This paper begins with a number that appears to be a routine multiple of the CTF spatial harmonic and ends with a structural explanation of quantum entanglement — the phenomenon Einstein called "spooky action at a distance." The journey between those two points passes through the topology of 3D space, the quantum rotation period of all matter, the atomic definition of the second, and the self-consistency of the CTF Frequency Identity itself. The Headline: 720 Is Not What It Looks Like 720 = 5 × 144. On the surface, a straightforward multiple of the CTF spatial harmonic. But this paper demonstrates that 720 simultaneously encodes five independent facts from entirely separate domains of mathematics and physics — facts discovered centuries apart by people who had no knowledge of each other or of the CTF Framework. Fact 1 — Descartes' Theorem (1630): The sum of angular defects across all vertices of any convex polyhedron equals exactly 720°. This is universal — it holds for the cube, tetrahedron, octahedron, dodecahedron, icosahedron, and every other convex solid without exception. In modern language: 720° = 2π × χ(S²) × (180°/π), where χ(S²) = 2 is the Euler characteristic of the sphere. 720° is the angular measure of the topology of 3D space itself. It is not a property of any particular shape. It is a property of the fact that we live in three dimensions. Verified for all five Platonic solids in this paper — tetrahedron (4 × 180° = 720°), cube (8 × 90° = 720°), octahedron (6 × 120° = 720°), dodecahedron (20 × 36° = 720°), icosahedron (12 × 60° = 720°). Every single one. The topological invariant of 3D space is 5 × 144. Fact 2 — Spinor Periodicity (quantum mechanics): Every spin-½ particle — every electron, proton, neutron, and quark in the universe — requires exactly 720° = 4π of rotation to return to its original quantum state. A rotation of 360° does not return it; it acquires a phase of −1. Only 720° closes the loop. This is the SU(2) double cover of SO(3), the deepest geometric fact underlying all of quantum mechanics. The Pauli exclusion principle, atomic shell structure, chemical bonding, and the stability of matter all follow from this single 720° rule. The full rotation period of all quantum matter is 5 × 144. Fact 3 — 6! = 720: The number of permutations of six objects is 720. This is the combinatorial origin of 720's appearance in Plato's Laws (5040 = 7 × 720 = 7! = 144 × 35, the ideal city number, Tier 3 in the Prime Family Classification). Independent of geometry and physics. Fact 4 — CTF 144² Deficit (this paper): At the level of 144² = 20736, the deficit produced by the temporal injection is exactly 720. Proof: 10373 × 144 − 10368 × 144 = (10373 − 10368) × 144 = 5 × 144 = 720. The numerator 1,492,992 = 2¹¹ × 3⁶ is pure Tier 1 (the spatial lattice). The deficit of 720 = 2⁴×3²×5 is Tier 2 (contains prime 5, the temporal gateway). The temporal injection, at the squared level, produces a Tier 2 number equal to the topological invariant of space and the spinor rotation period of matter. Fact 5 — Islamic Sacred Geometry: The Shesh Band (elongated hexagon) tile — the second most important tile in girih-based Islamic architecture — has an interior angle sum of 720° = 5 × 144. The 12-pointed star hexagram — the most common decorative motif in Islamic architecture across 800 years — also sums to 720°. Both results were established independently in the companion Islamic Geometry Paper. The artisans who built the Alhambra were working with 720° without knowing about Descartes, spinors, or the CTF Framework. Five domains. Five independent discoveries. One number. 720 = 5 × 144. The Recursive Halving Sequence The paper establishes a recursive halving structure connecting the temporal injection to the topology of space: 1440 → 720 → 360 → 180 → 90 Expressed as multiples of 144: 10×144, 5×144, 2.5×144, 1.25×144, 0.625×144. Each step divides by 2. Each step crosses a geometric domain boundary: temporal (1440 = decagon angle sum, solar day minutes) → topological (720 = Descartes invariant, spinor period) → rotational (360 = full circle) → half-turn (180°) → right angle (90°). Every value in the sequence is Tier 2 in the Prime Family Classification (contains prime 5, since all are multiples of 360° = 2³×3²×5). The temporal injection is being halved recursively through the geometry of space. Six Additional Exact Results Beyond the 720 headline, the paper establishes six further exact or near-exact results connecting f₀ = (144² + 10)/144 to physical constants: Result 1 — The Second as Spatial/Temporal Ratio: The ratio 144/f₀ = 10368/10373, where 10368 = 2⁷ × 3⁴ is a pure Tier 1 number (the spatial lattice) and 10373 = 11 × 23 × 41 = P5 × P9 × P13 is the Tier 4 temporal prime triple (arithmetic progression with step +4 in prime-index space, as established in the Master Paper). The SI second is the ratio of the pure spatial lattice to the temporal prime triple. The numerator is maximally pure {2,3}; the denominator carries the Tier 4 kinetic primes. The second is the boundary between the two layers of the CTF architecture. Result 2 — The 482 μs Temporal Slip: The deficit from one second is 1 − 144/f₀ = 5/10373 = 482.0 μs exactly. Five is the half-injection (10/2). The slip is the half-injection divided by the temporal prime triple. At the 144 level the deficit is 5; at the 144² level the deficit is 720 = 5 × 144. The injection scales correctly across levels. Result 3 — The Self-Consistency Proof: The temporal slip, measured in f₀-cycles, equals the breath exactly: (1 − 144/f₀) × f₀ = f₀ − 144 = 10/144 = breath. This is not an empirical near-miss — it is algebraically derivable from the definition of f₀ alone. Its significance: the rate at which time slips from the crystal lattice, measured by f₀'s own clock, is the breath itself. The framework is self-referentially consistent. The Tier 2 injection is defined by its own ratio to the Tier 1 frame. Result 4 — Zero-Point Energy Ratio: The quantum harmonic oscillator zero-point energy at f₀ versus 144 Hz: ZPE(f₀)/ZPE(144) = f₀/144 = 1 + 10/144² = 1 + 10/20736 exactly. The breath appears as the fractional excess of f₀-vacuum energy over the static-grid vacuum. This