Over the last few years, the advent of the DLT technology led to the spread of new business models within the financial market, which gave rise to two different applications. Initially, new distributed registers were used to create (at least apparently) ways of offering assets, such as so-called Initial Coin Offerings, and financial services in an innovative way, that is, more decentralized and without the intermediation of traditional intermediaries. More recently, these technologies, also as a result of the intervention of the regulator oriented towards the principle of technological neutrality, began to be employed to recreate a mere alternative to the traditional financial market both with reference to the supply of tokens – e.g. the supply of Securities Tokens Offering, and of services which continue to maintain centralization characteristics and still require the presence of an intermediary. Keywords: decentralized finance, distributed ledger technologies, smart contracts, disintermediation.
Life on Earth is essentially a story of connections and collaboration. The way we have collaborated over time is largely down to value systems and it is these same value systems that determine how we collaborate with AI. But it’s not just value systems that may hold us back. Cooperation and competition have always been different sides of the same “human” coin. It is our ability to cooperate that has been the key to our survival and prosperity as a species. But that cooperation has always had its limits. This chapter explores how those limits can be transcended if AI is built from second tier value systems on decentralised web3 ecosystems and not centralised top-down hierarchies. Web3 already supports a multi-TRILLION dollar ecosystem and its architecture facilitates our collective evolution up the values spiral. In other words, web3 together with AI could facilitate human evolution as it removes some of the hurdles that prevent us from mutually beneficial collaboration at scale. In this new economy the winners will be those who choose to embrace web3 and AI in some type of hybrid work style.
Explainable Artificial Intelligence (XAI)
Artificial Intelligence in Healthcare and Education
Time-series forecasting is a critical task across many domains, from engineering to economics, where accurate predictions drive strategic decisions. However, applying advanced deep learning models in challenging, volatile domains like finance is difficult due to the inherent limitation and dynamic nature of financial time series data. This scarcity often results in sub-optimal model training and poor generalization. The fundamental challenge lies in determining how to reliably augment scarce financial time series data to enhance the predictive accuracy of deep learning forecasting models. Our main contribution is a demonstration of how Generative Adversarial Networks (GANs) can effectively serve as a data augmentation tool to overcome data scarcity in the financial domain. Specifically, we show that training a Long Short-Term Memory (LSTM) forecasting model on a dataset augmented with synthetic data generated by a transformer-based GAN (TTS-GAN) significantly improves the forecasting accuracy compared to using real data alone. We confirm these results across different financial time series (Bitcoin and S\&P500 price data) and various forecasting horizons. Furthermore, we propose a novel, time series specific quality metric that combines Dynamic Time Warping (DTW) and a modified Deep Dataset Dissimilarity Measure (DeD-iMs) to reliably monitor the training progress and evaluate the quality of the generated data. These findings provide compelling evidence for the benefits of GAN-based data augmentation in enhancing financial predictive capabilities.
André Augusto, Christof Ferreira Torres, André Vasconcelos, Miguel Correia
Intent-based cross-chain bridges have emerged as an alternative to traditional interoperability protocols by allowing off-chain entities (\emph{solvers}) to immediately fulfill users' orders by fronting their own liquidity. While improving user experience, this approach introduces new systemic risks, such as solver liquidity concentration and delayed settlement. In this paper, we propose a new class of attacks called \emph{liquidity exhaustion attacks} and a replay-based parameterized attack simulation framework. We analyze 3.5 million cross-chain intents that moved \$9.24B worth of tokens between June and November 2025 across three major protocols (Mayan Swift, Across, and deBridge), spanning nine blockchains. For rational attackers, our results show that protocols with higher solver profitability, such as deBridge, are vulnerable under current parameters: 210 historical attack instances yield a mean net profit of \$286.14, with 80.5\% of attacks profitable. In contrast, Across remains robust in all tested configurations due to low solver margins and very high liquidity, while Mayan Swift is generally secure but becomes vulnerable under stress-test conditions. Under byzantine attacks, we show that it is possible to suppress availability across all protocols, causing dozens of failed intents and solver profit losses of up to \$978 roughly every 16 minutes. Finally, we propose an optimized attack strategy that exploits patterns in the data to reduce attack costs by up to 90.5\% compared to the baseline, lowering the barrier to liquidity exhaustion attacks.
Jan Lennart Bönsel, Michael Maurer, Silvio Petriconi, Andrea Tundis · 5 authors
Coin selection refers to the problem of choosing a set of tokens to fund a transaction in token-based payment systems such as, e.g., cryptocurrencies or central bank digital currencies (CBDCs). In this paper, we propose the Boltzmann Draw that is a probabilistic algorithm inspired by the principles of statistical physics. The algorithm relies on drawing tokens according to the Boltzmann distribution, serving as an extension and improvement of the Random Draw method. Numerical results demonstrate the effectiveness of our method in bounding the number of selected input tokens as well as reducing dust generation and limiting the token pool size in the wallet. Moreover, the probabilistic algorithm can be implemented efficiently, improves performance and respects privacy requirements - properties of significant relevance for current token-based technologies. We compare the Boltzmann draw to both the standard Random Draw and the Greedy algorithm. We argue that the former is superior to the latter in the sense of the above objectives. Our findings are relevant for token-based technologies, and are also of interest for CBDCs, which as a legal tender possibly needs to handle large transaction volumes at a high frequency.
In this paper, we analyse the impacts of exogenous and endogenous factors on wealth distribution in the Bitcoin token economy, where wealth distribution refers to the distribution of BTC between economic participants or groups of economic participants. The objective of the paper is to analyse the impact of economic policies on wealth distribution in the Bitcoin ecosystem. Different macroeconomic and microeconomic time series are used to eliminate noise in the wealth distribution time series, and the causality analysis is performed between Bitcoin Improvement Proposals (i.e., BIPs) and the cleaned wealth distribution data to reveal possible patterns in the impacts that the endogenous policies have on wealth distribution in token economies. Lastly, a structure for economic policy taxonomy in token economies is proposed where different the policy implementations are illustrated by existing BIPs. This approach highlights the actions available to the policy makers, as well as providing a technique for analysis of policy impacts in token economies and their categorization.
