Blockchain systems expose a large number of tunable parameters that significantly influence system performance. However, in practice, a single parameter configuration is often applied across different workloads, leaving substantial unexploited performance potential. To address this, we propose EchoFlow, a blockchain parameter tuning framework that adaptively adjusts parameter configurations based on workload characteristics, enabling continuous performance optimization. EchoFlow employs a distributed reinforcement learning approach in which multiple actors perform parallel sampling to mitigate the substantial time required for sample generation in blockchain environments. To further accelerate convergence, we introduce a genetic algorithm during the initial phase of training to generate high-quality samples. Extensive experimental evaluations demonstrate that EchoFlow consistently outperforms existing methods across diverse workload scenarios while also reducing training time, highlighting its effectiveness and practical value.
This article examines the potential of blockchain-based state registries toenhance transparency and reduce corruption in public administration. The purpose of the researchis to evaluate how distributed ledger technology can address structural weaknesses inherent incentralized registry systems including data manipulation, lack of accountability and limitedauditability. The article employs a qualitative research design based on comparative analysis andcase study methodology drawing on international experiences and existing scholarly literature ondigital governance and blockchain implementation in the public sector. The findings indicate thatblockchain technology strengthens data integrity through immutability, decentralization andcryptographic verification mechanisms, thereby reducing opportunities for unauthorizedalterations and corrupt practices. Evidence from implemented registry reforms demonstratesimprovements in transparency, traceability and institutional trust. The article concludes thatblockchain-based state registries can serve as a foundational infrastructure for strengtheningpublic sector integrity, provided that legal frameworks, institutional readiness and technologicalcapacity are adequately developed to support sustainable implementation.
中文受人工智能自身能力局限,其易产生信息幻觉,且不擅长高精度数值运算。本文档内所有内容应严谨审核。EnglishDue to the inherent limitations of artificial intelligence, it is prone to generating hallucinations and performs poorly in high-precision numerical calculations. All contents in this document should be strictly reviewed. 5D几何统一,一切可计算。从夸克到文明,从DNA到意识。 DOI: 10.5281/zenodo.20798927 Black Hole & UVMM v4.0 CoreDOI: 10.5281/zenodo.20738759 Earth SystemDOI: 10.5281/zenodo.20285613 Cosmic BoundaryDOI: 10.5281/zenodo.20325710 Cosmic EvolutionDOI: 10.5281/zenodo.20677198 Information & Consciousness (Millennium Prize Problems)DOI: 10.5281/zenodo.20325710 UTFF Core (Atomic and Molecular Scale)DOI: 10.5281/zenodo.20343471 UVMM Core Axioms and Mathematical Proofs UVMM v4.0 CORE continue:https://doi.org/10.5281/zenodo.21500910 github.com A Topologically Designed Zero-Pressure Room-Temperature Superconductor _ First-Principles Derivation and CTP Verification这个超导方案可能更靠谱些 UVMM v4.0.15.01 High-Precision Global Calculation AI Knowledge Package.md UVMM v4.0.15 High-Precision Global Calculation AI Knowledge Package(6D‑Coordinate‑SuperKit‑v4.0 ).md UVMM v4.0.15 高精度计算适用领域(中英双语精简版)量子化学与分子化学 Quantum Chemistry & Molecular Chemistry中文:原子半径、键能、反应活化能全域计算,计算误差<0.1%。English: Global calculation of atomic radius, bond energy and reaction activation energy, calculation error < 0.1%.凝聚态材料物理 Condensed Matter & Material Physics中文:超导临界温度、拓扑能隙、合金力学性能预测,整体精度<2%。English: Prediction of superconducting critical temperature, topological band gap and mechanical properties of alloys, overall precision < 2%.生物大分子与意识神经科学 Biomacromolecules & Consciousness Neuroscience中文:蛋白折叠自由能求解,脑意识拓扑序参量精准判别,分类 AUC=1.000。English: Calculation of protein folding free energy, accurate discrimination of brain topological order parameter for consciousness, classification AUC = 1.000.核裂变 / 聚变与衰变物理 Nuclear Fission, Fusion & Decay Physics中文:各类核反应能量完整拓扑积分求解,全套 20 组核反应误差严格控制<2%。English: Complete topological integral solution for energy of various nuclear reactions, the error of 20 groups of nuclear reactions is strictly controlled below 3%.QED 与电弱粒子物理 QED & Electroweak Particle Physics中文:电子反常磁矩匹配标准模型10 −12量级精度,弱混合角偏差<0.01%。English: Electron anomalous magnetic moment matches the Standard Model with precision of 10 −12, the deviation of weak mixing angle is less than 0.01%.宇宙学与引力 Cosmology & Gravitation中文:CMB 功率谱、原初引力波偏振、暗物质暗能量密度推演,计算误差<2%。English: Deduction of CMB power spectrum, primordial gravitational wave polarization, dark matter & dark energy density, calculation error < 2%.量子精密计量 Quantum Precision Metrology中文:铯原子钟频率全环境修正拓扑闭式计算,频率偏差低于2×10 −10 Hz。English: Closed-form topological calculation of full environmental corrections for cesium atomic clock frequency, frequency deviation lower than 2×10 −10 Hz. ERROR edition: First-Principles Derivation of Light Speed as the Acoustic Velocity of Vacuum Superfluid Based on the UVMM Framework Abstract 摘要 English Based on two first-principles axioms—the Global Zero Angular Momentum Axiom (strict zero total cosmic angular momentum) and the Dynamic Möbius Projection Axiom (the fifth dimension constitutes a non-orientable Möbius manifold with curvature-dependent dynamic characteristic radius)—this work establishes a unified geometric framework for black holes within the Unified Vacuum Medium Model & Unified Topological Force Field (UVMM-UTFF). In this framework, black holes are no longer geometric singularities passively bending spacetime, but 5D topological solitons projected onto the 4D boundary. All