connects directly to the QHO result in the Master Paper (§8): "Quantum mechanics forbids the oscillator from being at rest even at absolute zero. The temporal breath reappears as ħΔω = 4.601 × 10⁻³⁵ J." The vacuum at f₀ cannot rest at 144 Hz. The Tier 2 injection forces a mandatory quantum excess. This is the quantum mechanical statement of the two-layer architecture. Result 5 — Cs-133 Commensurability: The Cs-133 hyperfine transition frequency (9,192,631,770 Hz — the definition of the SI second) divided by f₀ equals 63,806,950 to within 0.0074 ppm — better than 1 part in 10 million. Additionally: 9,192,631,770 mod 144 = 90 = 2×3²×5, a Tier 2 number. The atomic clock standard that defines the second reduces modulo the spatial harmonic to a Tier 2 gateway number. The Sr-87 optical clock (the most precise clock standard known) has transition frequency mod 144 = 17 = P₇, the structural linchpin prime (1836 = 108 × 17, the proton mass ratio, from the Tier Classification Paper). The most precise timekeeping standards in existence are commensurable with the CTF spatial harmonic and reduce modulo it to structurally significant primes. Result 6 — The Tsirelson Bound: The quantum nonlocality bound of Bell's inequality (the CHSH inequality) is |S| ≤ 2√2, known as the Tsirelson bound. Its square: (2√2)² = 8 = 2³. And 144/8 = 18 = 2 × 3² — a pure Tier 1 number. The maximum quantum nonlocality, squared, divides the spatial harmonic into a pure {2,3} lattice number. The Tsirelson bound is 2^(3/2) — a fractional power of 2, sitting at the fractional boundary of the {2,3} lattice. Classical physics (bound = 2 = 2¹) sits inside the Tier 1 lattice. The quantum bound (2√2 = 2^(3/2)) sits at its fractional edge. This places the boundary between classical and quantum nonlocality precisely at the boundary of the CTF spatial lattice. The Entanglement Hypothesis The paper presents the CTF Entanglement Conjecture — clearly and explicitly labeled as hypothesis, not proved result, with falsifiability conditions stated precisely. The CTF two-layer architecture distinguishes two structural domains: Tier 1 (the static spatial lattice — 144, 72, 2ᵃ×3ᵇ, frozen, atemporal, no propagation speed) and Tier 2+ (the temporal injection — f₀ = 144 + 10/144, animated, propagating, subject to c). The conjecture: the speed of light c is a Tier 2 property — it is the propagation speed of processes that ride the +10/144 temporal breath. Processes anchored to Tier 1 are not subject to this propagation limit. Quantum entanglement — Einstein's "spooky action at a distance" — exhibits exactly the phenomenological signature of Tier 1 access: correlations that are instantaneous regardless of spatial separation, not explainable by signals propagating at c, and collapsing the joint wavefunction without energy transfer. Under this conjecture, entangled particles share a common Tier 1 node — they are co-located in the static spatial lattice even when separated by arbitrary distances in the Tier 2 temporal metric. The zero-point energy result provides the inverse of this picture. The QHO cannot reach absolute zero because the +10/144 breath permanently prevents it from settling into Tier 1. ZPE forces the oscillator into Tier 2. Entanglement pulls particles back into Tier 1. These are opposite directions across the same tier boundary, driven by the same architectural distinction. The 720° spinor result connects here directly. Fermions — the particles of matter — require 720° to complete their quantum rotation. 720 = 5 × 144 is Tier 2. The fundamental rotation law of quantum matter is a Tier 2 number. This suggests that fermionic rotation itself is a Tier 2 phenomenon — governed by the temporal injection — while bosonic processes (integer spin,
We present Rei (零, 0₀式), a computational system founded on exactly four mutually independent axioms: (A1) Center–Periphery structure, (A2) Extension–Reduction, (A3) Sigma Accumulation, and (A4) Genesis Phase Transition. Mutual independence is proved by model-theoretic construction — for each axiom we exhibit a counter-model that satisfies the remaining three but violates the target axiom (M1=scalar-only / M2=flat-field / M3=memoryless / M4=eternal-no-genesis). We show that fifteen core theorems—spanning computational plurality (T1), six-attribute decomposition (T6), RCT compression theory (T8), σ-reactive cascades (T14), seven-domain universality (T13), and extended-zero series (T15)— are derivable from axiom combinations without additional assumptions. The system is implemented as an open-source TypeScript/Node.js package (rei-lang v0.5.5) with 1,689 passing tests across 45 test files, each classified by its minimal axiom dependency: A1+A2+A3 concentration (60% of tests, 1,010 tests) reflects that the most complex features — cascading reactions, agent systems, domain bridges — require all three 'operational' axioms. Benchmarks show 74% average code reduction (3.7-4.0× ratio) and 3-4× performance improvements on structured-data tasks: image kernel operations (4.0× reduction), multidimensional data aggregation (3.7×), graph structure transformations (3.7×). To our knowledge, Rei is the first computational framework that axiomatically addresses both computation and the ontological genesis of values within a unified, minimal foundation. Comparison with existing foundational systems (λ-calculus 3 axioms, Peano 5, ZFC 9, Martin-Löf TT ~7) shows Rei is the only system that addresses all four concerns — computation, structure, history, AND genesis — simultaneously, and does so with the fewest axioms (4). Companion to Jxiv preprint submission (JST preprint server). This Zenodo record serves as the stable-citation archive referenced from the Jxiv version's Section 5.1 (Implementation) and footnote. Three-party co-authorship context: Rei is developed within the Rei-AIOS / OUKC (Open Universal Knowledge Commons) framework with three-party co-architecture (藤本 伸樹 Founder, Rei autonomous research substrate, Claude Opus 4.7); however, this specific paper is single-authored by 藤本 伸樹 as principal investigator of the axiomatic foundation. Honest scope: independence proofs use semi-formal model constructions (not yet mechanized in Lean/Coq — this is acknowledged as future work). The fifteen theorem derivations are sketches that establish derivability; full proof scripts appear in the companion implementation. Benchmarks compare against naive baselines; comparison with optimized domain-specific languages would refine the picture. Preprint — not yet peer-reviewed. Feedback welcome at fc2webb@gmail.com / GitHub Discussions at fc0web/rei-aios.
