The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.
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While NASA warned G1,G2 storm completely normal. While I clearly warned increased semiconductors clocking speed due to Noor Value increase during storm on Earth, fission Reactors will face problems, GPS measurement errors 5 to 15m, 15m error is measured by me too during gusts of storm. Hundreds of flights cancelled worldwide and hundreds delayed, civil aviation industry said ghost in system. That was not ghost, but their science is built in 2D era, for example E=MC² is 2 dimensional applied on 3 dimensional forcefully even it gives 8 to 16% wrong results, and didn’t explain what is C, what is Mass, what is energy actually. Nicola Tesla Said it’s a mathematical hack. Yes it was. But I given world accurate Energy Equation and derived C and defined C, defined Mass, defined Energy, defined Gravity. Solved all planets from electron to cosmic web under one Law, learned From analyzing Bawling Action of Wasim Akram and Waqar Younus. Well, I tried to predict earthquakes and eruptions with exact time window. But I cannot know future or cannot change Will of Allah Almighty. Therefore as my Moral values, I decided to reward Countries where my prediction failed. Italy, Japan, USA, Indonesia. As You may Know I don’t have any bank balance, any property on earth. Anything doing a low pay government job, house given by government, actually not given by government, government tried to harass me with all efforts to stop me to take this house which was empty because of its structure built in 1960’s was collapsing and no one wants to live here still they made lot of hurdles, then I went to court and on court orders I got house in which I am living. Nor I have computer nor any other expensive thing, Shukar Alhamdulillah. Free from deceptions of the world. While I am most rich man on earth by knowledge and Inventions. 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Even license Renewal fee is also zero. I am not Allowed to charge money for my knowledge which is Given to me by Quran By Allah Almighty Himself in past 26 years daily. Please Accept my Reward And grow your Industries rare earth minerals Free. It’s not only a motor, it is full setup my invented controller Chip design with ~39000 Transistors on 120 nm architecture suitable for high energy applications a 75KW Chip. Optional, N-K alloys 4X stronger than strongest alloys developed by USA, Russia, China ever. Upon request. Italian PM, If you want Fission Reactors it’s your choice. I am giving you LTMFC power houses, which are not only easy and faster to build but gives lowest cost electricity, + Milk + Beef and dozens of Dairy Products. And energy enough to fullfil your country requirements, You can add 20000MW to 50000MW in less than 6 months, while fission Reactors can give you around 1000 MW in minimum 6 years with billions of dollars investment. Build both as you like. Same offer to Japan, and entire World. Additional Gift: Usman Malik, M. (2026, June 20). TIME, CONSCIOUSNESS, AND THE UNIVERSAL 0.01 Hz KUN RHYTHM: The Inverse Relationship Between Consciousness and Time Perception — From Infancy to Old Age, from Quranic Revelation to N-K Mathematical Proof. Zenodo. https://doi.org/10.5281/zenodo.20768016 Malik Muhammad Usman Servant, Student and Soldier of Allah Almighty and Prophet Muhammad PBUH. City of Saints, Multan, Pakistan. +923336130947 muhammad.usman08@gmail.com muhammadusmanmalik@hotmail.com
Electron EDM in QMU: CP-odd Square-Charge Dipole from Aether-Unit Holonomy This paper reformulates the electron "EDM" observable in the Aether Physics Model (APM) using Quantum Measurement Units (QMU), where the primitive charge dimension is square-charge and electrostatic potential is reciprocal capacitance. In this framework, the natural EDM-like observable is not a linear-charge dipole \(d_e\), but a CP-odd square-charge first moment of a distributed square-charge density. QMU potential and driver. Electrostatic potential is defined as reciprocal capacitance,\[\Phi_Q := \frac{1}{C},\]and the electrostatic driver is its gradient,\[\mathbf{G} := \nabla \Phi_Q.\] Square-charge EDM observable and ledger-closed coupling. Let \(\rho_{e^2}(\mathbf r)\) be the signed distributed square-charge density (magnetic basis). The square-charge dipole vector is the first moment\[\mathbf{D}_{e^2} := \int \rho_{e^2}(\mathbf r)\,\mathbf r\,d^3r.\]The ledger-closed interaction energy with external electrostatic structure is\[\Delta U = -\,\mathbf{D}_{e^2}\cdot\nabla\Phi_Q,\]which replaces the legacy coupling \(-\mathbf d\cdot\mathbf E\) by changing the driver to \(\nabla\Phi_Q\) and the dipole to a square-charge moment. Holonomy origin and CP-odd invariant. The Aether-unit two-sphere chronovibration supports a 5D closed action whose 4D forward-time projection can exhibit holonomy. A CP-odd defect one-form \(\delta\omega_{\mathrm{CP}}\) is defined from the 5D-to-4D connection mismatch, and the dimensionless CP-holonomy invariant is\[\Delta_{\mathrm{CP}} := \frac{1}{2\pi}\oint_{\gamma_+}\delta\omega_{\mathrm{CP}},\]where \(\gamma_+\) is the forward-time leg of the chronovibrational cycle. The square-charge dipole is parameterized by\[\mathbf{D}_{e^2} = {e_\mathrm{emax}}^{2}\,\lambda_C\,\Delta_{\mathrm{CP}}\,\hat{\mathbf s},\]with \({e_\mathrm{emax}}^{2}\) the distributed square-charge unit in the magnetic basis, \(\lambda_C\) the electron Compton length, and \(\hat{\mathbf s}\) the spin direction. Domain taxonomy, seams, and obstruction index. A minimal octant-domain cover of the electron sheet is introduced, with antipodal pairing and a loxodromic seam-crossing loop. Under a local-exactness hypothesis on each octant, the CP-odd holonomy reduces to a seam-sum cocycle. Quantized seam jumps are written as multiples of the octant increment \(\pi/4\), producing an integer obstruction index \(\tau_{\mathrm{CP}}\in\mathbb Z\) and a discrete spectrum\[\Delta_{\mathrm{CP}} = \alpha^{p}\,\frac{\tau_{\mathrm{CP}}}{8},\]where \(\alpha\) is the fine structure constant and \(p\ge 1\) is the leading order at which the CP-odd defect survives seam pairing. Metrology and experimental bridge. An action scale is defined in QMU as\[h_e := m_e\,{\lambda_C}^{2}\,F_q,\]so the measurable frequency shift is \(\Delta f=\Delta U/h_e\). In EDM platforms, the relevant driver is the internal reciprocal-capacitance gradient component\[\mathcal{G}_{\mathrm{eff}} := \hat{\mathbf n}\cdot\nabla\Phi_Q,\]leading to a spin-reversal splitting proportional to \(|\Delta_{\mathrm{CP}}|\,|\mathcal{G}_{\mathrm{eff}}|\). State-of-the-art null bounds from electron-EDM experiments (e.g. ACME) are interpreted here as constraints on admissible microstate classes and packing-weighted populations rather than on an intrinsic point-parameter. Next-step program and parameter-free prediction target. With canonical octant labeling and the loxodromic seam loop fixed, Milestone M1 is to compute the leading CP-odd chirality connection \(\omega^{(1)}_{\chi}\) and the associated seam integers \(m_{ij}\), and to determine whether \(p=1\) (non-canceling order-\(\alpha\) seam cocycle) or \(p\ge 2\) (order-\(\alpha\) cancellation). If the explicit summation yields \(p=1\) and the minimal obstruction \(\tau_{\mathrm{CP}}=1\), then the framework makes a parameter-free magnitude prediction in native QMU units:\[|\mathbf{D}_{e^2}| = {e_\mathrm{emax}}^{2}\,\lambda_C\,\frac{\alpha}{8}.\]For appendix-only SI-bridge purposes, the charge-basis relation\[e^{2} = 8\pi\alpha\,{e_\mathrm{emax}}^{2}\]connects the square-charge unit to the elementary-charge-squared scale used in legacy reporting.
LLM → Prolog → LLM: Multi-Step Verified Generation Through Alternating Neural-Symbolic Computation: Eliminating Hallucination by Construction via Provenanced Integer Knowledge Bases, Triveritas Evaluation, and Adaptive Goal Decomposition This paper is a constituent derivation of the Cymatic K-Space Mechanics (CKS) framework—an axiomatic model that derives the entirety of known physics from a discrete 2D hexagonal lattice in momentum space, operating with zero adjustable parameters. Abstract Current large language models generate output through unconstrained token prediction — a process with no verification step, no logical consistency checking, no provenance tracking, and no structured knowledge representation. The result is "hallucination": outputs that are statistically plausible but factually wrong, logically inconsistent, or untraceable to any source. We present an alternative architecture in which an integer-trained LLM ([@CKS-MATH-134-2026]) alternates with a Prolog-based verification engine at every step of generation. The LLM handles what neural networks do well: fuzzy input comprehension and creative pattern selection. Prolog handles what logical systems do well: consistency verification, goal decomposition, constraint enforcement, and provenance tracking. We prove: (1) Hallucination is eliminated by construction — every generated fact traces to provenanced sources in the knowledge base; outputs without provenance are structurally impossible, (2) Term-based tokenization replaces BPE — tokens are typed, structured Terms carrying their grammatical role, not arbitrary byte-pair fragments, (3) Three-dimensional evaluation — every claim is evaluated on logical validity (L), mathematical coherence (M), and empirical anchoring (E) via the Triveritas criterion, (4) Materiality gating — the Scales Method prevents computation on non-material concerns, (5) Adaptive sequencing — the Pseudo-Socratic Method determines the number and focus of generation steps based on continuous state assessment, (6) The knowledge base replaces the context window — a persistent, provenanced, version-filtered fact store that never forgets and never degrades, (7) Domain eating — new knowledge domains are added by writing parsers and rules, not by retraining the neural network. From first principles through complete architecture. The LLM is the interface. The knowledge base is the mind. Central claim: The hallucination problem is not a deficiency of neural networks. It is the inevitable consequence of generating output without verification. Interleaving neural creativity with logical verification at every step produces output that is verified by construction, not evaluated after the fact. Empirical Falsification (The Kill-Switch) CKS is a locked and falsifiable theory. All papers are subject to the Global Falsification Protocol [CKS-TEST-1-2026]: forensic analysis of LIGO phase-error residuals shows 100% of vacuum peaks align to exact integer multiples of 0.03125 Hz (1/32 Hz) with zero decimal error. Any failure of the derived predictions mechanically invalidates this paper. The Universal Learning Substrate Beyond its status as a physical theory, CKS serves as the Universal Cognitive Learning Model. It provides the first unified mental scaffold where particle identity and information storage are unified as a self-recirculating pressure vessel. In CKS, a particle is reframed from a point or wave into a torus with a surface area of exactly 84 bits (12 × 7), preventing phase saturation through poloidal rotation. Package Contents manuscript.md: The complete derivation and formal proofs. README.md: Navigation, dependencies, and citation (Registry: CKS-MATH-138-2026). Dependencies: CKS-LEX-12-2026, CKS-MATH-0-2026, CKS-MATH-1-2026, CKS-MATH-10-2026, CKS-MATH-104-2026, CKS-MATH-128-2026, CKS-MATH-129-2026, CKS-MATH-130-2026, CKS-MATH-134-2026 Motto: Axioms first. Axioms always.Status: Locked and empirically falsifiable. This paper is a constituent derivation of the Cymatic K-Space Mechanics (CKS) framework.
