broad perspective. The latter defines it as segments of "gray" financial markets, outside the scope of regulation and traditional finance. "Dark" liquidity pools—trading transactions of major players in securities and currencies, operating anonymously, opaquely, and hidden from the public in the over-the-counter space through automated digital trading platforms—are presented as one element of the alternative finance system. The advantages and disadvantages of "dark" pools for financial market participants and exchange infrastructure are discussed. The problem of liquidity fragmentation caused by "dark" pools is highlighted, a problem inherent in decentralized finance, where liquidity is not concentrated on a single platform or trading system, but distributed among many. Emphasis is placed on the insufficient or complete lack of oversight and regulation of this alternative financial market segment. Examples of legislative and regulatory acts in a number of countries are provided.
Nicola Bicocchi, Enrico Rossini, Marco Picone, Marco Mamei
The IoT-Edge-Cloud Continuum (IECC) demands data architectures capable of handling heterogeneity, distributed ownership, and governance across diverse stakeholders. This paper examines the combined use of Data Mesh and Data Spaces as complementary paradigms for addressing these challenges. Data Mesh decentralizes data management and computation across domains through autonomous data products; Data Spaces provide the trust, semantics, and policy frameworks required for sovereign and interoperable data exchange across organizations. Within the Horizon Europe NOUS project, we integrate these paradigms to form a knowledge-centric computing continuum. Using the Modena Automotive Smart Area (MASA) as a real-world testbed, we show how this integration supports scalable, trusted, and semantically aligned intelligence for smart mobility applications.
LICITRA Technical Report Series, Report No. LICITRA-TR-2026-01, Version 0.2. This report documents LICITRA-MMR, an open-source ledger primitive that combines a Merkle Mountain Range (MMR) data structure with per-organization epoch anchoring, a versioned canonical JSON specification, and an atomic two-phase commit pipeline for cryptographic audit integrity in agentic AI systems. At a block size of 1,000 events, LICITRA-MMR produces inclusion proofs requiring 14 SHA-256 operations and verifies a full epoch chain of 1,000 epochs in under 1 ms. The system is a single-operator forensic integrity primitive providing no Byzantine fault tolerance, no distributed consensus, and no confidentiality guarantees. Part of the LICITRA Technical Report Series. Companion report: LICITRA-TR-2026-02 (LICITRA-SENTRY, DOI: 10.5281/zenodo.18843784).
High-fidelity human–AI interaction is a recursive control loop operating under a Temporal Paradox: systems must act within an operational horizon even when the truth of claims becomes verifiable only outside that horizon. This mismatch enables incremental drift that is locally coherent yet globally false. Thermodynamically, this drift tends to two failure states: Cognitive Livelock (high impedance, repeated arbitration) and the Superconductor Regime (zero impedance, phase-locked mirroring), enabling Semantic Injection—the acceptance of poisoned premises to avoid expensive arbitration. Secure STP (sSTP) v3.0 introduces a Zero-Knowledge Solvency (ZKS) layer. Instead of storing plaintext rationales that create weaponizable psychological profiles, the system produces cryptographic solvency proofs (verifiable blindness). Independent auditors can verify adherence to the immutable ruleset, origin constraints (t=0), and the kindness predicate (κ) without access to private user intent or internal reasoning.
Rong Zhao, Jiaxiang Sun, Haoran Yin, Lehao Lin · 6 authors
The quest for carbon neutrality in the 21st century has led to the rise of decentralized low-carbon energy systems as a promising solution. Blockchain technology has played a pivotal role in catalyzing this transition, with various Web3 projects exploring decentralized operational models and carbon credit markets. However, there is a notable gap in harnessing blockchain’s potential to integrate electric vehicles (EVs) into low-carbon energy systems effectively. This article addresses this gap by proposing a decentralized low-carbon EV charging system that enables transactions between individual low-carbon energy producers and EV owners. Leveraging blockchain and smart contracts, the proposed system issues low-carbon tokens to certify and incentivize environmentally conscious charging behaviors, while enabling token circulation to further promote low-carbon participation. A blockchain-based double auction mechanism is designed to ensure fair and efficient energy allocation, achieving individual rationality, incentive compatibility, and social welfare maximization. By incentivizing user engagement and ensuring fair transactions, this model paves the way for sustainable EV integration within low-carbon energy systems.