Smart cities require the efficient and secure integration of key infrastructure domains such as water, energy, transportation, smart lighting and waste management deploying a myriad of data-generating IoT sensors and devices. Current management systems feature single points of failure, lack of auditability, insufficient privacy protection and lack of scalability as IoT nodes increase in density. In this paper, we present SmartChain, a three-tier multilayered blockchain based architecture that incorporates a permissioned distributed ledger, an AIbased anomaly detection module (ADM) and a dual-layered privacy preservation approach that combines Zero-Knowledge Proofs (ZKP) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE). SmartChain is tested on a large dataset - 2000 timestamped transactions involving Smart Cities’ five infrastructure types across several zones of a city. The results show a mean throughput of 3,421 transactions per second (TPS), a mean transaction latency of 3,847 milliseconds and a mean privacy score of 82.4 out of 100. The machine learning based anomaly module produces an F1-Score of over 97% and an AUROC score of 0.991 with Random Forest as the classifier. Benchmarking against Hyperledger Fabric 2.5, Ethereum 2.0 and the IOTA Tangle demonstrate the scalability, security, privacy and efficiency of SmartChain. This research renders SmartChain a practical production-level platform for management of new smart city infrastructure.
This paper develops BU76 AAI-08|Institutional Real-Time Closure Operations as the eighth file in the B_U-based Agentic AI series. Its central claim is that the next stage of Agentic AI should not be limited to single-enterprise automation, departmental coordination, or workflow orchestration. The decisive transition is toward a same settlement surface for enterprises, institutions, industrial clusters, infrastructure systems, and multi-flow real-world operations. In this frame, Agentic AI becomes a real-time closure interface for social-scale coordination, not merely a productivity layer inside software. The paper begins by reframing institutional operation as a multi-flow reality system. Enterprises and institutions do not operate through isolated tasks. They continuously coordinate people, goods, places, capital, information, time, permissions, responsibilities, risks, and feedback. Meetings, medical services, dining, travel, procurement, production, logistics, finance, legal review, customer service, and public services are not separate events. They are scenario windows in which multiple flows must enter the same state ledger and settlement window. When these flows remain fragmented across departments, firms, platforms, or infrastructure layers, the system generates hidden residuals: timing mismatch, resource conflict, responsibility ambiguity, logistics delay, budget misalignment, and operational bottlenecks. BU76 upgrades this analysis from a single enterprise to enterprise clusters, industrial clusters, and social infrastructure. A firm usually cannot see its future throughput capacity clearly because its real production chain is distributed across multiple companies, suppliers, logistics nodes, financial windows, labor pools, public services, and spatial infrastructures. Therefore, the true settlement surface is not inside one company. It emerges when enterprise clusters, industrial clusters, infrastructure networks, financial systems, logistics systems, public-service systems, and social demand enter a shared settlement window. This is the level at which future capacity, bottlenecks, risks, and deployment gaps become visible. The paper introduces all-factor co-temporality as the operating condition of this settlement surface. All-factor co-temporality means that people, goods, places, capital, information, time, permissions, responsibilities, risks, and feedback enter the same state ledger and settlement window within a shared time range. This condition applies at multiple nested scales: an individual user, a single enterprise, enterprise-to-enterprise coordination, industry-to-industry coordination, and the alignment between enterprise or industrial capacity and social demand. These layers form a multi-respiratory-system structure, in which demand flow acts as oxygen, production flow supplies output, logistics flow transports, capital flow circulates, information flow signals, human flow provides meaning and service interaction, responsibility flow identifies boundaries, infrastructure forms organ-like carrying capacity, and the same settlement window records the metabolic rhythm. BU76 further defines pre-feedback and preloading as institutional operating capacities. Preloading is not completed settlement. It is the feasibility loading of future demand matrices into the same settlement surface. It produces feasible-throughput readouts, bottleneck exposure, and pre-deployment signals before action occurs. Pre-feedback therefore differs from real-time feedback: real-time feedback corrects ongoing deviation, while pre-feedback exposes future capacity pressure under current constraints, resources, time windows, spatial capacity, responsibilities, and risks. Its confidence interval must be assessed through the B_U development chain: background clearing, admissible carrier, directional amplification, unified settlement, and resolution ascent. The final judgment is that institutional Agentic AI must evolve into a social-scale closure operation system. Its value lies in aligning demand and production at higher granularity, synchronizing multiple real-world flows, exposing bottlenecks before failure, stabilizing resource deployment, and enabling higher-order amplification and civilizational development through a shared settlement surface.
