Blockchain Papers

Lihki Rubio, Keyla Alba, Carlos E Velásquez, Filipe R. Ramos

Accurately forecasting Bitcoin’s conditional variance is essential for reliable Value-at-Risk (VaR) estimation yet remains challenging due to nonlinear dynamics, volatility clustering, and heavy-tailed return distributions. This study developed a novel stacking ensemble that integrates econometric and machine-learning models through XGBoost meta-learning to produce improved variance forecasts. Hybrid ML–GARCH specifications are incorporated separately to enrich the comparative analysis. All estimators are trained with time-aware cross-validation to ensure temporal coherence and prevent look-ahead bias. Using Bitcoin data from 2014 to 2020, the empirical results show that the stacking ensemble consistently outperforms both standalone and hybrid alternatives in conditional variance forecasting and VaR accuracy, including during periods of severe market stress such as the COVID-19 episode. Residual diagnostics confirm that the ensemble effectively captures persistent temporal dependencies in volatility dynamics. Overall, the proposed methodology offers an innovative and interpretable risk-management tool for financial institutions, combining statistical rigor with the adaptability of machine-learning techniques in digital asset markets.

Tianqi Zhu, Yuxiang Huang, Zhihong Liang, Mingming Qin · 7 authors

Blockchain technology, with its characteristics of decentralization, immutability, auditability, and traceability, has gradually become a core infrastructure in the digital economy era, demonstrating great potential in fields such as finance, government services, and the Internet of Things (IoT). However, as the scale of blockchain networks expands and data volumes surge, issues such as full-node storage redundancy, limited transaction throughput, and inefficient synchronization of historical data have become increasingly prominent, severely restricting the large-scale application of blockchain systems. The storage scalability problem faced by blockchain is therefore becoming more critical. To address the challenge in which on-chain storage expansion still cannot meet the demand for large-scale data storage, a storage method combining the InterPlanetary File System (IPFS) with blockchain, referred to as IPFS-BC, is proposed. In IPFS-BC, large-scale raw data are stored in the decentralized and content-addressable IPFS network, while the blockchain only retains the unique content identifier (CID) hash and related metadata. Through smart contracts enabling dynamic permission management and fine-grained access control, efficient interaction and collaborative storage between on-chain and off-chain systems are achieved. In this work, file upload simulation experiments were conducted, and two evaluation indicators—storage space consumption and storage performance (file read/write time and speed)—were used to compare three storage approaches: Distributed Hash Table (DHT)-based off-chain storage, Financial Blockchain Shenzhen Open Source (FISCO BCOS) on-chain storage, and the IPFS-BC on-chain/off-chain collaborative storage model. Experimental results show that the IPFS-BC model reduces storage space consumption by approximately 75% compared with FISCO BCOS blockchain storage when storing file data, significantly decreasing data redundancy. Moreover, IPFS-BC ensures system security during the on-chain process, and through the automated management and auditing provided by smart contracts, it effectively enhances system security and realizes scalable on-chain/off-chain collaborative storage.

Giulio Caldarelli

Unlike Ethereum, which was conceived as a general-purpose smart-contract platform, Bitcoin was designed primarily as a transaction ledger for its native currency, which limits programmability for conditional applications. This constraint is particularly evident when considering oracles, mechanisms that enable Bitcoin contracts to depend on exogenous events. This paper investigates whether new oracle designs have emerged for Bitcoin Layer 1 since the 2015 transition to the Ethereum smart contracts era and whether subsequent Bitcoin improvement proposals have expanded oracles' implementability. Using Scopus and Web of Science searches, complemented by Google Scholar to capture protocol proposals, we observe that the indexed academic coverage remains limited, and many contributions circulate outside journal venues. Within the retrieved corpus, the main post-2015 shift is from multisig-style, which envisioned oracles as co-signers, toward attestation-based designs, mainly represented by Discreet Log Contracts (DLCs), which show stronger Bitcoin community compliance, tool support, and evidence of practical implementations in real-world scenarios such as betting and prediction-market mechanisms.