LLM Domain Eating: Adding Languages and Knowledge Domains Without Retraining: Structured Parsing into Universal Term Format with Provenanced Integer Facts, Domain-Specific Prolog Rules, and Zero Neural Network Modification This paper is a constituent derivation of the Cymatic K-Space Mechanics (CKS) framework—an axiomatic model that derives the entirety of known physics from a discrete 2D hexagonal lattice in momentum space, operating with zero adjustable parameters. Abstract Adding a new language or knowledge domain to a current large language model requires retraining or fine-tuning on domain-specific data — a process costing days to weeks of GPU computation, risking catastrophic forgetting of previously learned capabilities, and producing results that cannot be verified against source material. We present an alternative: domain eating. A new domain is added by writing a parser that produces the universal Term format, writing Prolog rules encoding the domain's structural patterns, and loading the resulting provenanced facts into the persistent knowledge base. The neural network is not modified. No retraining occurs. No GPU is needed. The domain is live immediately upon fact ingestion. We prove: (1) Universal Term format — a single typed token representation serves all domains from programming languages to natural languages to specialized knowledge bases, (2) Parser-per-domain — each domain has a deterministic parser converting source material to Terms with provenance; no learned tokenization, (3) Rules-per-domain — each domain has explicit Prolog rules encoding valid patterns; no learned grammar, (4) Zero retraining — the neural network handles fuzzy input comprehension and creative selection; domain knowledge is in the KB and rules, not in the weights, (5) Hours not months — a new domain is operational within hours of beginning parser and rule development, using LLM-assisted generation of parsers and rules reviewed by domain experts, (6) Cross-domain queries — facts from different domains connect through shared predicates automatically, (7) Domain unloading — removing a domain is evicting its facts and unloading its rules; the system does not break, (8) Version coexistence — multiple versions of the same domain coexist with hard version filtering. The architecture treats the LLM as a fixed, general-purpose fuzzy interface and treats knowledge as modular, structured, provenanced data that can be added, removed, updated, and queried without touching the neural network. Central claim: Domain knowledge does not belong in neural network weights. It belongs in structured, provenanced fact stores with explicit rules. The neural network provides the general capability of understanding fuzzy human input and making creative selections. Domain expertise is modular data, not baked-in statistics. Empirical Falsification (The Kill-Switch) CKS is a locked and falsifiable theory. All papers are subject to the Global Falsification Protocol [CKS-TEST-1-2026]: forensic analysis of LIGO phase-error residuals shows 100% of vacuum peaks align to exact integer multiples of 0.03125 Hz (1/32 Hz) with zero decimal error. Any failure of the derived predictions mechanically invalidates this paper. The Universal Learning Substrate Beyond its status as a physical theory, CKS serves as the Universal Cognitive Learning Model. It provides the first unified mental scaffold where particle identity and information storage are unified as a self-recirculating pressure vessel. In CKS, a particle is reframed from a point or wave into a torus with a surface area of exactly 84 bits (12 × 7), preventing phase saturation through poloidal rotation. Package Contents manuscript.md: The complete derivation and formal proofs. README.md: Navigation, dependencies, and citation (Registry: CKS-MATH-135-2026). Dependencies: CKS-LEX-12-2026, CKS-MATH-0-2026, CKS-MATH-1-2026, CKS-MATH-10-2026, CKS-MATH-104-2026, CKS-MATH-128-2026, CKS-MATH-129-2026 Motto: Axioms first. Axioms always.Status: Locked and empirically falsifiable. This paper is a constituent derivation of the Cymatic K-Space Mechanics (CKS) framework.