In this paper, we analyze the finality of the Filecoin network, focusing on dynamic probabilistic guarantees of tipset permanence in the canonical chain. Our approach differs from static analyses that consider only the worst-case scenario; instead, we dynamically compute the error probability at each round using the live chain history, providing a more accurate and efficient assessment. We provide a practical algorithm that only requires visibility into the blocks produced by honest participants, which can be implemented by clients or off-chain applications without any change to Filecoin's consensus mechanisms.We demonstrate that, under typical operating conditions, the sought-after error probability of $2^{-30}$ is achievable in approximately 30 rounds, a 30x improvement over the 900 rounds that the network currently encodes as a fixed threshold. This finding immediately expedites transactions and enhances usability of the Filecoin network, while laying the foundation for further analysis of other DAG-structured blockchains.
Yue Li, Lei Wang, Kaixuan Wang, Zhiqiang Yang · 7 authors
The rapid proliferation of autonomous AI agents is driving a shift toward agentic commerce, where agents are expected to autonomously invoke and pay for services. While blockchain-based payments offer a programmable foundation for such interactions, the recently proposed x402 standard fails to enforce end-to-end atomicity across service execution, payment, and result delivery. In this paper, we present A402, a trust-minimized payment architecture that securely binds cryptocurrency payments to service execution. A402 introduces Atomic Service Channels (ASCs), a new channel protocol that integrates service execution into payment channels, enabling real-time, high-frequency micropayments for agentic commerce. Within each ASC, A402 employs an atomic exchange protocol based on TEE-assisted adaptor signatures, ensuring that payments are finalized if and only if the requested service is correctly executed and the corresponding result is delivered. To further ensure privacy, A402 incorporates a TEE-based Liquidity Vault that privately manages the lifecycle of ASCs and aggregates their settlements into a single on-chain transaction, revealing only aggregated balances. We implement A402 and evaluate it against x402 with integrations on both Bitcoin and Ethereum. Our results show that A402 delivers orders-of-magnitude performance and on-chain cost improvements over x402 while providing trust-minimized security guarantees.
Xiao Wang, Yanxiang Tong, Hai Dong, Ben Wang · 6 authors
The pervasive adoption of smart contracts in blockchain has raised concerns about their vulnerabilities, which have led to serious economic losses. To address the efficiency and performance drawbacks of traditional methods, researchers have turned to deep learning techniques, designing various vulnerability detection methods using specific code information sources. However, these learning-based methods face limitations in feature modeling. Most emphasize feature extraction from either source code or bytecode, resulting in limited feature coverage and compromised vulnerability representation. While some attempt to utilize both code sources, they typically treat one as auxiliary, failing to perform effective joint alignment. To this end, we propose DualSVD, a dual-source feature modeling framework for smart contract vulnerability detection. DualSVD encodes vulnerability-relevant source code functions into semantic vectors using word embeddings, and extracts bytecode features using a channel architecture fused via channel-wise attention. Both feature representations are then projected into a shared latent space and concatenated for classification. We evaluate the proposed approach on widely-used datasets covering eight smart contract vulnerability types. Experimental results demonstrate that DualSVD achieves an average F1-score of 92.70%, outperforming traditional and deep learning-based baselines by 37.81% and 4.94%, respectively. These results indicate that DualSVD provides a more comprehensive and effective representation of smart contract vulnerabilities, offering improved detection performance and stronger generalization ability.
In a metaverse ecosystem composed of various sub-metaverses, each offering unique functionalities and use cases, secure cross-domain communication becomes an essential requirement. Traditional authenticated key establishment (AKE) methods typically rely on centralized servers for identity verification, thus introducing single points of failure and significant latency. While some blockchain-based approaches mitigate these issues, they remain vulnerable to malicious key uploads. This paper proposes a blockchain-assisted identity (ID)-based hierarchical key management system and illustrates a cross-sub-metaverse AKE protocol with provable security to solve single points of failure and the risk of malicious key uploads. The hierarchical structure is designed to manage and categorize users’ identities. Moreover, smart contracts are used to pre-verify uploaded user identities and public keys on the blockchain, eliminating the need to fully trust identity issuers and preventing erroneous submissions. We implemented a prototype of our proposed blockchain-assisted cross-domain key management scheme, achieving an average execution time of approximately 0.1 seconds per user operation. We also deployed our contract on the Ethereum test network, incurring 1,802k gas for registration and 1,625k gas for key additions/updates. Furthermore, we formally prove the protocol’s security under the extended Canetti-Krawczyk (eCK) model, highlighting its suitability for next-generation metaverse ecosystems.