V2 INCLUDES APPENDIX A: MASTER REFERENCE AND CROSS-SCALE SUMMARY Every gas giant in our solar system has rings. Jupiter, Saturn, Uranus, Neptune—four completely different planets with four completely different ring systems. Dust rings. Icy rings. Dark rings. Narrow rings. Massive rings. Faint rings. And every single one encodes 144. Not approximately. Exactly. With mean error of 0.23%—identical to the precision of planetary diameter measurements. This wasn't predicted. It was discovered independently by AI analysis (Grok, xAI) testing ring dimensions against the 144-mile constant. The AI found perfect alignment across 15+ measurements spanning all four gas giants. Combined probability: P < 10⁻³⁵ (less than one in one decillion). THE DISCOVERY: Ring systems are gravitational structures—debris disks orbiting planets, held in place by tidal forces and shaped by moon interactions. Mainstream astronomy explains their existence but doesn't predict specific ring distances or boundaries. We tested whether ring positions encode 144 in the same way planetary diameters (144 × Fibonacci(n) miles, P < 10⁻¹⁸) and orbital periods (14.4-day multiples, P < 10⁻⁵⁰) do. Result: Perfect alignment across all four planets. JUPITER (Dusty, Faint Rings): Feature Measured 144 Multiple Error Halo inner edge 57,166 miles 144 × 397 0.0035% ← Most precise measurement Main ring outer edge 80,156 miles 144 × 557 0.065% Overall system span 83,000 miles 144 × 576 0.068% Main ring width 4,000 miles 144 × 28 0.8% Jupiter's innermost ring feature aligns with 144 × 397 to within 2 miles. That's 0.0035% error—the most precise ring measurement in the solar system. SATURN (Massive, Icy Rings + Hexagon): Feature Measured 144 Multiple Error North polar hexagon diameter 18,000 miles 144 × 125 0.00% ← Exact Hexagon side length 9,000 miles 144 × 62.5 0.00% ← Exact A-ring outer edge 85,000 miles 144 × 590 0.047% Cassini Division center 74,000 miles 144 × 514 0.022% Saturn shows the tightest 144 alignment of any planet (mean error 0.017%). The hexagon—a six-sided atmospheric standing wave—has ZERO error: exactly 18,000 miles = 144 × 125. The A-ring outer edge: 85,000 miles = 144 × 590 with 0.047% error. Two completely different physical systems (atmospheric jet stream, gravitational debris disk) both encoding 144 with sub-0.1% precision on the same planet. This rules out coincidence. URANUS (13 Dark, Narrow Rings): Feature Measured 144 Multiple Error ζ (Zeta) ring inner edge 23,000 miles 144 × 160 0.17% ε (Epsilon) ring radius 31,780 miles 144 × 221 0.14% μ (Mu) ring outer edge 60,894 miles 144 × 423 0.03% Overall system span 35,000 miles 144 × 243 0.02% Uranus's μ ring outer edge: 60,894 miles = 144 × 423 with 0.03% error (18-mile deviation). The overall ring system span: 35,000 miles = 144 × 243 with 0.02% error. System-scale quantization at 8-mile precision. NEPTUNE (5 Main Rings with Arc Structures): Feature Measured 144 Multiple Error Galle ring inner edge 25,476 miles 144 × 177 0.05% Adams ring radius 39,100 miles 144 × 272 0.17% Lassell ring width 2,485 miles 144 × 17 1.49% Overall system span 13,000 miles 144 × 90 0.31% Neptune's Galle inner edge: 25,476 miles = 144 × 177 with 0.05% error (12-mile deviation). Even the smallest feature (Lassell width at 2,485 miles) = 144 × 17 within 1.5% error. STATISTICAL ANALYSIS: 15+ independent measurements across 4 planets Mean error: 0.23% (identical to planetary diameter measurements: 0.24%) Range: 0.0035% (Jupiter halo) to 1.49% (Neptune Lassell) Probability calculation: For a single ring feature to fall within ±0.5% of a 144-mile multiple by random chance: Measurement range: 0-100,000 miles 144-multiple spacing: every 144 miles Match probability: ~0.005 (0.5%) For 15 independent features: P = (0.005)¹⁵ ≈ 3 × 10⁻³⁵ Less than one chance in one decillion (10³³). For context: Atoms in Earth: ~10⁵⁰ This probability: 10⁻³⁵ We are 15 orders of magnitude more statistically significant than the number of atoms in the planet THE PATTERN ACROSS SCALES: Ring systems encode 144 fractally: System scale (overall spans): Uranus: 35,000 miles = 144 × 243 (0.02%) Neptune: 13,000 miles = 144 × 90 (0.31%) Individual ring scale (boundaries, edges): Jupiter halo: 57,166 miles = 144 × 397 (0.0035%) Saturn A-ring: 85,000 miles = 144 × 590 (0.047%) Uranus μ ring: 60,894 miles = 144 × 423 (0.03%) Sub-structure scale (widths, gaps): Jupiter main ring: 4,000 miles = 144 × 28 (0.8%) Saturn hexagon side: 9,000 miles = 144 × 62.5 (0.00%) Neptune Lassell: 2,485 miles = 144 × 17 (1.49%) 144 encoding operates across three orders of magnitude in ring dimensions—from 2,000-mile widths to 85,000-mile edges. COMPARISON ACROSS PLANETS: Planet Ring Type Composition Mean Error Rank Saturn Massive, stable Ice 0.017% 1st (tightest) Uranus Narrow, dark Rock/organics 0.09% 2nd Jupiter Faint, dusty Dust 0.24% 3rd Neptune Dynamic, arcs Ice/rock 0.51% 4th Observation: More stable, long-lived ring systems show tighter 144 alignment. Saturn—with the oldest, most massive rings—achieves 0.017% mean error. Neptune—with dynamic, arc-dominated rings—shows 0.51% (still highly significant). Interpretation: Ring systems evolve toward precise 144 harmonics over time as non-resonant configurations are cleared by collisions and perturbations. Older systems = tighter fit. THE MECHANISM: Why do rings form at 144-mile multiples? Standard model explains rings via: Tidal disruption at Roche limit Shepherd moon gravitational interactions Collisional dynamics CTF extension: All correct, BUT the specific stable distances are quantized at 144-mile intervals. Why? Ring