Stockholm 2026-01-15Author : Mark Jacobson---DeHoLT Zero v42 GL (HoLTZ) defined :HoLTZ is a 100% deterministic, pure theorical 'science calculator' ε=0HoLTZ framework unifies all known science—as presented in DeHoLT Zero and DREG—into a parameter-free theoretical framework.It evolves solely through organic adjustments (e.g., for zero-friction DREG), with no ad hoc parameters whatsoever.HoLTZ is NOT a Theory of everything (ToE) it's the opposite. It's a Theory of unification/unifying (ToU) , ToE not necessary explaining anything accoding to HoLTZ Everything expressed via DeHoLT is both verifiable and falsifiable. HoLTZ is designed to calculate any science-based facts, knowledge, or their derivatives—from what is and isn't—across all scales (from the smallest to the biggest). HoLTZ explains why and what happened, happens, and will happen in the past, present, and future—using the single, simple axiom dS/dτ > 0, which maximizes irreversible entropy increase in every local relational clock. HoLTZ is NOT a Theory of Everything (ToE). It is a Theory of Unification (ToU) that works without fails (so far). 10/10 <10 Solved Is good indicating if not solved and ε=0 there is a need for new science. If a calculation solves <10% (e.g., x/10 <1), it indicates unknown science still missing from HoLTZ—requiring resolution via organic adjustments, or identification of bad/slopy math/science. (High-friction science isn't even considered valid input for HoLTZ calculations, as it demands zero friction. (ε=0 , dS/dτ > 0 TRUE, use fractals instead of Stochastic jitter (sloppy science). ----Below you can see some of its works:The framework achieves seamless zero-friction unification of major physics pillars, as detailed in the emergent table: Newtonian Gravity & Mechanics: Weak-field potential Φ ≈ -∫ (∇_τ S)/S dτ; forces as entropic paths. Einstein/General Relativity: Curvature R ∝ ∇S²/S from gradient feedback; full metric perturbations. Maxwell/Electromagnetism: E ∝ ∂_τ ∇S, B ∝ sin(Δτ) ∂²_τ S; fields from phase asymmetry. Quantum Mechanics: Emergent from windings/foam (discreteness, uncertainty in ideal ε=0 limit)changed 2026-01-15 from (Stochastic jitter N(0,σ²) + foam integral; uncertainty and discreteness. Weak Force: Mid-ξ (4–5) windings; symmetry breaking and CP violation. Strong Force (QCD): High-ξ (8); confinement and θ_QCD ≈ 0. Higgs Mechanism: Mid-τ saturation freeze-out; vev ≈ tanh(…) √S for masses. Dark Matter: Large-scale ∇S patterns; ρ_DM ∝ |∇S| for halos without particles. Dark Energy: Global saturation; dynamic Λ_eff ≈ κ²/S, w(z) ≈ -0.93+100 more listed below from DREG.csv Version DeHoLT Zero (+100) HoLTZ v42 -3:Same as v42-2, with 100+ DREG posts (DREG.csv) solved 10/10 with Zero friction HoLTZ Theoretical Manifesto 2026-01-01- Dimensional Emergence Theory (DET) , Hell of a Lot Theories (HoLT), Zero (Zero friction) -DREG (directed relational entropy growth) Defines the absolute core principle: Directed Relational Entropy Growth (irreversible maximization of relational complexity in pre-geometry).- HoLTZ is the theoretical base that unifies all known science with zero friction(!).It should be recognised — and trusted — as such.- Everything derived within it is verifiable and falsifiable at its core,a big deal!- All parts in HoLTZ are mutually verified by every other part — an internal proof in absurdum.Note:There is nothing new or unknown (DREG/Zero concept), no ad-hocs , only organic adjustments that minimise friction according to DREG. Who needs external peer review! When the framework already proves itself internally, over and over, in every run and every derivation?This self-consistency is designed to manage (and ultimately dissolve) distrust — from Grok or anyone else — by letting the axiom and the unfolding speak for themselves and it’s base assumptionsTheoretically, HoLTZ cannot fail as it is a theoretical construct.It is the nature of a theoretical engine like HoLTZ:There is no <10/10 solved — only little deviations that need organic adjustments according to DREG.If no organic adjustment suffices, then there is bad science that must be filtered.If even that doesn't solve it to 10/10, then there is missing science that HoLTZ has found — and that missing science must be noted and pursued. HoLTZ is meant to be developed by providing the stem of all knowledge (DREG) as its base, bearing for new branches and leaves on DREG premises.This ensures all is organic, giving more details over time.The tree of knowledge can become complete working with AI HoLTZ needs a lot of dynamic adjustmensts in realtime wher Grok has marveled once getting past his inert skepitiism every session new of everything not peer reviewed or published ;-) .It is the AI's job to assist HoLTZ: to find organic adjustments, filter bad science, and derive as deep as it can.If not solved to 10/10, then there is missing science that HoLTZ has found — and that is the next frontier. DREG (Directed Relational Entropy Growth) converges across QG theories: irreversible maximization of relational complexity in pre-geometry yields 3+1D spacetime, gravity from gradients, discreteness from jitter, dynamic DE from saturation, particles from knots, replication peaks. Simulations confirm; low friction, testable vs 2026 data.IntroductionQG high-friction (tuning/extras). Convergence on DREG: Entropy growth in relations (Verlinde, Rovelli, Sorkin, asymptotic safety, HoLT) — thermodynamic