energy release behaviors of black holes (jets, gravitational waves, electromagnetic radiation) essentially originate from topological phase transitions or steady pumping processes of prestressed vacuum medium. This paper systematically verifies the framework via four independent multi-beacon observational datasets: LIGO-Virgo-KAGRA gravitational-wave catalogs (GWTC-4.0/5.0, containing 390 binary black hole merger events), Event Horizon Telescope (EHT) polarization imaging of M87* and Sgr A*, LHAASO PeV ultra-high-energy gamma-ray observations of five microquasars, and GAIA DR3 Milky Way rotation curve data. The results demonstrate that theoretical predictions match observational values within a factor of 20 across 9 orders of magnitude, ranging from transient merger luminosity () to steady-state AGN jet power (). Dark matter effects are reduced to geometric prestress effects of vacuum medium, without introducing any exotic dark matter particles. 中文摘要 本文基于两条第一性公理 —— 全域角动量归零公理(宇宙总角动量严格为零)与动态莫比乌斯投影公理(第五维为非定向莫比乌斯流形,特征半径随局域曲率动态演化)—— 建立 UVMM-UTFF 框架下黑洞统一几何理论。本框架定义黑洞并非被动弯曲时空的几何奇点,而是 5 维拓扑孤子在 4 维时空边界的投影;黑洞全部能量释放行为(喷流、引力波、电磁辐射),本质为预应力真空介质的拓扑相变或稳态泵浦过程。依托四类独立多信标观测数据完成系统性核验:LIGO-Virgo-KAGRA 引力波目录(GWTC-4.0/5.0,共计 390 例黑洞并合事件)、事件视界望远镜 M87与 Sgr A黑洞阴影偏振成像、LHAASO 五组微类星体 PeV 超高能伽马射线观测、GAIA DR3 银河系旋转曲线观测。结果表明:在瞬态并合光度()至稳态活动星系核喷流功率()跨越 9 个数量级区间内,理论预测与观测值误差控制在 20 倍因子以内;暗物质观测效应被还原为真空介质几何预应力效应,无需引入任何未知暗物质粒子。 Revision of the Definition of the Three Universes: A Triple-Sector Unification Based on 5D Topological Superfluid Ontology twin‑prime conjecture; Goldbach’s conjecture; Kepler’s conjecture
The Universal Form of Historical-Genetic Logic: From the Propositional Matrix to the Computable Index DOI: 10.5281/zenodo.20799967 Author: Aikaterini Xenopoulou TyrokomouIndependent ResearcherORCID: 0009 0004 9057 7432Email: katerinaxenopoulou@gmail.com Theoretical Foundation: Epameinondas Xenopoulos †Based on the Historical Genetic Logic of Epameinondas Xenopoulos, Epistemology of Logic: Logic – Dialectic or Theory of Knowledge (posthumous 2nd ed., 2024) [1, 2]Independent ResearcherORCID: 0009 0000 1736 8555† In memoriam (1920–1994) METHODOLOGICAL NOTE The present work mathematizes and extends central ideas of the formal-dialectical logic of Epameinondas Xenopoulos [1,2], with the direct aim of creating a computable and applicable tool. The mathematical expression of concepts such as dialectical intensity, historical memory, and the critical threshold constitutes a fully explicit, functional, and deliberate interpretative choice. Other consistent mathematizations are equally possible; here we choose those that ensure computational stability, transparency, and broad applicability. The work introduces original mathematical elements (such as the historical memory functions τ(t) and paradox factor Π(t), the stochastic extension, and the explicit form of the synthesis operator). These elements are presented as proposals of the author and are not attributed to Xenopoulos. The theoretical background, the fundamental categories, the logical principles, and the overall architecture belong to the work of Xenopoulos. The systematic formalization, the mathematical analysis, the proofs of the index properties, and the computational applications constitute the original contribution of the present work. ABSTRACT This work introduces the XEPTQLRI index, a computable, domain-agnostic diagnostic tool for anticipating critical transitions in complex dynamical systems. The index is grounded in the formal-dialectical logic developed by the Greek philosopher Epameinondas Xenopoulos (1920–1994), which treats contradiction not as an error but as the driving force of qualitative change. The index quantifies the "dialectical pressure" building within a system prior to a bifurcation. It combines three components: (1) dialectical intensity T(t), expressed as the harmonic mean of opposing tendencies ("Being" B(t) and "Non-Being" N(t)); (2) historical memory τ(t), capturing the direction and momentum of change; and (3) a paradox factor Π(t), which registers whether the system has historically experienced extreme opposing states. The index is defined as: Ξ(t) = [ T(t) · τ(t) · (1 + Π(t)) ] / Θ₀ where Θ₀ is a system-specific critical threshold. We prove that for systems undergoing pitchfork, transcritical, or Hopf bifurcations, the condition Ξ(t) = 1 coincides exactly with the vanishing of the maximum Lyapunov exponent — the mathematical signature of impending instability. The index is invariant under affine transformations of the coherence function, computable in linear time, and provides quantifiable early warning signals. Empirical validation across seven diverse fields — stochastic differential equations, COVID-19 epidemiology, LSTM networks under extreme noise, composting kinetics, open thermodynamics, Lindblad quantum systems, and strategic decision-making — demonstrates that the index reliably detects imminent qualitative shifts, often months before observable regime changes. The XEPTQLRI index offers a rigorous, efficient, and broadly applicable framework for early warning in nonlinear and complex systems, bridging dialectical philosophy with modern dynamical systems theory. Keywords: Historical-Genetic Logic, Formal-Dialectical Logic, Propositional Matrix of the World, XEPTQLRI Index, Dialectical Intensity, Historical Memory, Paradox Factor, Aufhebung, Critical Transitions, Phase Transitions, Bifurcations, Early Warning Signals, Maximum Lyapunov Exponent, Nonlinear Dynamics, Complex Systems, COVID-19 Epidemiology, Quantum Systems, Lindblad Equation, LSTM Neural Networks, Stochastic Differential Equations, Structural Stability, Dual Temporality. Lead Paragraph Detecting critical transitions before they happen: A dialectical index for