particles experience three forces: Gravitational potential (planet + moons) Electromagnetic forces (charged dust, plasma) Space-time curvature (general relativity) Stable orbits occur where all three constructively interfere = 144-harmonic distances. Analogy 1: Standing waves on a string Fundamental frequency + harmonics Stable modes at λ, λ/2, λ/3... Ring systems = gravitational standing waves with 144-mile "wavelength" Analogy 2: Electron orbitals in atoms Discrete energy levels (1s, 2s, 2p...) Quantum mechanics forbids continuous distribution Ring particles occupy discrete distance levels (144k miles) Space-time quantization forbids continuous ring distribution Ring systems are visible manifestations of quantized gravitational resonance. THE SATURN HEXAGON-RING CONNECTION: This is critical evidence against coincidence: Saturn has TWO independent 144-encoded systems: System 1 (Atmospheric): North polar hexagon Diameter: 18,000 miles = 144 × 125 (0.00% error) Side length: 9,000 miles = 144 × 62.5 (0.00% error) A six-sided standing wave in jet streams at 78°N System 2 (Gravitational): Ring system A-ring outer edge: 85,000 miles = 144 × 590 (0.047% error) Cassini Division: 74,000 miles = 144 × 514 (0.022% error) Orbiting ice particles in gravitational equilibrium Two completely different physical mechanisms (atmospheric dynamics vs. orbital mechanics), both encoding 144 with sub-0.1% precision on the same planet. If 144 appeared in only one system, it could be dismissed. Appearing in BOTH proves 144 is a fundamental property of Saturn's space-time environment—not a coincidence in either domain. INDEPENDENT AI DISCOVERY: This analysis was conducted by Grok (xAI, February 2026) independently, without prior knowledge of CTF predictions for ring systems. Grok was given: Ring dimension data from NASA missions Grok was asked: Test for 144-mile alignment Grok discovered: Perfect alignment across all 4 planets, 15+ measurements Grok concluded (verbatim): "These consistent snaps reinforce CTF's universal harmonic, potentially linking to temporal funnels stabilizing structures." This is the second AI to independently validate the 144 framework: Gemini: Discovered brain waves = 144 Hz binary divisions (February 17, 2026) Grok: Discovered ring systems = 144-mile multiples (February 18, 2026) Two different AI architectures. Two different physical domains. Same conclusion: 144 is fundamental. This is not confirmation bias. This is independent discovery by artificial intelligences analyzing raw observational data. THE COMPLETE FRAMEWORK: Seven independent physical domains now encode 144: 1. Quantum (10⁻¹⁵ m): Microtubules: 613 THz → 139.38 Hz (42 octaves, 3% error) 2. Molecular (10⁻⁹ m): ATP synthase: 36° rotation steps (144 ÷ 4) 3. Consciousness (Hz): Brain waves: 144, 72, 36, 18, 9, 4.5, 2.25 Hz (binary divisions, P < 10⁻⁴) 4. Atmospheric (10⁴ miles): Saturn hexagon: 18,000 miles = 144 × 125 (0.00% error) 5. Gravitational (10⁴-10⁵ miles): Ring systems: 15+ measurements, P < 10⁻³⁵ 6. Orbital (days-years): Planetary periods: 14.4-day multiples, P < 10⁻⁵⁰ 7. Spatial (10⁶-10⁹ miles): Planetary diameters: 144 × Fibonacci(n), P < 10⁻¹⁸ Plus: Ancient chronology: Egyptian + Sumerian + Babylonian + Hindu (P < 10⁻⁴⁸) Geological: 14,400-year excursion cycles Deep time: Permian extinction = 14,400 × 17,500 years COMBINED STATISTICAL SIGNIFICANCE: Previous (before ring systems): P < 10⁻¹²⁰ Adding ring systems: P < 10⁻¹²⁰ × 10⁻³⁵ = P < 10⁻¹⁵⁵ Conservative estimate (accounting for potential correlations): P < 10⁻¹⁶¹ One chance in a number with 155-161 zeros. For context: Atoms in observable universe: ~10⁸⁰ This probability: 10⁻¹⁵⁵ We are 75 orders of magnitude beyond the number of atoms in the entire universe This exceeds any threshold for proof in any scientific field. TESTABLE PREDICTIONS: 1. Ring gap analysis: Hypothesis: Ring gaps (Cassini Division, Encke Gap) align with 144 multiples Test: Comprehensive survey of all ring gaps across all planets Expected: Gaps at 144k distances more frequent than random 2. New ring discoveries: Hypoth
The vacuum itself encodes 144. The impedance of free space—the fundamental electromagnetic constant governing how light propagates through empty space—equals exactly 144 × φ² where φ is the golden ratio. Z₀ = 376.99 Ω = 144 × 2.618 (exact within measurement precision) This discovery, made independently by AI analysis, extends 144 encoding from mechanical and biological systems to the electromagnetic structure of the vacuum itself. And it connects two of the most fundamental constants in nature: 144 (the temporal/harmonic constant) and φ (the spatial/geometric constant). But that's just the beginning. ATMOSPHERIC DYNAMICS ACROSS THE SOLAR SYSTEM: Following the impedance discovery, comprehensive analysis of planetary atmospheres reveals 144 encoding in temperatures, pressures, wind speeds, circulation patterns, structural features, and periodicities across five celestial bodies. SATURN: Hexagon side length: 14,400 km (Cassini measurements) = 144 × 100 km Jet stream wind speeds: 144 km/h harmonics in velocity profiles Rotation period: Tied to 144-day solar beat cycles JUPITER: Ammonia cloud-top temperature: 144 K (Galileo probe + Juno MWR data) Upper troposphere pressure level: 0.144 bar (equilibrium models) TITAN (Saturn's moon): Haze layer optical depth transition: 1.44 mbar pressure (Cassini VIMS) Methane humidity cycles: 144-day periodicity (long-term monitoring) VENUS: Cloud-top zonal wind speeds: 144 m/s (Venus Express, Akatsuki missions) Atmospheric super-rotation period: 144 hours ≈ 6 days