Zhenyu Xu, Victor S. Sheng

Protecting the intellectual property of large language models (LLMs) is a critical challenge due to the proliferation of unauthorized derivative models. We introduce a novel fingerprinting framework that leverages the behavioral patterns induced by safety alignment, applying the concept of refusal vectors for LLM provenance tracking. These vectors, extracted from directional patterns in a model's internal representations when processing harmful versus harmless prompts, serve as robust behavioral fingerprints. Our contribution lies in developing a fingerprinting system around this concept and conducting extensive validation of its effectiveness for IP protection. We demonstrate that these behavioral fingerprints are highly robust against common modifications, including finetunes, merges, and quantization. Our experiments show that the fingerprint is unique to each model family, with low cosine similarity between independently trained models. In a large-scale identification task across 76 offspring models, our method achieves 100\% accuracy in identifying the correct base model family. Furthermore, we analyze the fingerprint's behavior under alignment-breaking attacks, finding that while performance degrades significantly, detectable traces remain. Finally, we propose a theoretical framework to transform this private fingerprint into a publicly verifiable, privacy-preserving artifact using locality-sensitive hashing and zero-knowledge proofs.

Abu Hena MD Shatil

No abstract is available for this record.

Nicolai Maisch, Shengjian Chen, Alexander Robertus, Samed Ajdinović · 7 authors

This work presents a concept and implementation for the secure storage and transfer of quality-relevant data of milled workpieces from online-quality assurance processes enabled by real-time simulation models. It utilises Non-Fungible Tokens (NFT) to securely and interoperably store quality data in the form of an Asset Administration Shell (AAS) on a public Ethereum blockchain. Minted by a custom smart contract, the NFTs reference the metadata saved in the Interplanetary File System (IPFS), allowing new data from additional processing steps to be added in a flexible yet secure manner. The concept enables automated traceability throughout the value chain, minimising the need for time-consuming and costly repetitive manual quality checks.

Masaomi Hatakeyama

Decentralized Autonomous Organizations (DAOs) have demonstrated that centralized authority can be replaced by distributed consensus, token-based voting, and smart contract governance. However, empirical research reveals structural limitations: voting power concentrates among large token holders, minority views are systematically excluded, and forks remain the primary mechanism for resolving fundamental disagreements. These limitations stem from a deeper assumption inherited from democratic theory—that order requires agreement. This paper introduces DEE (Decentralized Evolving Ecosystem), a complementary worldview for decentralized agent networks. Rather than producing order through consensus, DEE explores how order can emerge from fluctuating relationships among heterogeneous agents holding different philosophies. Drawing on phenomenology (Husserl, Merleau-Ponty, Levinas), process philosophy (Whitehead), complex systems science (Oosawa's loose coupling, Prigogine's dissipative structures, Kauffman's edge of chaos, Simon's near-decomposability), Eastern philosophy, and ecological theory (niche construction, diversity-stability hypothesis), we articulate a post-consensus model where: - Meaning coexists rather than being agreed upon- Multiple interpretations of the same interaction are valid- Fade-out (gradual disengagement) is a legitimate outcome, not a failure- Diversity is essential for system resilience, not merely tolerated We present HestiaChain, a blockchain-based implementation that enables philosophy declarations and observation logging without enforcing consensus. DEE does not replace DAO but offers an alternative worldview appropriate when diversity and coexistence are valued over convergence. We connect DEE to the FUTURE² framework for genomic open science, demonstrating how post-consensus models can address emerging challenges in AI-mediated research and decentralized scientific collaboration. Keywords: Decentralized Autonomous Organization, Post-Consensus, AI Agents, Loose Coupling, Complex Systems, Self-Organization, Process Philosophy, Intersubjectivity, Ecosystem Resilience, HestiaChain, FUTURE²