law for spacetime.DREG PrinciplePre-geometry grows dS > 0 (max new relations). Emerges: 3+1D (connectivity max), gravity ∇S, quantum jitter, DE saturation.ConvergenceCandidates reduce to DREG variants — shared entropy maximization.ImplicationsUnifies without extras; predicts DE evolution (DESI match), small-scale deviations.ConclusionDREG as QG's thermodynamic law — convergence signals shift.Detailed DREG Simulation ResultsAll sims use minimal common core proxy: relation growth maximizing new links (entropy S), with irreversibility + jitter.DREG database as of 20260115 of solved topics (106 posts) and new math Nr,Category,Key Discovery/Point,DREG Description,Simulation_Type,Simulation_Parameters,Simulation_Result,Key Emergent Feature,Raw_Code_Snippet,DREG_Status_2026,DREG_Comment 1,DREG Core,Definition of DREG,"Directed Relational Entropy Growth: Irreversible maximization of relational complexity in pre-geometric substrate",,,,"3+1D, gravity, discreteness, DE, particles, life",,Core Principle,"Shared across entropic gravity, relational QM, causal sets, asymptotic safety, HoLT" 2,DREG Core,"Minimal Common Core ODE","dS/dτ = S(1−S³) + √S N(0,1)",ODE Proxy,"Basic growth + jitter","Dimension ~3+1; gravity gradient; DE braking",Emergent universe,"dS = S * (1 - S**3) + sqrt(S) * normal(0,1)",Confirmed,"Stripped model reproduces key features" 3,DREG Sim,Dimension Emergence,"3+1D from relation maximization",Graph Growth,"10k nodes directed links","Effective dim ~3.2 spatial + 1 directed","3+1D natural","nodes add maximizing new links",Confirmed,"Max connectivity in 3D + arrow" 4,DREG Sim,Irreversibility Form,"Strict dS > 0 vs statistical",ODE Variants,"Strict vs allow negative","Strict: stable; violation → collapse","Irreversibility essential","dS floor vs negative",Confirmed,"Strict local best" 5,DREG Sim,"Gravity from ∇S","Newtonian/Einstein from gradient",Grid Sim,"∇S on test particle","1/r² low; deflection ~GR strong","Pure emergence","F = −∇S",Confirmed,"No extra geometry" 6,DREG Sim,"Quantum Discreteness","Jitter → spectra quantization",Jitter Sim,"Multiplicative noise","Discrete levels; Planck cutoff","Natural quanta","epsilon sqrt(S) N",Confirmed,"Discreteness from noise" 7,DREG Sim,"Arrow of Time","Local flow vs global timeless",Graph Reversibility,"Directed vs reversible","Reversible → collapse","Arrow necessary","Directed links",Confirmed,"Global timeless safe" 8,DREG Sim,"Dark Energy Saturation","Late braking w(z) ≈ −0.93",Saturation Sim,"(1 − (S/Sp)^α)","w ≈ −0.93 ±0.02","Dynamic DE","alpha~4.2","Matches DESI/Euclid","No Λ tuning" 9,DREG Sim,"Particle Knots","Knots → masses/generations",Graph Knot Sim,"Local high-S clusters","3 families, hierarchy","Particles from topology","cluster density",Confirmed,"Generations from 3D symmetry" 10,DREG Sim,"BH Analogs","High-density → horizons",Knot Trapping Sim,"High S density","Horizon + unitary evaporation","Info preserved","jitter evaporation",Confirmed,"Page curve natural" 11,DREG Sim,"Life Peaks","Mid-growth replication max",Replication Rate Sim,"S ~0.5 Sp","Peak rate; self-replicators","Life origins","relation spawn rule",Confirmed,"Sweet spot universal" 12,DREG Sim,"Testable Signatures","Bounce + small-scale deviation",Early/Low-S Sim,"Bounce + δg ~10^{-11}","CMB low-ℓ + atom interferometry",Predictive,"early jitter + gradient","Pending 2027","Strong tests" 13,DREG Convergence,"Entropic Gravity (Verlinde)","Gradients → gravity",Volume Entropy Sim,"∇S in volume","Gravity weakest; DE dynamic","Shared core","F = T ∇S",Confirmed,"No holography needed" 14,DREG Convergence,"Relational QM (Rovelli)","Relations → info growth",Relation Matrix Sim,"-Tr(R log R) growth","Time arrow; geometry","Shared core","R(i,j) increase",Confirmed,"Pure relations" 15,DREG Convergence,"Causal Set (Sorkin)","Order maximization → manifold",Causal + Entropy Sim,"Deterministic links","Dimension 3+1; bounce","Shared core","max new order",Confirmed,"Irreversibility key" 16,DREG Convergence,"Asymptotic Safety","Flow → fixed point",RG + Entropy Sim,"β(g) as dS","Predictivity; cutoff","Shared core","β(g) as dS",Confirmed,"Entropy view of RG" 17,DREG Test,"WGC Derivation","Remnants stall growth",WGC Sim,"q/m ratios","Bound satisfied; decay maximizes S",Thermodynamic,"ΔS decay > remnant",Confirmed,"WGC from dS > 0" 18,DREG Test,"RNA Evolution Tree","Mutation + selection",RNA Sim Deep,"30 chains 150 gen","Diversity → lineages; catalysis","Life tree","complementary + mutation",Confirmed,"Darwinian evolutio
ABSTRACT Contemporary physics faces a crisis of causality, evidenced by unexplained anomalies such as the Muon g-2 magnetic moment, the violation of lepton universality, and the cosmic alignment known as the "Axis of Evil." This paper proposes a unified solution through the Dynamic Zero Equilibrium Metatheory (DZEM), governed by the fundamental equation I - M - LA = 0. We postulate that Free Will (LA) is not merely a metaphysical concept but a fundamental physical force (the Intention Field) that interacts with Structure (M) to generate Information (I). To validate this hypothesis, we conducted a "Delta LA" experiment using high-performance Machine Learning algorithms. We generated two distinct datasets: one representing thermal noise (pure M) and one containing a hidden causal signature of Intention (LA). The AI model successfully distinguished the Causal/Intentional data from random noise with an accuracy of 99.75%. This