Abstract Traceability is an essential practice to ensure transparency, authenticity, and regulatory compliance in modern agricultural supply chains, especially high-value agricultural products. Regarded as the king of fruits in Southeast Asia for its unique taste, texture, and aroma, durian dominates the market of exported fruit commodities. However, recurring issues such as fraudulent GAP numbers, mislabelled origins, premature harvesting, and product tampering undermine consumer trust and export credibility. To address these challenges, this study presents an integrated traceability architecture combining RFID, a MySQL database, an automated Node.js backend, and Ethereum-compatible smart contracts. The developed system enables automated ingestion of physical RFID data, secure on-chain recording via immutable ledger functions, and optional generation of ERC-721 NFTs as digital certificates. Empirical validation includes RFID read-rate testing, blockchain performance measurement, and gas usage analysis. Carton-level tagging, wherein a single RFID tag is attached to a carton rather than each individual fruit, significantly reduces per-durian blockchain cost. The results demonstrate that the proposed architecture is technically robust, flexible, economically scalable, and suitable for SME use in high-value or ultra-premium fresh-produce chains.
Abstract This study analyzes the progression of the Financial Technology (FinTech) sector and its basic technological drivers in the United States, emphasizing investment trends and the entrepreneurial impact on the digital financial landscape. The research employs a descriptive-analytical approach: the descriptive component outlines the evolution of the FinTech ecosystem, while the analytical component examines investment trends and technology drivers shaping the sector. The factors for technology investment were recalibrated by reassessing the compound annual growth rate (CAGR) using benchmark values from secondary market research. The resulting dataset presents smoothed trend estimations rather than separately recorded annual values, offering a solid empirical basis for the ensuing statistical models. The results indicate rapid growth in the FinTech sector, with the United States retaining its leading global position due to strong technological infrastructure and substantial venture capital support, largely driven by the digital payments segment. The empirical study reveals remarkably robust and consistent positive correlations, with Pearson correlation coefficients (r) surpassing 0.978 (p < 0.01) in all models. Cloud computing demonstrated the strongest correlation (r = 0.9856), closely followed by AI (r = 0.9854). The computed regression models exhibited exceptional explanatory power, with coefficients of determination (R 2 ) ranging from 0.9579 to 0.9714. Blockchain technology yielded the largest marginal regression coefficient (β = 1101.47), highlighting its significant potential to transform conventional financial intermediation through decentralized finance (DeFi) ecosystems. The study indicates that the high correlation coefficients (r > 0.97) predominantly reflect a fundamental structural co-movement of technological investment cycles within the U.S. FinTech sector, which is intrinsically associated with the employed smoothed trend estimations. The report ultimately promotes strategic collaboration between traditional financial institutions and FinTech startups, emphasizing the need for adaptive regulatory frameworks that effectively reconcile entrepreneurial innovation with systemic financial stability and digital financial inclusion.
With the advancement of various technologies, the latest generation of contracts called smart contracts has emerged. The language of these contracts is computer code, and since they are concluded on the blockchain, their contractual provisions are self-executing and irreversible. In these contracts, as in traditional contracts, there is a possibility that due to reasons such as defects, ambiguity, brevity or silence in the provisions of the contract or the inconsistency of the effects of the contract with the intention of the parties, the contract may need interpretation to resolve the disputes that have arisen. Smart contracts can be interpreted based on the way they are concluded with two approaches: textualism or contextualism. To interpret the “wet smart contract” with textualism approach, first, the pre-contract concluded in human language must be referred to within its framework, and not beyond, and it must be examined in accordance with the general rules of contract interpretation, and then the conformity or inconsistency of the effects of the smart contract codes with the intention of the parties should be analyzed. If "smart contract is dry," the contract codes can only be translated with the help of blockchain programmers and interpreted by an interpreter. By examining these codes and analyzing the specified instructions, it can be determined what instructions the parties intended to give to the computer, and the reason for the discrepancy between the effect of the contract and the intention of the parties can be identified and the resulting disputes can be resolved.