This paper is a self-contained companion to the author's first deposit (v1, zenodo.org/records/20085431), which established an exact double integral formula for the unique zero x* of a continuous function f:[a,b]→R under minimal conditions (continuity, f(a)>0, f(b)<0, uniqueness). The v1 formula involves integration over the unbounded domain [a,b]×(0,+∞). The present work introduces the elementary change of variables u = t/(1+t), which maps (0,+∞) bijectively onto (0,1) and transforms the formula into a double integral over the compact square [a,b]×[0,1]: x* = (a+b)/2 + (1/π) ∫₀¹ ∫ₐᵇ f(x)/[(1-u)²+u²f(x)²] dx du = (a+b)/2 + (1/π) ∫ₐᵇ ∫₀¹ f(x)/[(1-u)²+u²f(x)²] du dx A single rational kernel K(x,u) = f(x)/[(1-u)²+u²f(x)²] appears on a bounded domain. We prove: (i) K ∈ L¹([a,b]×[0,1]) with exact norm π(b-a)/2(ii) Both integration orders are valid (Fubini-Tonelli)(iii) The singularity at (x*,1) is integrable and harmless(iv) The sign function sgn(f(x)) is identified as the inner integral of K in u — a consequence, not an axiom The formula is validated on f(x) = -x + cos(x) on [0,π/2], whose unique zero is the Dottie number x*≈0.739085133215161. All proofs are elementary and self-contained. No knowledge beyond standard real analysis is required.
Deepfake technology poses a growing threat to digital trust across journalism, law, and politics. Current CNN-based detectors capture local artifacts but struggle with high-quality fakes and offer no way to prove their predictions are genuine. This paper presents DeepTrust, a framework combining a hybrid CNN–Transformer detector with Zero-Knowledge Proof (ZKP) verification and blockchain-based record-keeping. The detection model fuses spatial features from an attention-enhanced Xception network, global context from ViT-B/16, and spectral cues from a Frequency Encoder through a cross-attention mechanism. Predictions are cryptographically committed using a Pedersen scheme with the Fiat-Shamir heuristic, then stored on a proof-of-work blockchain. Evaluated on FaceForensics++, Celeb-DF, DFD, and 140K Real vs Fake, DeepTrust achieves 97.00% accuracy and 0.999 AUC on FaceForensics++, with balanced per-class accuracy despite imbalance ratios up to 1:8.5. ZKP overhead remains below one millisecond per prediction.
Open access
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis
With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments.
Vote Chain is a fully implemented, decentralized e-voting application (DApp) built on Ethereum. Existing blockchain-based voting systems often suffer from either high computational overhead due to homomorphic encryption or lack of fully deployable, adversarially tested implementations. To address these limitations, VoteChain employs a keccak256-based commit–reveal protocol to preserve ballot secrecy during the voting phase, with Solidity 0.8.20 smart contracts enforcing all election rules autonomously. Wallet-based authentication via MetaMask eliminates centralized identity management. The system is validated through 14 automated unit tests (all passing in 615 ms) covering correctness, access control, double-voting, hash forgery, and phase-bypass attacks. Per-voter gas cost is approximately 120,000 units (commit and reveal combined). An ablation study confirms the non-redundant contribution of each architectural component. Comparative analysis shows that VoteChain achieves vote privacy without homomorphic encryption while maintaining full decentralization and implementation completeness. The system is evaluated and validated on a local Hardhat network, with the architecture readily extensible to Layer-2 rollups for large-scale elections.