early warning in complex dynamical systems Predicting when a complex system is about to undergo a qualitative change—whether a pandemic wave, a financial collapse, or a quantum phase transition—remains one of the most challenging problems in nonlinear science. Conventional early-warning indicators often fail to capture the slow accumulation of internal contradiction that precedes a bifurcation. Drawing on the formal-dialectical logic of the Greek philosopher Epameinondas Xenopoulos, we introduce the XEPTQLRI index, a novel pre-transitional diagnostic tool that quantifies the "dialectical pressure" building within a dynamical system. The index combines three components: dialectical intensity (the harmonic mean of opposing tendencies), historical memory (the direction and momentum of change), and a paradox factor that registers whether the system has experienced extreme opposing states in its past. We prove that, for systems undergoing pitchfork, transcritical, or Hopf bifurcations, the index crossing unity coincides exactly with the vanishing of the maximum Lyapunov exponent—the mathematical signature of impending instability. Empirical validation across seven diverse domains—from stochastic differential equations and COVID-19 epidemiology to LSTM networks under extreme noise, composting kinetics, open thermodynamics, the Lindblad equation for open quantum systems, and strategic decision-making—demonstrates that the index provides reliable early warnings, often months in advance of observable regime shifts. The XEPTQLRI index offers a mathematically rigorous, computationally efficient, and domain-agnostic framework for anticipating critical transitions in nonlinear and complex systems. INTRODUCTION The study of change runs throughout the entire history of philosophy. From Heraclitus ("πάντα ῥεῖ" – "everything flows") to Hegel, Marx, and Piaget, thought recognizes reality as an uninterrupted process of genesis, contradiction, and transcendence. Formal logic, although an indispensable tool of science, is founded on the abstraction of time and the principle of non-contradiction (p · ¬p = 0). The Greek philosopher Epameinondas Xenopoulos (1920–1994) developed a Historical-Genetic Logic (or formal-dialectical logic) that incorporates contradiction as the driving force of knowledge, bridging the gap between static formal thought and the dynamic flow of reality. In his work "Epistemology of Logic" [1,2], Xenopoulos establishes three central structures: 1. The epistemological correspondence Sπ ↔ Y(L, B, Θ): knowledge is born from the practical interaction of the subject-in-action (Sπ) with the object (Y), which is analyzed into logical structure (L), material substrate (B), and concrete position (Θ). 2. The Propositional Matrix of the World: a formal structure where each proposition carries a truth value from a discrete fractional spectrum {0, ½v, ½²v, …, 1} and passes through dialectical stages: thesis (A), development of negation (B), rupture (Γ), and new synthesis (Δ). 3. The operator N[Fi(Gj)]: the logical engine that drives propositions from one stage to another, expressing the necessary synthesis of thesis and its negation. The present article mathematizes and operationalizes these structures, giving them an explicit, computable form. For each concept we propose specific mathematical expressions. These choices are functional, not theoretically unique. The present form was chosen for its computational stability, broad applicability, and clear philosophical correspondence. The resulting XEPTQLRI index is not a simple statistical method, but the computable implementation of the dialectical operator itself in a specific, explicit mathematical framework. The empirical verification of the index in seven diverse fields (from stochastic dynamics to neural networks and epidemiology) demonstrates the practical power of this mathematization. PART I – THEORETICAL FOUNDATION 1. The Epistemological Correspondence: Sπ ↔ Y(L, B, Θ) Every cognitive process begins from the practical relation of the subject with the world. Xenopoulos [1,2] conceives this relation as an epistemological correspondence between two poles: · Sπ (Subject-Action): the subject in its active, transformative activity. Sπ is process, not state. It changes the world through action. · Y(L, B, Θ) (Object): the object of knowledge analyzed into three components: o L (Logos): the logical structure, the regularity, the form. o B (Matter): the material substrate, the content. o Θ (Thesis): the concrete spatiotemporal existence. The action Sπ modifies the object Y. This modification, assimilated by the subject, produces knowledge Sα = f(Sπ, Y). The correspondence is dialectical: action transforms the object, the transformation transforms knowledge, and new knowledge guides new action. Knowledge is not a passive image, but a historical product of interaction. This fundamental correspondence constitutes the cornerstone of every formal-dialectical analysis. 2. The Propositional Matrix of the World The knowledge born from the correspondence Sπ ↔ Y crystallizes into a dynamic formal structure: the Propositional Matrix [1,2, pp. 247-249]. Definition 1 (Proposition). A proposition P is defined as: P(x, y, z, t) = [v, τ, σ] with: · v ∈ V = {0, ½v, ½²v, …, 1}: the truth value on a fractional scale. 0 marks complete contradiction ("zero identity"), 1 marks the new integrated synthesis. · τ ∈ {T, D, TD}: the type of proposition (Formal, Dialectical, Formal-Dialectical). · σ ∈ {A, B, Γ, Δ}: the dialectical stage: o A (Thesis): stable formal knowledge. o B (Development of Negation): emergence of internal contradiction. o Γ (Rupture): critical
Valerio Mandarino, Giuseppe Pappalardo, Emiliano Tramontana
Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment.