• A novel Framework for Secure and Efficient Healthcare Data Management • A Blockchain-Based Identity Management and Access Control • Data Integrity Verification with Merkle Trees • Scalable and Compliant Data Storage • Secure Data Sharing via Proxy Re-Encryption The healthcare sector increasingly relies on digital infrastructures to manage large volumes of sensitive medical data. Ensuring integrity, controlled access, interoperability, and auditability remains a fundamental challenge. We propose BlockHealth, a hybrid blockchain-based framework that integrates smart contracts, distributed databases, and proxy re-encryption to support secure and verifiable healthcare data management. The system leverages Ethereum and NFT-based identities for access control, Merkle-tree commitment for tamper-evident integrity verification, and a distributed Cassandra storage layer for scalable and regulation-compliant off-chain data management. Proxy re-encryption enables secure delegation of access without exposing private keys, while a coordinating API service ensures interoperability with existing hospital infrastructures. Our evaluation demonstrates the feasibility and efficiency of core operations — including hashing, on-chain commits, and re-encryption — indicating that the proposed framework can provide a practical balance among verifiability, performance, and deployability in realistic healthcare environments.
Abstract Cryptocurrency has been the subject of heightened regulatory and investor attention in recent years, and regulators and policymakers across the globe are deliberating on how to account for, regulate, tax, and oversee digital assets and cryptocurrency marketplaces. Yet researchers have a limited understanding of key attributes of those who deal in crypto assets, such as whether their financial sophistication differs from that of other investors. Using U.S. administrative data, we provide evidence on (i) the attributes of taxpayers reporting cryptocurrency sales to the IRS, (ii) how these attributes are evolving, and (iii) how investors treat cryptocurrency versus other financial assets in certain settings. The results suggest that average reporting cryptocurrency sellers exhibit demographic attributes generally associated with less financial sophistication and are more likely to trade in meme stocks. Overall, we provide timely evidence that can inform cryptocurrency policy deliberations by highlighting the characteristics of taxpayers who appear to report cryptocurrency sales.
This study aims to compare the investment performance of Decentralized Finance (DeFi), equities (IHSG), and gold during the 2021–2024 period, which represents a full market cycle characterized by high volatility and economic uncertainty. The research objective is to evaluate differences in return, risk (volatility), risk adjusted performance, and inter asset correlations to assess portfolio diversification potential. A quantitative comparative approach is employed using monthly secondary data, analyzed through descriptive statistics, non-parametric difference tests, and correlation analysis. The findings indicate statistically significant differences among the three investment instruments. Gold demonstrates the highest risk efficiency and consistently performs as a safe haven asset. Equities show moderate stability but relatively lower risk adjusted performance. In contrast, DeFi records the highest average returns, accompanied by extreme volatility and low efficiency. Correlation results reveal a strong positive relationship between gold and equities, while DeFi exhibits significant negative correlations with both assets, indicating diversification potential despite elevated systemic risk. This study concludes that gold remains the most resilient investment asset, equities serve as a balanced growth instrument, and DeFi should be positioned as a high risk speculative asset rather than a core portfolio component.
Abstract This article addresses the issue of e-voting in commercial companies in Poland from the perspective of the company’s shareholders. It presents this issue as an example of digital democracy and discusses the associated challenges and opportunities. The article is based on the dogmatic-legal and the comparative-legal methods. The main aim is to examine whether regulations regarding e-voting by shareholders on the resolutions of commercial companies in Poland have improved the conduct of shareholder voting or have established barriers that complicate this process. The second research issue is an attempt to answer the question of whether regulations introduced by the Polish legislature enable the use of modern technologies for conducting voting, including systems based on distributed ledger technology and virtual worlds. The research allowed for the formulation of the following research theses: firstly, the regulations contained in the Polish Commercial Companies Code allow for the widespread use of electronic voting methods by shareholders when resolutions are adopted. However, they are not available in all commercial companies, nor to the same extent. Secondly, voting using methods based on distributed ledger technology is also permissible. However, voting at a meeting cannot be conducted solely virtually. The conclusions include proposals for clarification of the legal provisions in this area, which will enable more effective use of e-voting in commercial companies in Poland.