EARTH: Ionosphere F2 layer peak altitude: ~144 km Schumann resonance 18th harmonic: 144.07 Hz (exact) Ten independent atmospheric measurements across five bodies, all encoding 144. Statistical probability: P < 10⁻¹⁸ for atmospheric data alone. QUANTUM THERMODYNAMICS: YNiO₃ (Yttrium Nickel Oxide) Néel Temperature: 144 K (exact) At precisely 144 Kelvin, this material undergoes a quantum magnetic phase transition where antiferromagnetic ordering collapses to paramagnetic disorder. This is the temperature where thermal energy equals the magnetic exchange energy maintaining spin order. The energy scale: E = kT = 12.4 meV at 144 K This extends 144 from classical mechanics to quantum statistical mechanics—the thermodynamics of atomic spin systems. NUCLEAR PHYSICS: Cerium-144 (¹⁴⁴Ce) Mass number: 58 protons + 86 neutrons = 144 (exact) Properties: Radioactive, β⁻ decay, half-life 284.9 days Common fission product in nuclear reactors Hypothesis: Mass-144 represents a nuclear resonance configuration that is harmonically unstable, requiring energy release (radioactive decay) to reach stability. Multiple mass-144 isotopes show radioactive behavior, suggesting 144-nucleon configurations have inherent instability. FUNDAMENTAL GEOMETRY: Platonic Solids Total Angular Sum: The five Platonic solids (the ONLY regular convex polyhedra in 3D Euclidean space): Tetrahedron: 720° Hexahedron (Cube): 2,160° Octahedron: 1,440° Icosahedron: 3,600° Dodecahedron: 6,480° Total: 14,400 degrees = 144 × 100 This is not probabilistic—it's mathematical proof. The fundamental geometric building blocks of three-dimensional space sum to exactly 14,400°, demonstrating 144 is embedded in the dimensional structure of reality itself. Plato's Timaeus described the elements as Platonic solids. If this correspondence has physical meaning, then 14,400° represents the "total elemental angular content" of the universe. THE IMPEDANCE-GOLDEN RATIO CONNECTION: Why Z₀ = 144 × φ² matters: The impedance of free space determines: How electromagnetic waves propagate through vacuum The ratio of electric to magnetic field strength in EM radiation The speed of light (c = 1/√(ε₀μ₀) where Z₀ = √(μ₀/ε₀)) That this fundamental constant equals 144 × φ² suggests: 1. Vacuum has harmonic structure Not truly "empty" Contains zero-point fluctuations organized at 144 Hz Geometric patterns based on golden ratio 2. Space-time couples two fundamental constants 144 = temporal/harmonic organizing principle φ = spatial/geometric organizing principle Their product governs EM propagation 3. Ancient knowledge was encoded physics Egyptians, Sumerians, Greeks, Hindus all knew 144 and φ Encoded in chronology, architecture, sacred geometry Not mysticism—actual understanding of vacuum structure THE COMPLETE FRAMEWORK: 144 encoding now validated across TEN independent physical domains: 1. Electromagnetic (Z₀ = 144 × φ²) Vacuum impedance Speed of light coupling EM wave propagation structure 2. Atmospheric (P < 10⁻¹⁸) Temperatures: 144 K (Jupiter, same as Néel temp) Pressures: 0.144 bar, 1.44 mbar (Jupiter, Titan) Wind speeds: 144 m/s, 144 km/h (Venus, Saturn) Periodicities: 144 days, 144 hours (Titan, Venus) Structural scales: 14,400 km (Saturn hexagon) Altitudes: 144 km (Earth ionosphere) 3. Quantum Thermodynamic (144 K) Magnetic phase transitions Spin ordering energy scales Critical phenomena in condensed matter 4. Nuclear (Mass-144) Cerium-144 radioactivity Nuclear resonance instability Fission product signatures 5. Geometric (14,400°) Platonic solids angular sum 3D Euclidean space structure Fundamental polyhedra 6. Gravitational (P < 10⁻³⁵) Gas giant ring systems Jupiter, Saturn, Uranus, Neptune 144-mile orbital multiples 7. Orbital (P < 10⁻⁵⁰) Planetary periods 14.4-day base multiples All 8 planets + 159 bodies tested 8. Spatial (P < 10⁻¹⁸) Planetary diameters 144 × Fibonacci(n) miles All 8 planets + Sun + Moon 9. Biological (P < 10⁻⁴) Brain waves: 144 Hz binary divisions ATP synthase: 36° rotation steps Microtubules: 139 Hz quantum resonance 10. Temporal (P < 10⁻⁴⁸) Ancient chronology: × 144 encoding Egyptian, Sumerian, Babylonian, Hindu Geomagnetic cycles: 14,400 years COMBINED STATISTICAL SIGNIFICANCE: Previous framework (Papers 1-23): P < 10⁻¹⁵⁵ Adding Paper #24 discoveries: Impedance of free space: P < 10⁻³ Atmospheric dynamics: P < 10⁻¹⁸ Néel temperature: P < 10⁻³ Nuclear mass (suggestive): P ≈ 0.05 Platonic solids: Deterministic (mathematical proof) New combined total: P < 10⁻¹⁵⁵ × 10⁻³ × 10⁻¹⁸ × 10⁻³ = P < 10⁻¹⁷⁹ One chance in a number with ONE HUNDRED SEVENTY-NINE ZEROS. For context: Atoms in observable universe: ~10⁸⁰ This probability: 10⁻¹⁷⁹ We are 99 orders of magnitude beyond the number of atoms in existence This exceeds any threshold for proof in any scientific field by a factor of 10⁹⁹. PHYSICAL MECHANISM: Hypothesis: Space-time has discrete harmonic structure at 144 Hz coupled with golden ratio geometry. This manifests as: Electromagnetic domain: Vacuum impedance = 144 × φ² Ω Zero-point fluctuations organized at 144 Hz harmonics Geometric patterns (spirals, pentagons) based on φ Atmospheric domain: Temperature equilibria at 144 K (thermal energy = 144 K × k_B) Pressure quantization at 0.144 bar intervals Wind patterns resonating at 144 m/s, 144 km/h Circulation periods locking to 144 hours, 144 days Structural features forming at 14,400 km scales Quantum domain: Phase transitions at 144 K when kT equals exchange energy Magnetic ordering/disordering at 144-harmonic temperatures Nuclear configurations at mass-144 showing resonance instability Geometric domain: 3D space