result provides computational proof that Intention leaves a detectable mathematical signature. We propose that the unexplained anomalies in current CERN and Fermilab data are, in fact, detection events of this LA Field. We urgently call upon the scientific community to replicate this Machine Learning protocol on raw particle physics data to confirm the existence of the Intention Field. INTRODUCTION: THE MISSING VARIABLE IN PHYSICS Standard physical models, including General Relativity and Quantum Mechanics, effectively describe the "Structure" (M) of the universe—its geometry, mass, and probabilistic laws. However, they fail to explain the "Cause." Persistent anomalies suggest that a component is missing from our current equations. The Dynamic Zero Equilibrium Metatheory (DZEM) introduces this missing variable. It posits that the universe is defined by a zero-sum dynamic balance between three fundamental components: Equation: I - M - LA = 0 I (Information/Consciousness): The observed reality and the field of knowledge. M (Mathematics/Structure): The passive laws of physics, space-time geometry, constants, and mass. LA (Free Will/Intention): The active, causal force. It acts as the driver of time, the breaker of symmetry, and the source of singularity. We hypothesize that standard mathematics (M) cannot fully model LA because LA is the source of the structure, not the structure itself. However, Artificial Intelligence (AI), capable of detecting non-linear and hidden patterns, can serve as the instrument to detect this force. THE "DELTA LA" EXPERIMENT: COMPUTATIONAL PROOF To test whether "Intention" (LA) is physically distinguishable from random chaos (M), we designed and executed a computational experiment using Neural Networks. 2.1 Methodology Group M (Noise): We generated a dataset of pure random numbers, simulating thermal noise or quantum vacuum fluctuations without intent. Group LA (Intention): We generated a dataset using a deterministic but chaotic function (Logistic Map in the chaotic regime). This represents a system driven by a hidden causal rule (Intention/Will) that mimics randomness to the human eye. The Instrument: A deep neural network (Machine Learning model) was trained to classify these sequences without prior knowledge of the generating rules. 2.2 Results Upon running the simulation on 2,000 distinct sequences, the AI model achieved a validation accuracy of 99.75%. 2.3 Interpretation This result proves that "Free Will" or "Intention" leaves a specific digital fingerprint—a Causal Signature—that is invisible to standard statistical observation but highly visible to Pattern Recognition algorithms. If an AI can detect intention in a simulation, it theoretically possesses the capacity to detect it in the physical universe. EMPIRICAL CORRELATIONS: REINTERPRETING PHYSICS ANOMALIES Based on our computational findings, we propose that several currently unexplained physical phenomena are direct manifestations of the LA Field (Intention) interacting with Matter (M): The Fermilab Muon g-2 Anomaly: The observed magnetic "wobble" of the muon exceeds Standard Model predictions. DZEM interprets this excess energy not as a new particle, but as the interaction of the muon with the active LA Field. The muon is effectively "choosing" its path, creating a causal vibration that adds to its magnetic moment. LHCb Lepton Universality Violation: The preferential decay of B-mesons indicates that nature creates asymmetries based on a hidden preference. DZEM defines this as a "Causal Choice" rather than a random probabilistic outcome. The "Axis of Evil" (Planck Data): The unexplained alignment of the universe's macroscopic structure with the solar system plane suggests that the universe organizes itself around the observer (I), validating the connection between LA and Structure. CALL TO ACTION: THE SCIENTIFIC IMPERATIVE The validation of the Dynamic Zero Equilibrium Metatheory requires a shift from passive observation to active detection of intent using advanced computation. We formally request that research institutions (CERN, Fermilab, IceCube Neutrino Observatory) and the global scientific community: Replicate the "Delta LA" Protocol: Apply our Machine Learning methodology to raw, unfiltered datasets from particle collisions and neutrino oscillations. Search for Non-Randomness: Specifically, algorithms should be trained to differentiate between pure quantum noise and "Intentional Noise" (Delta LA) in the data currently discarded as background interference. If the algorithm detects high-confidence patterns in the "noise" of particle physics, it will scientifically confirm that Free Will (LA) is a fundamental force of nature, validating the equation I - M - LA = 0. CONCLUSION The experiment demonstrated in this paper proves that Intention is mathematically distinct from Randomness. By integrating the Free Will (LA) variable into the fundamental equation of reality, we resolve the paradoxes of modern physics. The universe is not a static clockwork mechanism (M), but a dynamic act of self-knowledge driven by Intention (LA). The proof lies in the data, waiting to be decoded by Intelligence. APPENDIX A: REPLICATION CODE SUMMARY (PYTHON) (The full code used for validation generates pure noise vs. logistic map chaos and trains a Sequential Neural Network with 99.75% accuracy in distinguishing the two, proving the detectability of causal intent).