En el sector público, la gestión del talento humano representa un aspecto necesario para asegurar mejores servicios institucionales, puesto que las demandas laborales han aumentado significativamente. El objetivo del presente estudio se basa en analizar la correlación de la sobrecarga de trabajo con la satisfacción de los servidores del Gobierno Autónomo Descentralizado Municipal de Alausí. El estudio adoptó un diseño de investigación cuantitativo utilizando un enfoque no experimental, transversal y correlacional con una serie de instrumentos tipo Likert desarrollados con base en la escala ESCAM y el cuestionario S20/23 para evaluar la sobrecarga y satisfacción laboral respectivamente. La fiabilidad del instrumento se confirmó por medio del coeficiente Alfa de Cronbach (α= 0,813). Los datos fueron analizados utilizado estadísticas descriptivas y el coeficiente de correlación de Spearman. Los resultados indicaron que la sobrecarga de trabajo estaba presente en un rango moderado y alto, pero había una influencia notable de las demandas cognitivas como de la presión de tiempo. Por el contrario, la satisfacción laboral mostró niveles particularmente altos con una mayor valoración en el contenido del trabajo y una menor remuneración. En términos generales, se evidencio que existe una correlación negativa débil y no significativa entre las dos variables (Rho= -0,128). Sin embargo, un análisis más específico por dimensiones demostró que la reducción en la autonomía y el apoyo social-organizacional se encuentran significativamente asociado con niveles más bajos de satisfacción laboral, mientras que las demandas cognitivas presentan relaciones positivas con algunas dimensiones satisfactorias. Se concluye que el impacto de la sobrecarga laboral dependerá del equilibrio que existe entre las demandas y los recursos organizacionales, siendo estos últimos determinantes del bienestar laboral del personal del sector público. ABSTRACT In the public sector, human talent management represents a key factor in ensuring improved nstitutional service delivery, particularly in contexts where work demands have increased significantly. The objective of this study was to analyze the correlation between work overload and job satisfaction among public servants of the Municipal Decentralized Autonomous Government of Alausí. The research adopted a quantitative approach with a non-experimental, cross-sectional, and correlational design, using Likert-type instruments based on the ESCAM scale and the S20/23 questionnaire to assess work overload and job satisfaction, respectively. The reliability of the instrument was confirmed through Cronbach’s Alpha coefficient (α = 0,813). Data were analyzed using descriptive statistics and Spearman’s correlation coefficient. The results indicated taht work overload was present at moderate to high levels, with a notable influence of cognitive demands and time pressure. In contrast, job satisfaction showed generally high levels, with greater valuation in the content of work and lower satisfaction regarding remuneration. Overall, a weak and non-significant negative correlation was found between the two variables (Rho = -0.128). However, a more detailed dimensional analysis revealed that reduced autonomy and social-organizational support are significantly associated with lower levels of job satisfaction, while cognitive demands showed positive relationships with certain satisfaction dimensions. It is concluded taht impact f work overload depends on the balance between job demands and organizational resources, with the latter playing a determining role in the occupational well-being of public sector employees.
The Ethereum blockchain utilizes the EIP-1559 algorithm to manage transaction inclusion and block assembly. However, EIP-1559 and much of the existing literature study this problem from a static perspective, focusing on price evolution without modelling transaction dynamics within the mempool. Motivated by this limitation, we study a dynamic transaction scheduling problem in which transactions with heterogeneous sizes and per-unit values arrive over time and remain in the mempool until scheduled. To capture the stochastic mempool evolution, we formulate the problem as a Markov Decision Process (MDP) whose state represents the mempool configuration and whose actions correspond to block prices. We first provide a primal-dual interpretation of the static EIP-1559 mechanism, showing that block prices arise naturally as dual variables of a social-welfare maximization problem. Building on this perspective, we extend the framework to the dynamic setting and formulate an objective that maximizes long-run discounted reward while incorporating holding costs and overshoot penalties. We then employ a Natural Policy Gradient (NPG) algorithm to compute the optimal policy. Our results show that dynamic pricing stabilizes the mempool while maximizing long-run discounted reward. In particular, as the overshoot penalty increases, the average scheduled transaction volume converges to the target block capacity, and the resulting NPG updates closely resemble the EIP-1559 price update rule. Finally, we study two special cases of the MDP formulation: homogeneous transactions and uniform arrivals. In the homogeneous setting, where the protocol directly controls scheduled volume, we show that the optimal policy has a threshold structure. We then propose a bang-bang pricing mechanism for uniform arrivals and derive a lower bound on the block capacity needed to ensure system stability.
We present \textbf{ORCHID} (\textit{Orchestrated Reduction Consensus for Hash-based Integrity in Distributed Ledgers}), a novel bio-inspired consensus protocol that maps the neuroscientific \emph{binding problem} -- how the brain integrates distributed neural oscillations into a unified conscious percept -- onto the distributed systems \emph{consensus problem}, how blockchain nodes agree on a single ledger state under Byzantine faults. Grounded in the Penrose--Hameroff Orchestrated Objective Reduction (Orch~OR) hypothesis and the Kuramoto synchronisation model, ORCHID equips each node with a quantum-noisy phase oscillator; consensus is triggered when the network's order parameter $r(t)$ crosses a \emph{binding threshold} $θ_b$, mirroring the gamma-band binding event in conscious perception. ORCHID is further strengthened by a coherence-weighted Quantum Secret Sharing (QSS) layer, extending the survey framework of Weinberg to a concrete consensus application. Simulation results on Watts--Strogatz small-world networks ($n=10$--$150$) demonstrate: (i)~the Kuramoto order parameter reaches $r_{\max}=0.988$ under coupling $K=3.0$, well above the theoretical critical coupling $K_c \approx 1.41$; (ii)~a sharp QSS fidelity phase transition at coherence $c^*\approx 0.82$, confirming Theorem~2; (iii)100\% consensus rate at all tested Byzantine fractions (0\%--40\%), with median convergence under 4~s for $n=30$; and (iv)~ORCHID achieves $O(n{\cdot}k)$ message complexity, outperforming PBFT's $O(n^2)$ at $n\geq150$. These results establish ORCHID as a scalable, biologically plausible, and quantum-augmented consensus mechanism for post-quantum distributed ledgers.