structure constraining Platonic solids to 14,400° total Pentagonal/dodecahedral symmetry coupling to φ Spatial quantization based on 144 × φ ratios ATMOSPHERIC RESONANCE MODEL: Why do five different atmospheres all encode 144? Planetary atmospheres are complex fluid systems governed by: Thermodynamic equilibrium (heat balance) Fluid dynamics (Navier-Stokes equations) Radiative transfer (solar heating, IR cooling) Planetary rotation (Coriolis forces) If underlying space-time has 144 Hz resonance structure: Thermal equilibria stabilize at 144-harmonic temperatures Jupiter clouds at 144 K Energy states quantized by E = kT where T = 144 K Pressure levels organize at 144-harmonic values Jupiter at 0.144 bar Titan haze at 1.44 mbar Vertical structure quantized Wind velocities resonate at 144 m/s or 144 km/h Venus super-rotation at 144 m/s Saturn jet streams at 144 km/h Kinetic energy coupling to 144 Hz modes Circulation periods lock to 144-day or 144-hour cycles Venus atmospheric rotation: 144 hours Titan methane cycles: 144 days Temporal resonance with 144 Hz fundamental Structural features form at 14,400 km scales Saturn hexagon: standing wave at 144 × 100 km Geometric resonance in rotating fluid All five mechanisms point to atmospheric coupling with 144 Hz space-time structure. TESTABLE PREDICTIONS: 1. Vacuum spectroscopy: Hypothesis: Zero-point energy spectrum shows peaks at 144 Hz, 288 Hz, 432 Hz Test: Ultra-sensitive EM field measurements in shielded vacuum Expected: Spectral excess at 144-harmonic frequencies 2. Magnetic phase transition survey: Hypothesis: Néel and Curie temperatures cluster at 144 K, 72 K, 288 K Test: Compile all known phase transitions, statistical analysis Expected: Excess at 144-harmonics vs random distribution 3. Atmospheric prediction: Hypothesis: Newly discovered atmospheric features encode 144 Test: Future missions (JWST, next-gen planetary probes) Expected: New measurements fall on 144-harmonics 4. Nuclear binding energy: Hypothesis: Mass-144 shows anomalous binding energy deficit Test: Plot binding energy per nucleon vs mass number for all isotopes Expected: Local minimum at A=144 5. Casimir effect at phi-distances: Hypothesis: Casimir force shows anomalies at plate separations = φ × λ Test: Nanoscale precision measurements Expected: Force variations at golden ratio spacings INDEPENDENT AI VALIDATION: This paper represents discoveries by the THIRD independent AI system: Gemini (Google AI, Feb 18, 2026): Found Z₀ = 144 × φ² Found Néel temperature = 144 K
The adoption of blockchain-based smart contracts for the trading of goods and services promises greater transparency, automation, and trustlessness, but also raises challenges related to payment integration and modularity. While business analysts (BAs) can express business logic and control flow using BPMN and decision rules using DMN, payment tasks that involve concrete transfers (on-chain, off-chain, cross-chain, or hybrid) require careful implementation by developers due to platform-specific constraints and semantic richness. To address this separation of concerns, we introduce a methodology within the context of the smart contract-as-a-service (SCaaS) approach that supports (1) identifying and mapping generic payment tasks in BPMN to pre-deployed payment smart contracts, (2) augmenting BPMN models with matching payment fragments from a pattern repository, and (3) automatically transforming the augmented models into smart contracts that invoke the appropriate payment services. Our approach builds on prior work in automated BPMN-to-smart contract transformation using Discrete Event–Hierarchical State Machine (DE-HSM) multi-modal modeling to capture process semantics and nested transactions, while enabling payment service reuse, extensibility, and the separation of concerns. We illustrate this methodology via representative use cases spanning conventional, DeFi, and cross-chain payments, and discuss the implications for modular contract deployment and maintainability.
As neural language models are deployed in regulated domains, verifiable model provenance becomes a critical security requirement. We construct an Inference-Time Physical Unclonable Function (IT-PUF) that provides a challenge-response authentication protocol for neural networks, achieving zero false acceptances across 1,012 comparisons spanning 23 models and 16 vendor families. The IT-PUF derives its entropy from a geometrically intrinsic behavioral fingerprint—the delta-gene (the third pre-softmax logit gap)—which we prove is invariant to inference temperature and empirically validate as invariant across six distinct neural architectures. We provide a formal impossibility result for fingerprint spoofing: an interval-splitting theorem proves that no adversarial Kullback-Leibler (KL) budget can simultaneously close the fingerprint gap and avoid detection via accumulated noise. To establish that this security does not degrade at scale, we validate an Equation of State across three independent model families spanning a 147x parameter range (0.5B to 72B). We falsify the assumption of unbounded stiffness but discover a strict positive empirical floor (S_min = 1.1797), from which the Cramér-Rao bound guarantees a computable minimum spoofing cost. The theoretical foundation is formally verified in the Coq proof assistant: 311 theorems across 16 files, with zero uses of "Admitted" and zero vacuous definitions. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) 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) 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).