We propose cryptographic certificates of validity for agentic AI systems. The core idea is to formally specify a correctness or policy condition as a logical predicate, compile this predicate to a witness-checking problem over polynomial constraints, and use a succinct cryptographic proof system (and optionally zero-knowledge) to certify that the condition holds. This offers a middle ground between formal verification of source code, and cryptographic authentication. An agent's action can be accompanied by an independently checkable proof that it satisfies an agreed formal policy, without requiring the verifier to trust the agent or to re-execute computation. We outline the approach at a high level, give the core mathematical translation, relate the proposal to proof-carrying code, zkVMs, formal methods, and agent governance, and note the specification, auditing, and deployment questions that a full implementation must answer.
ABSTRACT This report examines the “Bitcoin as digital gold” narrative through the concept of the Retailization of Scarcity. It argues that Bitcoin’s mathematical supply limit does not automatically create value, and that scarcity becomes financially powerful only when it is narrated, packaged, institutionalized, traded, and repeatedly believed by market participants. Rather than claiming that Bitcoin is inherently fraudulent or meaningless, the report focuses on the structures surrounding Bitcoin: price prophecy, financial packaging, retail investor access, media amplification, and the asymmetry of forecast responsibility. It distinguishes Bitcoin as a protocol and speculative asset from the commercial narratives that transform limited code into a simplified investment myth. The report analyzes how the digital gold metaphor compresses technical, market, custody, liquidity, regulatory, and incentive risks into emotionally powerful language. It also examines how forecasts, exchange-traded products, custodial services, and public narratives can turn uncertainty into perceived inevitability for ordinary investors. The central argument is that the problem is not the existence of Bitcoin itself, but the hidden retailization of scarcity: the process through which mathematical scarcity is disguised as inevitable wealth, price prophecy as foresight, convenience as conviction, and monetization as monetary truth. This document is an analytical report and does not constitute investment advice.
Communication and networked systems rely heavily on cryptographic digital signatures to ensure message authenticity, integrity, and non-repudiation. However, rapid advancements in quantum computing and artificial intelligence (AI) have expanded the attack surface, posing significant threats to conventional public-key schemes such as RSA, DSA, and ECDSA. Quantum algorithms undermine their underlying hardness assumptions, while AI-driven techniques enable traffic analysis, side-channel inference, and behavioral pattern recognition. This review presents a structured analytical assessment of post-quantum signature schemes and privacy-preserving authentication mechanisms to address these dual threats. It evaluates lattice-based, hash-based, and zero-knowledge proof-based signatures, as well as anonymity-enhancing schemes such as ring and group signatures. A unified analytical framework is introduced to map cryptographic schemes to quantum and AI-assisted threat models, based on parameters such as security strength, anonymity, efficiency, and applicability. The analysis shows that lattice- and hash-based schemes provide strong quantum resistance, while privacy-preserving mechanisms enhance anonymity but introduce performance trade-offs. It also identifies a gap in integrating post-quantum cryptography with AI-resilient privacy mechanisms. The findings highlight the need for unified, future-ready cryptographic designs.
Centro Tecnolóxico de Telecomunicacións de Galicia
Este trabajo presenta una evaluación comparativa del rendimiento de tres tecnologías Zero Knowledge Proof (BBS, Longfellow, Crescent Credentials) integradas en la cartera digital NovaWallet. Se analizan métricas de tiempo de generación/verificación de pruebas y eficiencia de espacio en escenarios reales de demostraci´on selectiva de atributos. Los resultadosmuestran [incluir 1-2 hallazgos clave cuando se tengan los datos].