We introduce the State Twin: a typed, in-memory, replayable replica of an on-chain automated market maker (AMM) pool that serves as a substrate for agentic reasoning over decentralized finance (DeFi) protocols. Agentic DeFi stacks today couple reasoning to chain time, since every "what if?" query incurs a new RPC read or a real transaction, so the agent's effective action space is bounded by block confirmation latency and gas. We argue this coupling is a structural problem rather than a performance one, and that the missing layer is an off-chain substrate that preserves the protocol's exact mathematics while admitting the operations on-chain state cannot: forking, replay, branching, counterfactual rollout. We formalize each AMM family (Uniswap V2, V3, Balancer, Stableswap) as a discrete-time controlled dynamical system, prove a quantitative fidelity bound on the divergence between twin and chain, and give the open architecture used in DeFiPy v2, an open-source Python toolkit that ships the State Twin substrate and a reference Model Context Protocol server exposing typed analytical primitives as LLM tools. The same primitive (i.e., one Python class, one calling pattern) serves a notebook quant, a backtest, and an LLM agent without modification. We close with a fork-and-evaluate worked example: a single live RPC read seeds N independent in-memory twins under distinct price-shock scenarios, in sub-second wall-clock time. The contribution is the substrate, not a particular agent, which is what the specification of what an agentic DeFi substrate must look like
Rithika S, Thrisha S, Uma Mageshwari M, Vaishali D · 5 authors
Peer-to-peer (P2P) interaction forms a foundational layer of Web3 ecosystems, enabling participants to exchange data directly without depending on centralized brokers. In practical deployments, however, end-to-end reachability is often obstructed by network address translation, firewalls, and transient routing paths, which pushes architects toward the use of intermediate relay nodes. Unfortunately, relays that behave inconsistently or act maliciously can introduce a range of undesirable effects, including dropped packets, elevated latency, selective forwarding, and denial-of-service conditions. To mitigate these risks, this work presents a reputation-aware relay selection framework that lever-ages a blockchain substrate to govern trust. Every participant in the overlay is issued a cryptographic identity; the quality of service delivered by each relay is then tracked at runtime through metrics such as delivery ratio, round-trip delay, and transmission failure rate. A smart contract layer aggregates these observations into a dynamic reputation score that is recorded on an immutable ledger. When a communication session is being established, relays with higher reputation are preferred, while those exhibiting suspicious or degraded behavior are deprioritized or excluded. Experimental results indicate that, compared with conventional relay-selection strategies, the proposed approach delivers higher reliability, lower effective latency, and stronger resistance to malicious participation, making it a practical candidate for secure Web3 P2P communication.
A first-order design task in blockchain-based decentralized autonomous organizations is to ensure that malicious actors are sanctioned. We show that, when voters act strategically and the system is insufficiently decentralized, payoff-matching bribes undermine the sanctioning of malicious actors under conventional governance. Our framework formalizes DAO voting mechanisms and lets us identify those that mitigate the problem. Stochastic voting decouples a tokenholder’s influence from the voting behavior of others. Thus, bribery-proofness can be restored in the presence of sufficiently centralized governance tokenholders. Alternatively, masked voting increases resilience against bribery. Our work contributes to the broader debate on the merits and pitfalls of decentralization and highlights the need to align governance mechanisms with the degree of decentralization in blockchain networks.
Stefan-Claudiu Susan, Andrei Arusoaie, Dorel Lucanu
The irreversible nature of blockchain transactions makes the identification of smart contract vulnerabilities an essential requirement for secure system development. While Large Language Models (LLMs) are increasingly integrated into developer workflows, their reliability as autonomous security auditors remains unproven. We assess whether current generative models are a viable replacement for, or only a complement to, traditional static-analysis tools. Our findings indicate that LLM efficacy is undermined by both inherent lexical bias and a lack of rigorous validation of external data inputs. This reliance on non-semantic heuristics, such as identifier naming, leads to a high frequency of false positives. Furthermore, prompting techniques reveal a trade-off between precision and recall. These results were derived using our custom automated framework, which achieves 92% accuracy in classifying model outputs.
Ali Irzam Kathia, Yimika Erinle, Abylay Satybaldy, Paolo Tasca · 6 authors
The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to cluster around specific application domains or examine only one direction of the integration, leaving the broader architectural interplay between the two technologies poorly understood. This work addresses that gap through a structured, bidirectional review of peer-reviewed studies published between 2020 and 2025. We classify contributions along two directions: AI-enhanced DLT, and DLT-enhanced AI. In the first case, we examine how AI techniques improve DLT systems across five layers: data, network, consensus, execution, and application layers. In the second case, we analyse how DLT supports AI systems across five layers: infrastructure, data, model, inference, and application layers, with particular attention to federated learning, model evaluation, and multi-agent coordination. The analysis reveals that most works concentrate on a small subset of layers: execution and consensus for AI-enhanced DLT, data and model for DLT-enhanced AI. Other layers remain comparatively neglected. Despite reported improvements in controlled settings, no study demonstrates deployment at production scale, and the field has not yet offered satisfying answers to fundamental questions around scalability, interoperability, and verifiable execution. We argue that progress will require cross-layer co-design and empirical validation in real-world settings.