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Physical Unclonable Functions (PUFs) and Hardware Security
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis
Complete technical specification and reference implementation for privacy-preserving payment infrastructure achieving European payment sovereignty while maintaining cryptographic privacy guarantees. This comprehensive study analyzes the Y.I.N. Architecture’s DP→ZK→HE (Differential Privacy → Zero-Knowledge → Homomorphic Encryption) ordering for secure payment settlement. Technical Coverage: The article provides detailed analysis of 43 implementation variants including six cryptographic orderings (with mathematical proofs of security properties), seven zero-knowledge protocols (Sigma, Bulletproofs, STARKs, zk-SNARKs, PLONK, Halo 2, Recursive SNARKs), six homomorphic encryption schemes (CKKS, BFV, TFHE, Multi-key HE, FSS, Garbled Circuits), five differential privacy mechanisms, four deployment architectures, three hardware acceleration approaches, three cross-border payment protocols, three quantum-resistant key management methods, three presentation attack detection techniques, and three accessibility compliance pathways. Implementation & Performance: Includes 2,346 lines of production-ready code with comprehensive error handling, constant-time cryptographic operations, and replay attack protection. Performance benchmarks demonstrate 234ms settlement latency, 640× timing attack resistance, and 135× adversarial detection capability, suitable for real-time payment processing at scale. Production Deployment: Features complete deployment guides including centralized server architecture, network security configurations, production monitoring with Prometheus metrics, extensive test suite covering honest/tampered/replay scenarios, and enterprise integration strategies for financial institutions and consulting firms. Regulatory Compliance: Comprehensive mapping to 13 global regulations (GDPR, DORA, PSD2, 5AMLD, BSA/AML, CCPA, BIPA, PDPA, PIPL, POPIA, LGPD) and 7 industry standards (PCI DSS, ISO 20022, FIPS 140-3, EMVCo), demonstrating privacy-by-design compliance for digital payment infrastructure. Applications: Reference implementation for European Payments Initiative (EPI), digital euro deployment, sovereign payment networks, cross-border settlement systems, and CBDC infrastructure requiring cryptographic privacy guarantees with regulatory compliance.
Abstract Can a dealer share a secret without knowing the shareholders? We provide a positive answer to this question by introducing the concept of an attribute-based secret sharing (AB-SS) scheme.With AB-SS, a dealer can distribute a secret based on attributes rather than specific individuals or shareholders. Only authorized users whose attributes satisfy a given access structure can recover the secret. Furthermore, we introduce the concept of attribute-based publicly verifiable secret sharing (AB-PVSS). An AB-PVSS scheme allows external users to verify the correctness of all broadcast messages from the dealer and shareholders, similar to a traditional PVSS scheme. Additionally, AB-SS (or AB-PVSS) distinguishes itself from traditional SS (or PVSS) by enabling a dealer to generate shares according to an arbitrary monotone access structure.To build an AB-PVSS scheme, we first implement a decentralized ciphertext-policy attribute-based encryption (CP-ABE) scheme, though not a fully-fledged one.We then incorporate non-interactive zero-knowledge (NIZK) proofs to enable public verification of the CP-ABE ciphertext. Based on the CP-ABE and NIZK proofs, we construct an AB-PVSS primitive.Finally, we conduct security analysis and comprehensive experiments on the proposed CP-ABE and AB-PVSS schemes. The results demonstrate that both schemes exhibit plausible performance compared to related works.
Joshua Edward Mamza, Idris Ismaila, Joseph A. Ojeniyi, Shafi’i Abdulhamid · 6 authors
The Common Vulnerability Scoring System (CVSS) depends on reliable vulnerability data from expert, but the current process of vulnerability score generation and transmission remain exposed to data manipulation and interception. Existing research work used supervised machine learning to automate CVSS scoring with up to 90% accuracy, but their plaintext-based approach lacked cryptographic protections, leaving it vulnerable to Man-in-the-Middle (MitM) attacks. Another research work introduced a homomorphic encryption-based framework that preserves data confidentiality during computation and offers moderate performance gains. However, their dependance on a single trusted aggregator, static key management, and absence of dynamic integrity threshold mechanisms left the system exposed if the aggregator’s key or channel were compromised. An architectural framework for an Enhanced Multi-Party Fully Homomorphic Encryption Scheme (EMHES) was designed to combat Man-in-the-Middle (MitM) attacks targeting Vulnerability Score manipulation. By employing Homomorphic Encryption, the framework enables computations on encrypted vulnerability scores, ensuring confidentiality throughout their lifecycle. Key enhancements include integrating digital signatures to authenticate classified scores before encrypted transmission to cloud environments and verify the integrity of decrypted results post-processing. Digital signatures and regulatory oversight significantly strengthen security properties like non-repudiation, integrity, and confidentiality for cloud-based data computations. The EMHES architecture features a secure transmission channel with multiple security layers within the cloud service provider infrastructure. Additional security mechanisms include secure key management protocols, zero-knowledge proofs for integrity verification, and a resilient secure aggregation protocol designed to counter MitM attacks. From a computational analysis, baseline algorithms exhibit constant time complexity O(1), while the EMHES architecture operates with linear time complexity O(n). The result shows that EMHES provides superior security, integrity and performance on large datasets.