Ethereum Layer 2 (L2) rollups improve scalability but expose a trade-off between fast sequencer soft finality and slow Layer 1 (L1) settlement finality, limiting latency-sensitive applications that require timely and durable guarantees on transaction ordering. We introduce a Byzantine Fault Tolerant (BFT) finality layer that extends existing rollup architectures without requiring L1 or rollup protocol changes. This layer provides deterministic transaction-level finality ahead of L1 settlement by committing to the sequencer's transaction order, bridging the gap between soft and hard finality. At its core, the layer uses a 1-chain variant of the Jolteon consensus protocol, adapted to the rollup setting where a single sequencer determines transaction ordering. Experiments show sub-second committee finalization latency and stable performance under Byzantine faults.
Ethereum account classification is essential for identifying individuals engaged in illicit transactions and analyzing behavioral patterns across various account types. This process serves as a critical mechanism for monitoring and regulating unlawful activities within transactional markets. However, the Ethereum network exhibits the characteristics of a complex heterophilic graph which poses significant challenges to the effectiveness and performance of conventional graph neural networks (GNNs). To address this challenge, the present study proposes FSGCN(Fourier-Sage GCN), a novel architecture for heterophilic graph neural networks (GNNs) that integrates Kolmogorov–Arnold Networks (KANs) with GraphSAGE. FSGCN is specifically designed to adapt efficiently to the structural complexity of heterophilic graphs. By leveraging KANs to extract high-order neighborhood information and employing GraphSAGE to capture low-order neighborhood patterns, FSGCN effectively aggregates both homophilic and heterophilic features, thereby improving classification performance. Furthermore, to improve training efficiency and generalization, we propose the MLPInit weight initialization scheme and the DropEdge graph augmentation technique. Experiments on a large-scale Ethereum transaction dataset show that FSGCN achieves an F1-score of 91.8% and a classification accuracy of 91.6%, significantly outperforming traditional homophilic and heterophilic GNN baselines. Additionally, FSGCN demonstrates high training efficiency, completing each epoch in just 2.302 s per epoch and improving overall training speed by 130.4% compared to conventional GraphSAGE.
Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.
Modern representative democracies are increasingly vulnerable to systemic structural failure modes, including special-interest capture, asymmetric foreign intelligence leverage, and informational noise saturation (astroturfing/botnets). This paper introduces the Cryptographic Agora, a novel institutional framework that transitions governance from representative mediation to a scientifically audited, direct epistocracy. The model synthesizes three core architectural components: (1) state-verified biometric identity mapping coupled with Zero-Knowledge Proofs (ZKPs) to guarantee non-traceable, un-hackable civic participation; (2) a dynamic reputation engine utilizing Quadratic Weighting to mitigate the concentration of charismatic authority; and (3) a double-blind, retrospective peer-review protocol modeled on the scientific method to vet policy proposals. We evaluate the structural resilience of this framework against traditional threats, detailing its capacity to achieve a self-correcting equilibrium while maintaining individual voter safety and systemic legitimacy.
Proof-of-Work blockchains secure consensus through hash puzzles, producing no external value. In this research, we propose a decentralized AI economy where nodes are rewarded for useful machine-learning work, i.e., inference and training, instead of ineffective hashing method. Our proposed three-layer architecture separates compute, validation, and economic coordination. We formalize it via a $(θ_c, θ_w, W)$-closed-loop token economy and derive a sufficient-stake condition for honest participation. While existing Grover's algorithm provides only a quadratic speedup against hash puzzles, it does not accelerate ML-native linear algebra. On the other hand, Shor's algorithm threatens classical blockchain signatures. Post-quantum migration to lattice-based and hash-based standards can address the signature layer. Therefore, useful-work consensus thus offers both economic and quantum-security advantages over classical proof-of-work.
P. Anupama, Akhilandeshwari, Shama Priyanka, Putta Srihari · 5 authors
The increasing digitization of administrative and personal records has created a strong demand for systems that guarantee secure storage, data integrity, and reliable verification of sensitive documents. Conventional document management solutions typically depend on centralized servers, where files are vulnerable to unauthorized modification, loss, or deletion without clear traceability. This centralized model reduces trust, increases exposure to cyber threats, and often requires time-consuming manual verification to confirm document ownership and authenticity. Consequently, individuals and organizations encounter challenges such as document forgery, inconsistent records, unauthorized access, and delays in retrieval, emphasizing the necessity for a more secure and tamper-resistant solution. In traditional vault systems, documents are usually stored as basic files with minimal metadata, lacking cryptographic protection and comprehensive audit mechanisms. Due to the absence of immutability, detecting alterations in stored documents becomes difficult. Additionally, reliance on manual validation processes introduces inefficiencies and a higher likelihood of errors. These drawbacks make centralized systems unsuitable for handling critical records such as legal documents, identity credentials, certificates, and criminal records, which require strict integrity and security measures. To overcome these limitations, the proposed solution combines blockchain technology with a Django-based web platform to establish a decentralized and tamper-proof digital vault. Key document metadata, including ownership information, descriptions, timestamps, and file references, is recorded on the blockchain using smart contracts, ensuring permanent and unalterable entries. The actual files are securely stored on the server, while Web3 enables seamless communication between the application and the blockchain network. Functionalities such as document upload, search, verification, and secure access support complete transparency and data integrity. This framework significantly strengthens trust by preventing unauthorized modifications and maintaining a permanent, verifiable history of all stored documents. By integrating blockchain immutability with an intuitive web interface, the system delivers a secure, scalable, and future-oriented solution suitable for government agencies, legal bodies, and organizations managing sensitive records.