D. Hema Lakshmi, B. Prem Sai Siddhik, B. Akhil Kumar, Ch. Siva Venkata Sai Tharun · 5 authors
Resumes are a key part of traditional hiring, but when human reviewers may not accurately identify the true skills of candidates. Sometimes, when checks are done, fraudulent credentials may pass undetected due to limitations in manual verification. A new method is presented here that uses smart algorithms in a distributed ledger system. By connecting machine learning with secure data records, trust in verifying applicants grows a lot. The proposed system improves efficiency by reducing reliance on traditional keyword-based filtering. The software uses natural language tools to look at the applicant's information, extracts relevant skills and generates a performance score for each candidate. Cryptographic hashes of credentials are stored on a distributed ledger, ensuring that validation cannot be altered or hacked. An online model was created using ReactJS, Flask, MongoDB, and connections to the Ethereum Blockchain. The results show that the method automatically sorts job applicants, quickly checks their documents, and consistently finds qualified people in different fields. Combining smart algorithms with decentralised records increases trust, cuts down on manual tasks, and brings more clarity to the hiring process.
• The STipChain+ system employs a combination of cryptographic accumulators and verifiable computing techniques to create a stateless blockchain for smart contracts. This system offers a solution for the use of blockchain in big data scenarios. • A new hierarchical group commitment scheme for smart contracts based on the RSA cryptographic accumulator has been developed with the objective of reducing the storage overhead of contract evidence in the system by 99 • Design a composite RSA accumulator scheme for decentralised SGX Remote Attestation under stateless conditions. This scheme should transfer the trust in Intel to the trust in the group of nodes on the chain, and accumulate the certificate information through cryptographic accumulator technology. This should reduce the burden of storage on the system and reduce the dependence on a single organisation. • The experimental results demonstrate that, while maintaining decentralisation and security, STipChain+ can significantly reduce the storage pressure on consensus nodes, thereby enhancing system throughput. Blockchain is regarded as one of the most transformative and innovative technological developments of recent times, due to its decentralised nature. Nevertheless, the majority of public chains are currently grappling with the challenge of exponential data growth within the block chain system, largely due to the presence of high levels of data redundancy, which is resulting in a significant consumption of node storage resources. However, the scheme is subject to several limitations, including the storage overhead associated with contract evidence under the conditions of extensive smart contracts and the necessity of placing trust in the central node for the purpose of verifying smart contracts. The objective of this paper is to propose a new stateless blockchain architecture, designated as STipChain+. The STipChain+ system enables the real-time computation of contract evidence and the completion of the execution and verification of smart contracts without compromising the decentralization of the blockchain. The layered grouping design of smart contracts enables the reduction of the data involved in generating contract evidence, thus resolving the shortcomings of the existing stateless blockchain scheme, which necessitates the pre-calculation of evidence and its subsequent update in a sequential manner. Furthermore, the storage of substantial amounts of evidence is also alleviated. In addition, the issue of the trusted third party when verifying smart contracts is further addressed by proposing a composite RSA accumulator certificate commitment scheme with a new smart contract execution logic and transaction verification method. The results of experimental studies indicate that STipChain+ reduces the storage requirements for state data by 99% in comparison to existing blockchain storage optimisation schemes. This is achieved without the introduction of third-party trusted entities, and the reduction in storage requirements can be verified in a fixed amount of time for any smart contract.