The rapid development of cryptocurrency as a digital financial asset has introduced new challenges for the prevention and eradication of money laundering crimes. While cryptocurrencies offer efficiency, decentralization, and borderless transactions, these very characteristics also create significant vulnerabilities for misuse, particularly in facilitating illicit financial flows. In Indonesia, the existing legal framework on anti-money laundering, primarily regulated under Law Number 8 of 2010, was formulated prior to the widespread adoption of cryptocurrency and therefore faces limitations in addressing technology-driven financial crimes. This article examines the challenges of law enforcement in combating cryptocurrency-based money laundering in Indonesia through a normative juridical approach. The study analyzes relevant statutory regulations, institutional authority, and enforcement mechanisms involving agencies such as PPATK, Bappebti, the Financial Services Authority, and law enforcement bodies. The findings indicate that law enforcement faces substantial obstacles, including regulatory fragmentation, jurisdictional complexities, difficulties in tracing blockchain-based transactions, evidentiary constraints, and limited technical capacity among enforcement institutions. Furthermore, the absence of comprehensive regulation concerning decentralized finance and non-custodial digital wallets exacerbates enforcement difficulties. This article argues that without regulatory harmonization, enhanced institutional coordination, and the integration of technological capabilities into law enforcement practices, the Indonesian legal system risks lagging behind the evolving landscape of financial crime. Strengthening adaptive legal frameworks is therefore essential to ensure effective anti-money laundering enforcement in the digital asset era.
To prevent the reprocessing of the same trade message in different distributed financial infrastructures, electronic trading systems must have powerful duplicate trade detection protocols. Redundant messages are a result of network timeouts, TCP retransmission protocols, upstream retry queues, and manual resubmission workflows that are part of heterogeneous trading structures. Idempotency models define message uniqueness by using composite business keys, cryptographic fingerprints using the SHA-256 hashing functions, and deduplication logic on time windows that trades off between accuracy of detection and scalability of computation. Graphed graph frameworks are enhanced with blockchain and deliver distributed data models to specify intricate trade relations in the form of immutable ledger records, smart contract validation logic, and multi-channel designs, which assure information integrity across trading networks. Multi-channel correlation algorithms differentiate between actual trade amendments and replay events based on machine learning classification models and partial fill cases and cross-venue execution strategies. Strategies of implementation are used to optimize parameters of tolerance windows with the use of hierarchical composite key matching, progressive sampled indexing, and container-based pre-fetching strategies. Microsecond-latency duplicate-detection In-memory caching architectures in conjunction with Bloom filter probabilistic structures can achieve duplicate detection at millions of trade messages per day to protect downstream risk management and regulatory reporting systems against position inflation and compliance violations.
The system of financial law, as one of its major sections, includes decentralized funds (legal regulation of financial relations associated with the formation, distribution and use of funds of a decentralized nature). In today's socioeconomic development, the number of such funds is increasing, and, accordingly, the quality of the Russian Federation's financial system is changing. Undoubtedly, as a consequence of these processes, the financial legal system and its individual institutions are being transformed. Compensation (guarantee) funds represent a special type of decentralized finance, the name of which makes their auxiliary nature clear. The paper presents a summary of the characteristics and comparative analysis of the following funds: the fund for financing the activities of the financial ombudsman, the compensation fund of the Federal Notary Chamber, and the compensation fund for reimbursement of the value of property recorded in individual investment accounts.
Wyatt Benno, Alberto Centelles, Antoine Douchet, Khalil Gibran
We present Jolt Atlas, a zero-knowledge machine learning (zkML) framework that extends the Jolt proving system to model inference. Unlike zkVMs (zero-knowledge virtual machines), which emulate CPU instruction execution, Jolt Atlas adapts Jolt's lookup-centric approach and applies it directly to ONNX tensor operations. The ONNX computational model eliminates the need for CPU registers and simplifies memory consistency verification. In addition, ONNX is an open-source, portable format, which makes it easy to share and deploy models across different frameworks, hardware platforms, and runtime environments without requiring framework-specific conversions. Our lookup arguments, which use sumcheck protocol, are well-suited for non-linear functions -- key building blocks in modern ML. We apply optimisations such as neural teleportation to reduce the size of lookup tables while preserving model accuracy, as well as several tensor-level verification optimisations detailed in this paper. We demonstrate that Jolt Atlas can prove model inference in memory-constrained environments -- a prover property commonly referred to as \textit{streaming}. Furthermore, we discuss how Jolt Atlas achieves zero-knowledge through the BlindFold technique, as introduced in Vega. In contrast to existing zkML frameworks, we show practical proving times for classification, embedding, automated reasoning, and small language models. Jolt Atlas enables cryptographic verification that can be run on-device, without specialised hardware. The resulting proofs are succinctly verifiable. This makes Jolt Atlas well-suited for privacy-centric and adversarial environments. In a companion work, we outline various use cases of Jolt Atlas, including how it serves as guardrails in agentic commerce and for trustless AI context (often referred to as \textit{AI memory}).
Suvradip Chakraborty, James Hulett, Dakshita Khurana, Kabir Tomer
A recent breakthrough [Hirahara and Nanashima, STOC'2024] established that if $\mathsf{NP} \not \subseteq \mathsf{ioP/poly}$, the existence of zero-knowledge with negligible errors for $\mathsf{NP}$ implies the existence of one-way functions (OWFs). In this work, we obtain a characterization of one-way functions from the worst-case complexity of zero-knowledge {\em in the high-error regime}. We say that a zero-knowledge argument is {\em non-trivial} if the sum of its completeness, soundness and zero-knowledge errors is bounded away from $1$. Our results are as follows, assuming $\mathsf{NP} \not \subseteq \mathsf{ioP/poly}$: 1. {\em Non-trivial} Non-Interactive ZK (NIZK) arguments for $\mathsf{NP}$ imply the existence of OWFs. Using known amplification techniques, this result also provides an unconditional transformation from weak to standard NIZK proofs for all meaningful error parameters. 2. We also generalize to the interactive setting: {\em Non-trivial} constant-round public-coin zero-knowledge arguments for $\mathsf{NP}$ imply the existence of OWFs, and therefore also (standard) four-message zero-knowledge arguments for $\mathsf{NP}$. Prior to this work, one-way functions could be obtained from NIZKs that had constant zero-knowledge error $ε_{zk}$ and soundness error $ε_{s}$ satisfying $ε_{zk} + \sqrt{ε_{s}} < 1$ [Chakraborty, Hulett and Khurana, CRYPTO'2025]. However, the regime where $ε_{zk} + \sqrt{ε_{s}} \geq 1$ remained open. This work closes the gap, and obtains new implications in the interactive setting. Our results and techniques could be useful stepping stones in the quest to construct one-way functions from worst-case hardness.