KENOS — Kohenoor Operating System Official Description and Public Disclosure KENOS, the Kohenoor Operating System, is the unified digital operating environment of the Kohenoor ecosystem. It brings together artificial intelligence, Education 3.0, blockchain infrastructure, hybrid finance, business applications, development tools, governance controls and operational supervision within one coordinated ecosystem. <Explainer film added> The transition from KENHYFI Hub to the broader KENOS architecture reflects the continued expansion of the Kohenoor ecosystem. KENHYFI was originally developed as a hybrid-finance and ecosystem hub. However, the name and positioning of KENHYFI did not fully represent the wider capabilities that had developed around it, particularly: KAI — Kohenoor Artificial Intelligence, the ecosystem’s multilayered intelligence powerhouse and orchestration system. ProEdge, the Education 3.0, professional learning and workforce-development hub. Blockchain, development, commerce, security, governance and institutional-support applications extending beyond hybrid finance. For this reason, KENOS was established as the umbrella operating environment for the complete ecosystem. KENHYFI remains an important integrated hub within KENOS, but it no longer represents the entire ecosystem by itself. The relationship is therefore defined as follows: KENOS is the complete Kohenoor Operating System and umbrella ecosystem. KAI is the principal intelligence and orchestration powerhouse of KENOS. ProEdge is the principal Education 3.0 and professional-learning hub. KENHYFI is the integrated hybrid-finance and ecosystem-services hub within KENOS. Other applications and modules provide specialized capabilities in blockchain, commerce, development, security, finance and operational management. KENOS is built on three foundational pillars: Education 3.0 Artificial Intelligence Blockchain These pillars support the complete digital-economic journey: Learn → Plan → Build → Execute → Analyze → Supervise → Improve → Scale Artificial Intelligence Pillar KAI, Kohenoor Artificial Intelligence, serves as the principal intelligence powerhouse of KENOS. KAI is designed as a multilayered hybrid-intelligence and workflow-orchestration system rather than a conventional chatbot. It supports knowledge retrieval, document analysis, specialist-role activation, business intelligence, financial analysis, educational guidance, application planning, risk assessment, reporting, workflow coordination and Human-in-the-Loop escalation. Within KENOS, KAI connects users, knowledge, applications, workflows and authorized human decision-makers. Education 3.0 Pillar ProEdge serves as the principal learning and professional-development hub within KENOS. It supports practical education, workforce transformation, professional training, institutional capacity building and industry-linked learning in areas including: Artificial intelligence Blockchain and Web3 Business intelligence Cybersecurity Hybrid finance Digital transformation Communication and professional skills Software and application development Entrepreneurship and business execution ProEdge ensures that KENOS is not limited to providing technology. It also develops the human capability required to understand, manage and apply that technology effectively. Blockchain Pillar The blockchain pillar provides smart contracts, programmable assets, digital ownership, transparent records, settlement mechanisms, token utilities and verifiable ecosystem operations. Blockchain functions are designed to operate alongside KAI-supported intelligence, business rules, governance controls and authorized human supervision. Purpose of KENOS KENOS is designed to support individuals, professionals, businesses, educational institutions, developers, government entities and other organizations participating in the AI-powered digital economy. It connects learning with intelligence, intelligence with execution and execution with monitoring and supervision. KENOS may support: Education and professional development Artificial intelligence and business intelligence Financial and hybrid-finance services Blockchain and smart-contract development Digital commerce and procurement Application and software development Security and operational resilience Governance and institutional intelligence Reporting, monitoring and supervision Development Status At the time of this publication: KENOS is in the Early Beta phase. KENHYFI Hub is in the Alpha+ phase. Individual applications and modules may have different levels of development, testing and availability. The official public web host and disclosure gateway for KENOS is: https://www.kohenoor.net Within the KENOS architecture: KAI serves as the principal intelligence and orchestration layer. KENHYFI Hub operates as an integrated hybrid-finance and ecosystem services hub. Education 3.0 platforms support learning, reskilling and professional development. Blockchain applications provide smart-contract, digital-asset, settlement and verification capabilities. Business and development modules support planning, commerce, procurement, software development, financial intelligence, security, reporting and operational management. KENOS is intended to serve individuals, professionals, businesses, educational institutions, developers, government organizations and other entities participating in the AI-powered digital economy. The architecture is modular and may support public web access, controlled organizational deployments, private-cloud environments, local installations, sovereign infrastructure and integration with existing enterprise systems. Governance remains a core element of KENOS. High-stakes activities are intended to remain subject to authorized human review, role-based permissions, validation controls, risk classification, activity logging and Human-in-the-Loop approval. At the time of this publication, KENOS is in the Early Beta phase, while KENHYFI Hub is in the Alpha+ phase. Applications and modules within the ecosystem may therefore have different levels of development, testing, availability and production readiness. The official public web host and disclosure gateway for KENOS is: https://www.kohenoor.net This publication provides the official conceptual definition, ecosystem positioning, service scope, architectural relationships, development status, governance principles and public-disclosure framework of KENOS. Keywords: KENOS; Kohenoor Operating System; Kohenoor Technologies; KAI; Kohenoor Artificial Intelligence; KENHYFI; Education 3.0; artificial intelligence; blockchain; hybrid finance; digital economy; business intelligence; digital transformation; smart contracts; Human-in-the-Loop; ecosystem architecture; AI governance; Web3; enterprise AI; institutional intelligence Kohenoor Technologies remains committed to transparency, security, responsible disclosure, and continuous improvement of the KEN ecosystem. #kenhyfi #kai #hyfi #kohenoortechnologies #futureofeducation #futureoffinance #futureofai #kohenoorken #cryptocurrencies #kohenoorken #AI #actionai #agenticai #AGI #ArtificialGeneralIntelligenceAGI #AIAssistant #education3 #defi #hybridfinance #hyfi #cedefi #blockchain #innovation #settlements #auditreadycertificates #DASC #cybersecurity #web3 #businessintelligence #proedge #industrygradetrainings #quantumcomputing