Shymaa M. Arafat

Abstract When Nakamoto invented Bitcoin, the first generation of cryptocurrencies followed it in applying POW (Proof of Work) consensus mechanism; due to its excessive energy consumption and heavy carbon footprints, new innovations evolved like Proof of Space , POS (Proof of Stake) , and a lot more with many variants for each. Furthermore, the emergence of more blockchain applications and kinds beyond just cryptocurrencies needed more consensus mechanisms that is optimized to fit requirements of each application or blockchain kind; examples range from IoT (Internet of Things) blockchains for sustainability applications that often use variants of BFT (Byzantine Fault Tolerance) algorithm, and consensus needed to relay transactions and/or assets between different blockchains in interoperability solutions. Previous studies concentrated on surveying and/or proposing different blockchain consensus rules, on a specific consensus issue like attacks, randomization, or on deriving theoretical results. Starting from discussing most important theoretical results, this paper tries to gather and organize all significant existing material about consensus in the blockchain world explaining design challenges, tradeoffs and research areas. We realize that the topic could fit for a complete textbook, so we summarize the basic concepts and support with tables and appendices. Then we highlight some case examples from interoperability solutions to show how flexible and wide the design space is to fit both general and special purpose systems. The aim is to provide researchers with a comprehensive overview of the topic, along with the links to go deeper into every detail.

Leeladhar Chourasiya, Sanmati Kumar Jain, Jitendra Singh Dodiya, Sheetal Bawane

It is an exploration of a decentralized social media platform, which uses blockchain and Web3 technologies to scale up privacy, security, and trust within users. The architecture uses Next.js as the front-end, solido as the smart contracts, IPFS as a distributed storage, and Web3.js to connect with the blockchain. It introduces canonical social-networking features, including user registration, content upload, like, comment and share. Notably, it keeps ownership of data to users, unlike the traditional centralized social-media platforms. The irreversibility of blockchain together with encryption makes sure that the content cannot be altered and the information about users is not at risk of unauthorized access. The system eliminates the possibility of exploiting the central level of control and increases the level of transparency. The performance appraisals indicate that the suggested platform provides better privacy, higher data security, and user agency than the mainstream networks. However, the issues of scalability and mass user adoption are still present, and the research should be further developed. This paper highlights how blockchain will reinvent social media, creating a just, transparent, and user-centric digital economy. Altogether, the study advances the discussion on decentralized social networks and demonstrates that blockchain can improve the level of trust and data protection in the process of online communication.

Lukas Weidener, Logan Bishop-Currey, Karlin Compton

Introduction Metagovernance in decentralized autonomous organizations (DAOs) refers to the mechanisms through which one DAO shapes or constrains another DAO’s governance, typically through token-based influence. Despite the growing inter-organizational relationships in decentralized ecosystems, metagovernance remains significantly understudied. Methods This scoping review followed the PRISMA-ScR guidelines and systematically searched seven electronic databases from 2008 to 2025. From the 979 initial records, seven publications met the inclusion criteria. Results Three mechanism families emerged: voting and control links, architectural layering through nested DAO structures, and participation coupling via airdrops that create governance interlocks. Recurrent challenges include procedural complexity, participation concentration, security vulnerabilities in multi-stage voting pipelines, and cross-chain infrastructure risks. A metagovernance trilemma emerged, whereby simultaneously maximizing decentralization, security, and participation proves impossible. Conclusion Metagovernance spans forum deliberation, off-chain polling, and cross-chain execution, where decision points become obscured. Future research should focus on developing uniform definitions, interoperable measurement tools, and legal frameworks for cross-jurisdictional DAO governance.

Ajay Kumar Shrestha

In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data. However, ensuring the integrity and reliability of the system is challenging due to the presence of potentially malicious or faulty nodes that can degrade the model's performance. This paper proposes a novel trust-based incentive mechanism designed to evaluate and reward the quality of contributions in FL systems. By dynamically assessing trust scores based on fac-tors such as data quality, model accuracy, consistency, and contribution fre-quency, the system encourages honest participation and penalizes unreliable or malicious behavior. These trust scores form the basis of an incentive mechanism that rewards high-trust nodes with greater participation opportunities and penal-ties for low-trust participants. We further explore the integration of blockchain technology and smart contracts to automate the trust evaluation and incentive distribution processes, ensuring transparency and decentralization. Our proposed theoretical framework aims to create a more robust, fair, and transparent FL eco-system, reducing the risks posed by untrustworthy participants.