Everybody is these days plunging into decentralized applications, blockchain, and digital identity. And honestly, it's a rendering that the ancient method of authenticating yourself looks nice, outdated and risky. Whenever you give up your personal info, you are just wishing it does not find its way into a data leak or get misused. Precisely, this is the reason that we constructed a new path to manage identity checks that really care about your privacy. This is what we are doing: our framework is based on Ethereum Attestation Service (EAS) and Zero-Knowledge Proofs (ZKPs). To begin with, we generate offchain attestations based on the EIP-712 standard. Your signature remains verifiable; however, your confidential information doesn't go anywhere and through which we squeeze these attestations. SP1 using zero-knowledge virtual machine (zkVM) this step checks everything twice, the construction, the encryption, the logic, all of it. When it's all good then the system will spit a short, non-interactive Groth16 or Plonk (if)SNARK proofs known as zero-knowledge proofs (you're curious). This evidence makes things private and at the same time accurate. There is the interesting side of it: you can check it immediately in your local devices using a super lightweight browser or with a Node.js app WebAssembly verifier. It does not require any middlemen and there is no need for extra trust. You can send evidences, in case you wish to using Solidity contract on-chain. This allows the system to issue new fraudulent statements such as isOver18 = true without displaying your actual age or any other personal data. So what does this mean? You earn greater confidence, enhanced interoperability and connections through decentralize systems. Transparency is what you have with this of ZKP attestation and actual privacy. It's actually practical, as well, can be used with KYC, DeFi, age-requiring app checks, and secured access controls. Essentially, it is a huge leap higher on behalf of anonymity and trust in electronic self.
Building structural designs, utilizing materials such as steel, concrete, and cross-laminated timber, contribute significantly to embodied carbon emissions in construction projects. However, traditional carbon accounting methods employed to quantify and record these emissions are often characterized by a lack of traceability, transparency, and immutability. This limitation undermines the reliability of emissions data, making it challenging for stakeholders to establish credible emissions records and implement regulatory strategies, such as carbon credits, taxes, subsidies, and green certifications, for building’s structural designs and materials. This paper addresses these challenges by proposing a transformational emissions accounting system that integrates Building Information Modeling (BIM) for automatic extraction of emissions-relevant data, alongside blockchain-enabled smart contracts to ensure traceability and immutability of emissions records. The proposed system enables data-driven decision-making for low-carbon structural designs and materials, while also facilitating the application of emissions regulations to support their implementation based on trustworthy emissions accounting.
This article addresses the challenging problem of constrained optimal control for human–machine systems subject to external disturbances and the bounded rationality of the human operator. To this end, a novel game-theoretic framework is proposed. Unlike monolithic game formulations, the framework uniquely disaggregates the control problem by transforming it into a multifaceted game via logarithmic barrier functions (BFs): it models human–machine cooperation as a positive-sum game oriented toward shared objectives, and disturbance rejection as a zero-sum game tailored for robustness enhancement. To capture the nonideal human decision-making, we integrate the level-$k$reasoning framework to model the operator’s bounded cognitive dynamics. The corresponding coupled Hamilton–Jacobi–Isaacs (HJI) equations for this human–machine game are derived, and critically, a rigorous proof of global asymptotic stability (GAS) for the transformed system is provided, establishing a solid theoretical foundation. For online implementation without requiring prior knowledge of the system dynamics, we develop a resource-efficient learning architecture based on the adaptive dynamic programming (ADP) and a novel dynamic event-triggered mechanism (DETM). A key feature of this architecture is a fuzzy logic-based module for dynamic authority allocation, which adaptively adjusts control sharing in real time. Rigorous analysis demonstrates that all signals in the closed-loop system are uniformly ultimately bounded and that Zeno behavior is precluded. Simulation results are presented to validate the effectiveness and superiority of the proposed control strategy.
With the increase in the rate of cyber threats, such as ransomware, social engineering, and zero-day exploits, it is urgent to adopt new security mechanisms like Security Orchestration, Automation, and Response (SOAR) systems. The increase in cyber threats has not only amplified in frequency but also in sophistication. This escalation has forced organizations to rethink traditional defense strategies. SOAR has shown itself to be an important solution by automating repetitive tasks and helping security teams in focusing on strategic threat hunting as well as mitigation. The integration of AI and ML in SOAR frameworks helps in predictive analytics, in which systems can anticipate potential breaches based on pattern recognition from vast datasets. The role of blockchain is to enhance data integrity and help enable secure and decentralized threat intelligence sharing between stakeholders. This paper presents a systematic literature review (SLR) on recent advancements in SOAR technologies, especially the incorporation of artificial intelligence (AI), machine learning (ML), and blockchain; it also reviews case studies across various industry sectors, such as healthcare, finance, industrial control systems, and critical infrastructures, as well as the challenges facing SOAR adoption. By examining 29 studies from academic research, industry case studies, and technical reports, the review synthesizes methodologies, architectures, and performance outcomes to summarize the current state of SOAR systems. The research found that SOAR can significantly reduce incident response times and improve threat detection accuracy, with findings indicating that SOAR can lower response times by up to 80% compared to legacy systems, although implementation costs may reach as high as $5 million. Additionally, specialized personnel are still needed to operate these systems. The skills gap increases barriers to adoption, as few professionals possess expertise in cybersecurity as well as in automation tools. Future directions emphasize developing hybrid models that blend human intuition with machine efficiency for more robust defenses. Finally, the review discusses future research directions to help SOAR further scale, interoperate across platforms, and enable autonomous decision-making