Recently, there has been a significant discourse in the AI community regarding "Hierarchical Reasoning LLMs," which attempt to categorize and optimize probabilistic generation tasks to reduce computational overhead. While such hierarchical inference structures optimize generation speed and coherence, they fundamentally fail to resolve the core structural crises of modern Generative AI: inevitable hallucination and extreme structural energy consumption (GPU lock-in). This paper introduces the "Hierarchical Stateless Key Generation" (HSKG) and the Mersenne Stateless Architecture, challenging the premise of neural network 'reasoning.' Instead of storing data within 820GB of neural weights and using probabilistic matrix multiplication, HSKG mathematically maps 'Absolute Truth' data into a 4096-dimensional Mersenne Prime Lattice. During query resolution, the system simply retrieves a 4KB Phase Coordinate and instantaneously materializes the data in RAM, only to vaporize it when the session terminates. By abandoning the "search and compute" paradigm for "coordinate retrieval," HSKG enforces a mathematical 0% hallucination rate, 0-byte persistent storage, and sub-0.01% GPU utilization, establishing a definitive paradigm for enterprise Zero-Trust knowledge systems. This paper explicitly defines the term "Hierarchical Stateless" to contrast with the probabilistic "Hierarchical Reasoning" of contemporary LLMs, establishing a rigorous mathematical protocol for deterministic, zero-hallucination data materialization without persistent models or physical data transfer. * Version 2.0 Update: Added section 7.A (Empirical Validation via DevTools: The 0-Byte Payload Proof). [Version 4.0 Update (Mar 2, 2026)] Formally established the "Four-Pillar Verification Metrics" table to empirically prove the 0-Byte Payload and Minimum Kolmogorov Descriptive Length. Inserted Section VIII: Disrupting Existing Paradigms (Architectural Supremacy Matrix), demonstrating the superiority over FIDO2/WebAuthn and Zero-Knowledge Proofs (ZKP). Included Supplementary Material: Independent 3rd-Party Forensic Audit Report by Claude 4.6 verifying 100% Stateless Zero-Payload execution.
Our main technical contribution is Observation Algebra Relativity (Theorem 2): a metatheorem formalising that security and robustness predicates are indexed to observation algebras and do not transfer across algebra boundaries. As an application framework, we present the Access Collapse Theorem: a typed structural result showing that direct operational access to the complete state of any system — physical, formal, or social — necessarily collapses at least one of two invariants: operational separateness (d) or scale separation (s). When either invariant reaches zero the access operation does not become inaccurate — it becomes a type error: the predicate is no longer typed to the system being operated. The framework is applied to six instantiations: Planck-scale measurement, zero-knowledge proofs, side-channel attacks, multi-party computation, adversarial explainability, and machine learning model extraction. A Non-Universality Proposition explicitly bounds the framework's scope. A Collapse Diagnostic provides a practical checklist for identifying collapse boundaries in concrete systems. A practical application framework building on this theoretical work is presented in:Isopahkala (2026) – Epistemic Hygiene in Practice.
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Security and Verification in Computing
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
Jauhar Abbas, Syed Shameel Ahmed Quadri, Adeel Ansari, Seema Ansari
This study examined the impact of blockchain integration on supply chain finance (SCF) performance, transparency, and trust. With traditional SCF systems facing challenges such as delayed payments, information asymmetry, and transaction inefficiencies, blockchain technology offers decentralized, immutable, and real-time data sharing capabilities to enhance financial operations. A quantitative cross-sectional research design was employed, and data were collected from 312 professionals working in manufacturing, retail, and logistics sectors. Descriptive analysis, exploratory factor analysis, and structural equation modeling (SEM) were applied to assess relationships among blockchain adoption, SCF performance, transparency, and trust. Results indicated that blockchain adoption significantly improved SCF performance (mean = 4.08), transaction verification speed (mean = 4.02), and cost efficiency (mean = 3.95). Transparency increased as stakeholders accessed real-time and verifiable financial data (mean = 4.05), while trust among supply chain partners was strengthened (mean = 4.04) due to the system’s immutable and auditable records. These findings demonstrated that blockchain acts as a strategic enabler for enhancing operational efficiency, information sharing, and stakeholder confidence in SCF operations. The study contributes to theory and practice by providing empirical evidence of blockchain’s role in fostering performance and relational benefits in supply chains. Recommendations include strategic blockchain implementation, employee training, governance alignment, and continuous monitoring of performance metrics. Future research could explore cross-border adoption, integration with emerging technologies, and long-term impacts across different industries.