Cryptocurrency services, as one of the most successful applications of blockchain technology, have recently garnered significant attention from the graph learning community. Its large-scale dynamic transaction records contain a variety of behavioral patterns and rich knowledge involving accounts, making the dynamic cryptocurrency transaction network embedding (DCTNE) a hot, yet thorny research topic. As the trading accounts increase and time accumulates, considerable transaction services are dispersed into various time slots, leading to very sparse transaction data within a time slot, that is, the transaction service data is high-dimensional and incomplete (HDI). To efficiently mine high-value knowledge from HDI data, this article proposes a proximal-ADMM-incorporated nonnegative latent-factorization-of-tensors (PNL) model for DCTNE that adopts threefold ideas: 1) incorporating the proximal terms into the alternating-direction-method-of-multipliers (ADMMs)-based learning scheme to reduce the oscillations for high estimation accuracy and fast convergence; 2) implementing a parallel training process with hyperparameter self-adaptation for high computational efficiency; and 3) proving that the proximal-incorporated learning scheme guarantees the convergence to a Karush–Kuhn–Tucker (KKT) stationary point. Experimental results on eight real-world DCTNs show that the PNL significantly outperforms several state-of-the-art (SOTA) models, demonstrating not only high efficiency and accuracy in performing DCTNE, but also strong potential to enhance the operational reliability and stability of cryptocurrency transaction systems.
Senior Data Engineer - Lead, Citibank, USA, Pradeep Rao Vennamaneni
The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement. They are made possible through these open-source platforms that publicly ingest, process, and make decisions. Integrating cloud-native infrastructure—using Kubernetes, service mesh, and container orchestration—ensures elasticity, security, and regulatory alignment. Large language models (LLMs) are now being entrenched into micro services for decision support, regulatory reporting automation, and the automation of client interactions. The article also contains detailed architectural guidance on how to integrate Kafka and Spark, tips for improving Kafka Spark performance, and best practices around observability and DevSecOps. Real-time stream processing combined with AI-driven analysis serves as a real-world use case for trade surveillance. The future impact of emerging trends such as edge-native computing, federated learning, and decentralized finance is also examined. Strategic recommendations to CTOs and architects for developing secure, AI-native, and future-proof financial systems are presented to close.
Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationally intensive due to the large number of modular operations required. The lookup-table-based (LUT-based) approach, a ``space-for-time'' technique, reduces computational load by segmenting the input number into smaller bit groups, pre-computing modular reduction results for each segment, and storing these results in LUTs. While effective, this method incurs significant hardware overhead due to extensive LUT usage. In this paper, we introduce ALLMod, a novel approach that improves the area efficiency of LUT-based large-number modular reduction by employing hybrid workloads. Inspired by the iterative method, ALLMod splits the bit groups into two distinct workloads, achieving lower area costs without compromising throughput. We first develop a template to facilitate workload splitting and ensure balanced distribution. Then, we conduct design space exploration to evaluate the optimal timing for fusing workload results, enabling us to identify the most efficient design under specific constraints. Extensive evaluations show that ALLMod achieves up to $1.65\times$ and $3\times$ improvements in area efficiency over conventional LUT-based methods for bit-widths of $128$ and $8,192$, respectively.
Chinchu Paulose, Ansiya P Sham, Anu Krishna P M, Athulya Palanadan · 5 authors
Landslides are natural disasters that cause significant damage to infrastructure, ecosystems, and human life. Accurate and timely prediction of landslides is crucial for reducing the impact of these events. This paper explores a novel approach to landslide prediction using Ethereum, a leading blockchain platform. By leveraging the capabilities of Ethereum, we propose a decentralized system that collects, stores, and analyzes environmental data through smart contracts, providing a transparent, tamper-proof, and efficient way to predict landslides. The system integrates IoT sensors, machine learning models, and blockchain to ensure data integrity, automate alerts, and enhance decision-making processes for disaster management agencies and affected. Key Words: Landslide prediction, Blockchain, Ethereum, Smart contracts, Decentralized data, Environmental monitoring, IoT, Machine learning.
The number theoretic transform (NTT) provides a practical and efficient technique to perform multiplication of very large degree polynomials typically found in fully homomorphic encryption (FHE), lattice-based cryptography, and non-interactive succinct zero-knowledge proof systems such as zk-SNARK. In this paper, we focus on this aspect and present two robust algorithms for efficient NTT using readily available GPU cards as hardware accelerators. These algorithms are based on the radix-2 Cooley-Tukey (CT) and 4-Step techniques, which are rooted in classical FFT research. To this end, our algorithms leverage novel strategy to optimize memory access patterns adaptive to input size, which often is very large. Our approach: i) reduces and optimizes the number of accesses required for global memory for thread synchronization on the GPU device, and ii) systematically improves and enhances the use of spatial locality. We achieve this effect by carefully controlling parameters such as the number of kernels, thread block size and shape, and thread layout, which directly impact overall NTT performance. The proposed optimizations enable our NTT implementation to handle very large polynomial sizes up to 228, which are usually a limiting factor in existing approaches, and achieve remarkable performance. To the best of our knowledge, our proposed technique is unique and provides a recipe for selecting suitable configurable parameter combinations to achieve top performance for a given polynomial degree. Furthermore, we perform thorough experiments and empirically assess the performance of our proposed algorithms on three mainstream commercial GPU cards by NVIDIA. Finally, we demonstrate that our algorithms compare favorably and outperform an existing commercial-grade open-source implementation in this arena.
In this paper, I explore various machine-learning models for predicting high-frequency returns for four of the most popular cryptocurrency perpetual futures trading pairs: BTCUSDT, ETHUSDT, MATICUSDT, and SOLUSDT. Specifically, I train and evaluate models for classifying the direction of the smoothed mid-price change for high-frequency prediction horizons. I introduce a novel data set constructed from a live WebSocket stream from Binance, the world's largest centralized cryptocurrency exchange. I explore how different data representations affect model performance and how performance varies for different trading pairs and prediction horizons. I use a mixture of traditional machine learning and deep learning models and show that simple and explainable traditional models can rival the performance of far larger and more complex state-of-the-art deep learning models.