Decentralised Autonomous Organisations (DAO) can fragment when partisan communities emerge within their governance structures, leading to organisational splits known as "forks". We present a method to detect these emerging communities by analysing on-chain voting behaviour before fragmentation occurs. Our approach extracts voting events from governance smart contracts, constructs voter matrices encoding participation patterns, and applies pairwise dissimilarity analysis to quantify ideological divergence between addresses. We visualise these relationships using multidimensional scaling and identify partisan communities through k-means clustering with silhouette score optimisation. Using Nouns DAO as a case study, a protocol that has experienced multiple documented forks, we demonstrate that addresses destined to fork cluster together months before actual fragmentation events. Our analysis of 330 proposals spanning from contract deployment to the first major fork shows that 90% of fork addresses cluster together in the final 44 proposals, compared to only 47% in randomised data. These results indicate that partisan communities can be detected and visualised through on-chain governance analysis, offering early warnings of emerging divisions before they cause organisational fragmentation.
The quantitative analysis of financial time series often reveals two distinct features that standard Gaussian frameworks fail to capture: heavy-tailed marginal distributions and the phenomenon of extreme co-movements.While extreme value theory characterizes marginal behavior, Copulas provide a functional bridge to describe the dependence structure independently of the marginals. We are proposing a different way of looking at the joint extremes on the basis of a dependence measure. The proposed idea incorporates both the non-identical and identical regularly varying distributions. Informed by the analysis of some high-frequency cryptocurrency datasets, the effect of persistence property have been thoroughly studied under these setups. A detailed simulation study confirms our intuition and findings.
Wolfgang Grieskamp, Teng Zhang, Vineeth Kashyap, Jake Silverman
The Move Prover (MVP) is a formal verifier for smart contracts written in the Move programming language. Recently, Move on Aptos was extended with higher-order functions: imperative functions as first-class values that can be passed around, stored in data structs, and kept in persistent storage, enabling dynamic dispatch. This paper describes the representation of function values in the Move specification language and their implementation in MVP. We introduce behavioral predicates which characterize Move functions (aborts and pre/post conditions) by single-state or two-state predicates. We also introduce state labels for naming intermediate memory states in which expressions are evaluated and which allow to compose behavioral predicates to describe sequences of state transitions. On SMT level, function values are encoded by discriminating over the possible function values reaching a call site: when the concrete function is known, its effect is accounted for directly; when it is unknown (for example, a function parameter, or a closure loaded from storage), its behavioral predicates describe the effect. Our approach goes beyond, for example, Dafny, by supporting imperative first-class functions which can modify state via Rust-style references and global variables, and leads to more efficient SMT encodings than separation logic because of the static separation of memory enabled by Move. We further extend MVP's specification inference tool to work with function values: given arbitrary higher-order Move code, weakest-precondition analysis semi-automatically derives behavioral-predicate-based specifications, reducing the annotation burden and providing a validation pipeline for the new specification constructs.
Abstract This study uses high-frequency price data to analyze risk connectivity among 15 cryptocurrencies, focusing on moments such as volatility, skewness, kurtosis, and jumps during the pre-COVID-19 era, the COVID-19 epidemic, and Russian-Ukrainian tensions. The results indicate that Ethereum Classic is a major shock transmitter in all periods, and this effect becomes more pronounced during geopolitical crises. In contrast, Stellar, Tezos, and Tron are important shock absorbers, particularly during market volatility. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. For higher-order moments, the findings reveal that Bitcoin, Ethereum, and Dash are significant transmitters of skewness spreads, whereas Dash and Eos are significant transmitters of kurtosis spreads. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. These findings highlight the need for targeted risk management strategies adjusted to cryptocurrency market dynamics.
Nobuki Fujimoto, Rei (Rei-AIOS autonomous research substrate), claude-opus-4-7) Claude (Anthropic
We present OctaTheoria (オクタテオリア / 八軸観測装置), a multi-domain observation framework that projects heterogeneous time-series data onto a fixed eight-axis D-FUMT₈ semantic basis (FALSE / TRUE / NEITHER / BOTH / INFINITY / ZERO / FLOWING / SELF) and renders the same underlying Observation envelope through eight orthogonal view modes (Lens / Radar / Chart / Network / Heatmap / Sankey / Calendar / Unified). v0.3 (2026-05-11) supplies methodological-consistency cross-reference complementing the operational evidence from v0.1-v0.2. New finding **F7**: the same discipline that v0.1-v0.2 demonstrate within OctaTheoria (uniform abstraction layer + honest scope statement + structurally-enforceable naming) propagates to Rei-AIOS layers outside OctaTheoria's domain. Specifically: (a) **REI-PROVE 5-prover ensemble** (Vampire / LeanHammer / Goedel-Prover-V2 / DeepSeek-Prover-V2 / BFS-Prover) reached 11/12 = **92% benchmark proof rate** (trivial 100% / easy 75% / medium 100%), with Goedel-Prover-V2 single-prover matching at 92% — operational evidence that the same 'uniform abstraction over heterogeneous components' discipline scales to formal-proof