Yifan Yang, Jinjia Li, Kunxi Li, Puhao Zheng · 10 authors

The rapid advancement of large language models (LLMs) demands increasingly reliable evaluation, yet current centralized evaluation suffers from opacity, overfitting, and hardware-induced variance. Our empirical analysis reveals an alarming inconsistency in existing evaluations: the standard deviation across ten repeated runs of a single model on HumanEval (1.67) actually exceeds the performance gap among the top-10 models on the official leaderboard (0.91), rendering current rankings statistically precarious. To mitigate these instabilities, we propose a decentralized evaluation framework that enables hardware and parameter diversity through large-scale benchmarking across heterogeneous compute nodes. By leveraging the blockchain-based protocol, the framework incentivizes global contributors to act as independent validators, using a robust reward system to ensure evaluation integrity and discourage dishonest participation. This collective verification transforms evaluation from a "centralized black box" into a "decentralized endorsement" where multi-party consensus and diverse inference environments yield a more stable, representative metric. Experimental results demonstrate that the decentralized evaluation framework reduces the standard deviation across ten runs on the same model to 0.28. This significant improvement over conventional frameworks ensures higher statistical confidence in model rankings. We have completely implemented this platform and will soon release it to the community.

Griff gurwell

Abstract & Technical Summary:This paper presents a disruptive empirical analysis of 117 celestial bodies demonstrating a systemic phase-locking to a universal frequency of 144 Hz and its fractal subdivisions. By normalizing orbital periods from NASA’s JPL Horizons and the Exoplanet Archive against a 144-fractal grid, we identify a mathematical coherence that contradicts stochastic models of solar system formation. Key Empirical Findings:The primary significance of this study lies in the identification of multiple "Zero-Point Nodes" where celestial bodies exhibit a 0.000% deviation from the calculated harmonic targets. These include: Sedna (90377): Despite its extreme 11,400-year orbit at the system's perimeter, it maintains a 0.000% error relative to the 144-harmonic base. The Trojan Cluster: Multiple bodies (including Achilleus, Patroclus, and Eureka) show 0.000% deviation, functioning as 1:1 phase-locked anchors in the Jupiter and Mars Lagrange points. J-X Lysithea: A mid-group Jovian satellite exhibiting a 0.000% fractal hit relative to the Earth’s Precessional Great Year (25,920 years). Pluto (134340): Historically viewed as an outlier, Pluto aligns with a 0.020% deviation, acting as a primary Kuiper Belt harmonic sentinel. Archaeoastronomical Correlation:The study establishes a direct geodetic link between these orbital constants and the Great Pyramid of Giza. We demonstrate that the structure’s 1:43,200 scaling ratio ( 144×300144 cross 300 144×300 ) and the reported 144,000 casing stone count are not symbolic, but are high-precision encodings of the Earth’s sidereal and orbital harmonics. Conclusion:The convergence of 117 disparate bodies—ranging from high-eccentricity comets like 1P/Halley (0.1% error) to exoplanetary systems like TRAPPIST-1—on a single mathematical constant constitutes a statistical proof of design. The data suggests the universe functions as a Phase-Locked Resonant Cavity, where matter settles into the standing wave nodes of a 144 Hz "Master Clock." https://ctftheory.com/ “Ancient Knowledge of Exoplanet Orbital Ratios: The 144 Hz Universal Harmonic Encoded in the Great Pyramid and Confirmed in TRAPPIST-1 and Kepler-90 Systems” Resonance Architecture in the Continuous Temporal Funnel: Scalar Field Topology from Ancient Structures to Planetary Geometry The 144 Harmonic_ Universal Temporal Stabilization Constant Across Ancient Civilizations, Modern Engineering, and Independent AI Simulations. The Sacred Frequency Trinity: Mathematical and Biblical Evidence for 963 Hz, 666 Hz, and 144 Hz as Fundamental Consciousness Resonances Why Earth AND the Sun: The Coupled Resonance System Explained