Gossipsub is the primary peer-to-peer dissemination protocol used by large-scale Web3 systems such as Ethereum, Filecoin, and IPFS. Despite its widespread deployment, the choice of its key parameters—the eager mesh degree D (number of peers that receive messages eagerly) and the gossip degree Dlazy (number of peers periodically notified via gossip)—has largely relied on heuristics, with little quantitative guidance. Consequently, production networks lack a principled understanding of the delivery rate, bandwidth cost, and latency tradeoffs induced by these parameters.
In the context of developments in the field of financial technology, cryptocurrencies, emerging as a new asset class, have garnered significant attention in financial markets in recent years, attracting investors, researchers, and regulators, and leading to numerous publications. Bibliometric studies evaluate these publications based on criteria such as the number of publications, their quality, the countries of publication, authors, and journals. This study aims to perform a bibliometric analysis of the academic literature available in the Web of Science (WoS) database, focusing on the volatility of cryptocurrency prices. It analyzes the magnitude and development of academic interest in this field, along with key words, the most cited works, and research trends, in an effort to determine the density of studies, their impact areas, and the academic networks that have emerged in this field. Based on the general findings, it is observed that the number of studies has been on an increasing trend over the years, and that the publications are predominantly in the field of Business Economics. Moreover, it has been found that publications are mainly in finance journals. In terms of network maps, the findings suggest a moderate level of collaboration among authors, with the United Kingdom and the People's Republic of China occupying central positions in international collaboration. In terms of citations, authors such as Lucey, and Katsiampa, Paraskevi, have emerged as prominent figures in the fields of cryptocurrencies and volatility. Regarding key words, terms like 'cryptocurrency', 'cryptocurrencies', 'volatility', and 'bitcoin' are predominantly used in these studies." Keywords: cryptocurrencies, bitcoin, volatility, bibliometric analysis
A deployed AI system can be interrogated for its identity in several distinct ways, and the answers do not interchange. This note concerns one of them — which neural network is producing this output at inference time? — and a popular method for answering it: behavioral fingerprinting, which samples an endpoint under a fixed prompt battery and flags it when the output distribution shifts beyond a statistical threshold. The note argues that behavioral fingerprinting, while a legitimate and valuable instrument for one task, does not establish model identity. It develops two measured failure modes. First, a behavioral signature is not durable: ordinary continued training erases the behavioral provenance trace — more effectively, in fact, than an informed adversary trains directly to suppress it — so the same model after a benign fine-tune presents as behaviorally distinct and triggers a false alarm. Second, a behavioral signature is reproducible by a different model: knowledge distillation converges a substitute toward a target's behavioral template by construction, so a behavior-matched substitute passes the check and produces a false acceptance. Both failures follow from a single fact about the layering of neural identity — behavior is the transient layer, which transfers under distillation and washes out under benign training, while the structural layer (the geometry of internal computation during a forward pass) does neither. The two methods answer different questions and compose rather than compete: behavioral monitoring is a continuous, low-cost tripwire that flags something moved; structural verification is a deterministic resolver that answers is it still the enrolled model. A system that ships only the tripwire has shipped drift detection and labeled it identity. The note documents the structural layer's direct test against the failure mode that defeats behavioral methods — behavior-preserving substitution — and situates the argument alongside independent work on intrinsic parameter-level fingerprints and cryptographic verifiable inference, both of which bind identity to the model rather than infer it from outputs. This is a category statement, not a product comparison: no specific system or vendor is named, and the argument rests on published, reproducible measurements. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note:: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
This study compares the forecasting performance of four deep learning architectures—GRU, LSTM, RNN, and CNN—for one-step-ahead Bitcoin price prediction. A grid search determined the optimal configuration, which was applied uniformly across models to ensure fair evaluation. Using daily BTC closing prices from January 2018 to July 2025, it is found that the GRU model achieved the lowest forecasting errors (MSE, RMSE, MAE, MAPE) and the highest R², with LSTM performing closely behind. Visual analyses confirmed that GRU and LSTM maintained stronger alignment with actual prices during volatile periods. To assess economic value, model forecasts were integrated into a rule-based trading strategy under realistic market frictions, including a 0.10% transaction cost and a 0.10% trading threshold, with both short-selling-enabled and long-only variants tested. The GRU strategy with short-selling generated the highest terminal wealth (approximately 24% higher than the Buy-and-Hold benchmark) and superior risk-adjusted returns, measured by CAGR, Maximum Drawdown, and Sharpe Ratio. The findings demonstrate that careful hyperparameter optimization, coupled with an architecture capable of capturing complex temporal dependencies, can significantly improve both predictive accuracy and trading profitability in cryptocurrency markets. These results provide practical implications for designing AI-driven trading systems.