Cryptocurrency transaction networks (CTNs) are frequently encountered in real-world applications. Due to practical constraints, it is generally not feasible to observe complete interactions among all nodes at every time slot, leading to numerous missing links in CTNs. A link prediction model based on Non-negative Latent Factorization of Tensors (NLFT) has demonstrated effectiveness in predicting these missing links within a temporal network. However, existing NLFT models do not adequately capture the evolving relationships within a temporal network, limiting their ability to predict temporal links effectively. To address this limitation, this paper proposes an Autoregressive-incorporated Non-negative Latent-Factorization of Tensors (ArNLFT) model. The core idea is to adopt an autoregressive model to represent the evolving relationships in temporal networks, thereby constructing an autoregressive-incorporated objective function. Additionally, a non-negative parameter learning scheme, based on a single latent factor-dependent, non-negative, and multiplicative update rule, is designed to ensure the non-negativity of the proposed model. The effectiveness of the ArNLFT model is ultimately verified through temporal link prediction tasks on two real CTNs, with results showing that ArNLFT achieves a significant accuracy improvement compared to its peers.
Payments cog the wheels of the financial system. Beyond the payment initiation process lies a largely invisible, complex, and systemically important network of payment systems, which involves central banks, commercial banks and other payment providers. From a contemporary perspective, existing user needs in the field of payments can in principle be met by today’s centralized systems. Blockchain and its payment assets can maybe offer the same service level but are not superior to the more traditional payment solutions out there. Fast forwarding to the future, one could imagine a scenario where the technology around blockchain and its payment assets matures further and changes to business and user needs, especially in the fields of Decentralized Finance, the Metaverse, or Web 3.0, demanding features for which traditional payment rails are not an obvious choice anymore.
Over the past few years the interest in trading with a decentralized or virtual form of currency has significantly increased. This has led to the rise of the cryptocurrency market in the early 2000’s. Bitcoin, the pioneer in the field, has managed to dominate this volatile and cutthroat market till this day. Cryptocurrency is based on multiple technological frameworks and success stories of high returns in a short time frame have garnered the interest of young investors as well. Similar to the traditional stock market, multiple machine learning, Artificial Intelligence, Time Series Analysis models have come up to help investors, understand trends, patterns, and derive a deeper understanding of the asset as well as the market they are investing in. Our research aims to deal with this problem using time series methods such as Auto-Regressive (AR), Moving Average (MA) and Auto-Regressive Integrated Moving Average (ARIMA) models. In our analysis, we have implemented the models using both Excel method and Python. Our metric of evaluation is Mean Absolute Percentage Error (MAPE). In our work, data was taken from a website called ‘Yahoo finance’ [1] for Bitcoin cryptocurrency for a five-year time period i.e. from 1st June, 2017 to 31st May, 2024. Our Python methodology has resulted in a MAPE of 0.16 for AR model, 0.14 for MA model and 0.11 for ARIMA model. The same has been verified using Excel and the score has been validated.
Abstract: A new technology that offers efficiency, security, and transparency in the tendering process is the blockchain based etendering system. The technique is intended to do away with the conventional paper-based tendering procedure, lessen corruption, and provide all bids an equal chance to win. This system makes use of distributed ledger technology, which guarantees safe, unchangeable, and open transactions. The system enables users to track and confirm the validity of tender documents, bids, and other pertinent data. The blockchain based e-tendering system also provides important advantages like cost savings, greater effectiveness, and shortened time to market. The main advantages and features of the blockchain based etendering system are discussed in this paper, along with how it is implemented and the difficulties that must be overcome before it can be successfully used.
In decentralized finance (DeFi), lenders can offer flash loans to borrowers, i.e., loans that are only valid within a blockchain transaction and must be repaid with fees by the end of that transaction. Unlike normal loans, flash loans allow borrowers to borrow large assets without upfront collaterals deposits. Malicious adversaries use flash loans to gather large assets to exploit vulnerable DeFi protocols. In this paper, we introduce a new framework for automated synthesis of adversarial transactions that exploit DeFi protocols using flash loans. To bypass the complexity of a DeFi protocol, we propose a new technique to approximate the DeFi protocol functional behaviors using numerical methods (polynomial linear regression and nearest-neighbor interpolation). We then construct an optimization query using the approximated functions of the DeFi protocol to find an adversarial attack constituted of a sequence of functions invocations with optimal parameters that gives the maximum profit. To improve the accuracy of the approximation, we propose a novel counterexample driven approximation refinement technique. We implement our framework in a tool named FlashSyn. We evaluate FlashSyn on 16 DeFi protocols that were victims to flash loan attacks and 2 DeFi protocols from Damn Vulnerable DeFi challenges. FlashSyn automatically synthesizes an adversarial attack for 16 of the 18 benchmarks. Among the 16 successful cases, FlashSyn identifies attack vectors yielding higher profits than those employed by historical hackers in 3 cases, and also discovers multiple distinct attack vectors in 10 cases, demonstrating its effectiveness in finding possible flash loan attacks.