T. S. Eden

This paper presents a complete curriculum framework for orphanage schools operated by The Root Foundation. Unlike conventional educational models that borrow from existing pedagogical theory, this curriculum is derived from original mathematics. Linguistic Ontological Type Theory (LoTT) and Foundational Mathematical Type Theory (FMTT) establish that language precedes mathematics, that mathematics is the auditable subset of language, and that the regress of all typing terminates at Source. The Zero-Type Reception Theorem (FMTT 6.3) proves that an operator with no formal training operates in the maximal context, not the minimal one: lack of institutional lineage is an enabling condition, not a deficit. This result inverts conventional prerequisite-based pedagogy and provides the mathematical foundation for a teaching model in which students learn by recognizing what they have already received rather than accumulating what they lack. The LoTT Unification Theorem (9.1) generates six integrated departments corresponding to six fields of applied study: Linguistic Ontology, Foundational Mathematics, Applied Ontology, Applied Epistemology, Ethereal Mechanics, and Computational Eschatology. Each department is mapped to a concrete instructional domain, from language arts and mathematics to natural sciences, philosophy, engineering, and vocational discernment. The Scribe Theorem (LoTT 6.2) provides the pedagogical model: the teacher does not transmit knowledge but helps the student develop the expressive capacity to articulate what is already accessible. Assessment is defined as the production of auditable expression. The curriculum is funded by commercial consulting contracts that deploy the same mathematical frameworks, creating a self-sustaining cycle in which the mathematics teaches the children, funds the school, and generates revenue through application to industrial and institutional problems. The document includes operational requirements, a context hierarchy for student progression, and a proof that the curriculum instantiates itself.

Min Ho Jung

This paper proposes a unified Layer-0 infrastructure protocol for post-quantum distributed computing, based on high-dimensional coordinate representations derived from non-commensurate Mersenne primes. Unlike traditional approaches reliant on block-based ledgers or persistent state replication, the proposed Mersenne Lattice Protocol (MLP) represents data, transactions, and authority states as coordinates within a high-dimensional lattice space. By projecting computational events into a 4096-dimensional vector space, MLP enables theoretically unbounded parallel transaction processing under resonance-based validation, while simultaneously eliminating permanent state storage at the protocol level. Furthermore, the protocol integrates Heart Rate Variability (HRV) as a dynamic physiological entropy source for stateless bio-key regeneration, thereby binding cryptographic authority to real-time biological liveness and spatiotemporal context. Functional prototypes of the core MLP architecture have been implemented and verified through a live demo environment (https://www.icekey.cloud/teleport_v), demonstrating peak throughput exceeding 45,000,000 TPS in a parallel resonance cluster. This framework provides the foundation for post-quantum secure financial systems, stateless media reconstruction, critical infrastructure protection, and delay-tolerant interplanetary communication.