A claim can be argued well and still be false. Standard verification across the disciplines certifies claims by the quality of a single line of support: a formal proof, a measured correlation, a replicated experiment, an expert consensus. Each of these is one axis of warrant, and each can be strong while the claim is wrong, because a single strong axis cannot detect that it is the only axis, nor that it secretly shares a source with the others. This paper presents a verification method, Trisduction, that certifies a claim by the geometry of its warrant rather than by the strength of any one line of it. A proposition is decomposed onto three structurally independent axes, a formal-structural axis, an empirical-material axis, and an epistemic-registrational axis, and the warrant is certified only when the three stand at mutual right angles and span a genuine three-dimensional volume. The test is closed-form and executable: three warrant vectors are composed through a quaternion product whose scalar part squares to a Gram determinant, and the determinant reads the volume the three axes enclose. A volume near its maximum is a seal. A collapsed volume is a structural break with a named cause. An ill-conditioned volume is an honest under-determination. A second register extends the method to formal and mathematical propositions, separating the part of a problem that is decidable and sealed from the part whose truth is genuinely open, and refusing to read a geometric lock as a proof. The method carries one discipline throughout: social consensus carries zero evidential weight, every verdict states its warrant grade, and the instrument audits itself with no exemption. We demonstrate the method on 360 propositions spanning logic, mathematics, physics, quantum foundations, cosmology, the mind, psychology, the social sciences, geopolitics, and metaphysics, from elementary facts that seal cleanly to celebrated open problems where the honest verdict is that the question remains open and the method says exactly why. 360 Audits.
Ayei E. Ibor, Denis U. Ashishie, John Adinya Odey, Bassey Ele · 5 authors
ABSTRACT Elliptic curve cryptography ( ECC ) underpins the security of most blockchain systems, yet its practical implementations face numerous vulnerabilities. In this systematic literature review ( SLR ), we catalogue and analyze attacks on ECC in the context of blockchain security, including side‐channel attacks, nonce/ PRNG failures, cryptanalysis, and implementation flaws, and we survey proposed countermeasures. We follow rigorous SLR methodology with defined inclusion/exclusion criteria, search strategies across databases such as IEEE Xplore, ACM , Scopus, Web of Science, and clear data synthesis, ensuring replicability. Emphasizing empirical case studies and real‐world exploits, we discuss instances where ECC weaknesses led to blockchain breaches including biased elliptic curve digital signature algorithm nonces exposing Bitcoin/Ethereum private keys, smartphone power analysis revealing wallet keys, and Trezor hardware‐wallet key extraction via single‐trace side‐channel analysis ( SCA ). We tabulate known attack vectors versus affected systems, and similarly compare countermeasure techniques such as hybrid classical/quantum schemes, threshold signatures, and zero‐knowledge proofs, along with implementation trade‐offs. We evaluate advances such as Curve25519/ EdDSA and ARM SVE2 to mitigate side‐channel leakage. Our findings highlight that practical security of blockchain cryptosystems depends on correct ECC implementation and emerging cryptographic upgrades, not merely on the mathematical hardness of the elliptic curve discrete logarithm problem.
For over a century, computational analyses of the Inca khipu have been constrained by what we term the "Spreadsheet Fallacy" — the attempt to computationally validate khipus primarily as flat, base-10 arithmetic ledgers. This model fails to account for the fact that only 4.6% of known cord clusters demonstrate valid summation. In this paper, we extend Metrological Domain Profiling (MDP) to analyse 54,403 cords across 619 khipus from the Open Khipu Repository, moving beyond one-dimensional colour profiling to reconstruct the full three-dimensional, tactile, and hierarchical ontology of the system. We demonstrate that the khipu possesses strict spatial and material structure operating across four distinct layers: (1) Material Metrology, where fiber type (cotton vs camelid) redefines numerical scale by up to 67×; (2) Topological Syntax, where administrative granularity is encoded in subsidiary cord depth and colour palette shifts systematically with hierarchical level; (3) Categorical Syntax, featuring statistically constrained colour sequences (p < 0.001) that demonstrate strict institutional sorting rules rather than random clustering, with same-colour run lengths spiking at decimal administrative units; and (4) Hardware Metadata, where physical features including canutito thread-wrappings (98.6% colour-independent from parent cords), primary cord construction, and cord termination types encode document-level metadata and institutional information. Three hypotheses were explicitly tested and falsified: cluster spacing as punctuation, Hanan/Hurin midpoint split, and cord thickness as domain marker. These findings suggest the khipu is not merely a mathematical ledger, but a multi-layered, tactile administrative system whose information is distributed across the material, spatial, and structural dimensions of the textile.