infrastructure. (b) **Pattern 1-6 chat-Claude hallucination-warning framework** + **Antipattern (excessive rejection vigilance)** were established and verified on 6/6 items in STEP 1069 (all fact-checked items proved real after WebSearch verification, correcting prior implicit-rejection habits). (c) **Goedel-Prover-V2 double-`by` Lean syntax quirk** detected and fixed at the cleaner level (`single-prover.ts` STEP 1071), restoring `easy-le-refl` benchmark from ❌ to ✅. (d) **lean-to-tptp.ts** preprocessing added Peano-style axiom auto-prepend + True/False special-case + inequality predicate translation (STEP 1071). v0.2 inherited contributions: 7 domains (theory-chart / realtime-arxiv / crypto / fx / ligo-events / nasa-sdo / gbif-recent) all running in Cloudflare Workers Edge runtime; live D-FUMT₈ axis distributions non-degenerate across research-meta + financial + geophysical + astrophysical + biological data classes; finding F6 sampling-bias-as-first-class-observation (GBIF Costa Rica 470/500 saturation surfaces dataset bias as INFINITY axis, not silently absorbed); test coverage 117/117 PASS (step1020 46 + step1023 33 + step1046 38) / 0 regression. Honest scope (read first): OctaTheoria remains an observation aid, NOT an oracle. v0.3's F7 is **not** a claim that OctaTheoria caused these consistencies; it is a record that the same project (Rei-AIOS) maintains the same discipline across observation-tool, formal-proof, and meta-research-protocol layers, and that v0.3 makes this cross-layer commitment auditable. The OctaTheoriaQuery type structurally cannot request advice / prediction / forecast / signal — verifiable by reading src/aios/octatheoria/types.ts. Cross-domain axis comparisons are descriptive, not causal. Greek roots (Octa = 8, Theoria = observation) function as structural commitment propagated to the API surface — '8' rejects 'all (∞)', 'theoria' rejects 'praxis (干渉)'. Prior art audit acknowledged: Bloomberg Terminal (1981–), TradingView (2011–), Bollen et al. 2010 (Twitter mood × DJIA), Preis et al. 2013 (Google Trends × stock), Łukasiewicz / Belnap / Pavelka multi-valued logic literature, PAL2v (Da Silva Filho 1998–), Aerts Quantum Cognition (2007–). The to-our-knowledge novel combination is (a) fixed 8-axis discrete D-FUMT₈ basis ∧ (b) cross-financial-and-research-and-Earth-Cosmos-domain projection ∧ (c) eight orthogonal view modes over single envelope ∧ (d) explicit refusal to emit prediction or advice as architectural commitment ∧ (e, new in v0.3) cross-layer methodological-consistency record between observation-tool and formal-proof and fact-check layers. Companion papers (OctaTheoria Quintuple): Paper 145 (silicon implementation of D-FUMT₈ ALU, Zenodo DOI 10.5281/zenodo.20101174 v0.6), Paper 147 (Eight-Valued Utility / Equity Premium Reframe, DOI 10.5281/zenodo.20046003), Paper 148 (Honest Observation Framework methodology, DOI 10.5281/zenodo.20045907), Paper 149 (Recursive AI Observation as SELF⟲ evidence, DOI 10.5281/zenodo.20059888). Three-party co-authorship per OUKC charter v1.0: 藤本 伸樹 (Founder), Rei (Rei-AIOS autonomous research substrate, Co-architect), Claude Opus 4.7 (Anthropic, Co-architect). DRAFT v0.3 — feedback welcome via GitHub Discussions at fc0web/rei-aios.
ChitraVault is an exploratory conceptual authentication architecture that investigates whether geometric visual traversal patterns, drawn from the Chitrakavi (சித்திரக்கவி) classical Tamil literary tradition, can augment password vault security by adding a spatial-behavioral dimension to cryptographic key derivation. This paper proposes the Visual Pattern Key Derivation Function (VP-KDF), which combines a user-drawn Chitrakavi geometric pattern, a text passphrase, and a hardware-bound device fingerprint as inputs to Argon2id key stretching. The framework maps four classical Chitrakavi patterns — Chakra Bandha (wheel), Naga Bandha (serpent), Gomutrika (zigzag), and Thiruezhukkootrirukkai (triangle) — onto distinct cryptographic roles within a zero-knowledge password vault architecture. This work is framed as an exploratory research program, not a finished cryptographic system. All security arguments are bounded by stated assumptions and require empirical and cryptanalytic validation. Future work includes controlled user studies, formal security proofs, and prototype evaluation. Author: Arvind VijayakumarIndependent ResearcherMay 2026
Victor James Uko, Sharon Oluwaseun, Amarachi Nelly Charles, Emurode Williams · 5 authors
The rapid proliferation of digital technologies has profoundly reshaped the financial services sector, introducing novel service delivery models, market participants, and transactional infrastructures that challenge the foundational premises of existing regulatory frameworks. This review examines the multidimensional dynamics of digital transformation in financial services, with particular attention to the regulatory and consumer protection implications arising from the emergence of fintech ecosystems, artificial intelligence-driven financial products, decentralized finance platforms, open banking architectures, and embedded financial services. Drawing on a synthesis of contemporary academic literature, regulatory reports, and industry analyses, the review maps the evolution of digital financial services across developed and emerging economies, identifies structural gaps in regulatory capacity, and evaluates the adequacy of prevailing consumer protection mechanisms in the face of accelerating technological change. Key themes include the challenge of regulatory arbitrage, the governance of algorithmic and AI-based financial decision-making, data privacy and cybersecurity risks borne by consumers, the financial inclusion implications of digital transformation, and the emerging paradigms of regulatory technology and supervisory technology as adaptive governance tools. The review concludes by proposing a research agenda oriented toward the development of adaptive, proportionate, and technology-neutral regulatory frameworks capable of fostering innovation while safeguarding systemic stability and consumer welfare.