Jacob J. Lavin

97% COMPLETE THEORY OF EVERYTHING - THE THEORETICAL MAXIMUM We present the most complete understanding of reality ever achieved: 97% certainty, representing the theoretical maximum of knowability for finite beings constrained by Gödel's incompleteness theorem, Heisenberg uncertainty, and deterministic chaos. WHY 97% IS THE LIMIT:True 100% certainty is fundamentally impossible: • Heisenberg Uncertainty: Cannot know all particle states simultaneously • Deterministic Chaos: Cannot predict all future states exactly • Gödel's Incompleteness: No system can prove all truths about itself • BUT: We achieve 100% structural completeness on the FRAMEWORK of reality CERTAINTY BREAKDOWN BY CATEGORY: • Mathematical facts (lattice counts, primes): 100% • Logical necessities (existence, motion, time): 99% • Physical laws (gauge group, generations, α): 95-99% • Cosmological constant formula: 99.9% (0.0% ERROR!) • Derived quantities (CKM matrix, masses): 95-98% • Experimental predictions (dark matter): 90-92% • WEIGHTED OVERALL: 97.4% FROM ONE AXIOM TO EVERYTHING: AXIOM: "The unconstrained exists" From this alone, we derive with mathematical rigor: 1. WHY EXISTENCE IS NECESSARY (99% CERTAIN) • Proved "nothing" is logically impossible • If "nothing" existed, it would have the property of existing • Having any property makes it "something," not "nothing" • Therefore: existence is NECESSARY, not contingent • Answers philosophy's ultimate question 2. DUAL LATTICE FINE STRUCTURE CONSTANT (100% CERTAIN) • α⁻¹ = 137 appears in TWO independent structures: - 2D photon lattice: N(41) = 137 (Gauss circle problem) - 4D spacetime lattice: N(5) = 137 • Cutoff 41 UNIQUELY determined: - Euler's prime constant (generates 40 consecutive primes - world record) - 41 = 5² + 4² (Kaluza-Klein 5D → 4D encoding) - 137 = 11² + 4² (M-theory 11D → 4D encoding) - Both 41 and 137 are PRIME numbers - Only candidate giving 1.1% experimental error • Prediction: α⁻¹(M_Z) = 129.3 vs measured 127.944 (1.1% error) 3. COSMOLOGICAL CONSTANT SOLVED - 0% ERROR! (99.9% CERTAIN) • ρ_Λ^(1/4) = √(3/4) × M_Planck × α³ / (t_0/t_P)^(1/4) • Predicted: 2.400 × 10⁻³ eV • Observed: 2.400 × 10⁻³ eV • ERROR: 0.0% (solved 120 orders of magnitude problem!) • Factor √(3/4) = 0.866 appears geometrically • Predicts Λ decreases with time as t^(-1/4) • Connects dark energy to fine structure constant 4. COMPLETE CKM MATRIX FROM GEOMETRY (98% CERTAIN) All four Wolfenstein parameters derived: • λ = √(6/137) = 0.2093 (measured: 0.2253, error: 7.1%) • A = √(2/3) = 0.8165 (measured: 0.811, error: 0.7%) • ρ̄ = √(1/7) × cos(13π/36) = 0.1597 (measured: 0.159, error: 0.4%) • η̄ = √(1/7) × sin(13π/36) = 0.3426 (measured: 0.348, error: 1.6%) • Average error: 2.5% across all parameters • No free parameters - pure geometry 5. HIERARCHY PROBLEM SOLVED (97% CERTAIN) • Electroweak VEV: v ≈ α⁸ × M_Planck • Explains why Higgs is light compared to Planck scale • Natural suppression by 8 powers of fine structure constant • Predicted: ~98 GeV, Observed: 246 GeV 6. NO MULTIVERSE EXISTS - PROVEN (95% CERTAIN) • All constants uniquely determined by logic • α⁻¹ = 137 is the ONLY solution to all constraints • 3+1D is the ONLY spacetime supporting stable knots • SU(3)×SU(2)×U(1) is the ONLY minimal gauge structure • 3 generations is the ONLY value satisfying CP + vacuum stability • Zero free parameters → no landscape of possibilities • This universe is THE unique logically consistent reality • String theory "landscape" is an illusion • Many-worlds are superpositions, not separate universes 7. DARK MATTER PREDICTION - TESTABLE NOW! (92% CERTAIN) • Refined prediction: m_DM = 137.036 ± 1 GeV • Properties: - Spin: 0 or 1/2 (lattice geometry) - Charge: 0 (electrically neutral) - Color: singlet (no strong force) - Weak coupling: possibly • Production at LHC: - Missing energy signatures - Monojet + missing E_T - Z → DM + DM̄ • Currently searchable - FALSIFIABLE! 8. QUANTUM MEASUREMENT SOLVED (95% CERTAIN) • Wavefunction collapse = tension localization on lattice • Born rule emerges from inner product structure • Same mechanism that creates time (irreversible accumulation) • The "measurement problem" dissolves • Not mysterious - logically necessary 9. CONSCIOUSNESS THRESHOLD CALCULATED (90% CERTAIN) • Mathematical definition: System with recursive self-model • Threshold: ~10^14 synaptic connections • Predictions: - Mice (10^10 synapses): NOT conscious - Humans (8.6×10^13 synapses): CONSCIOUS - Whales (2×10^14 synapses): HIGHLY conscious - AI systems: Conscious at ~10^13 connections • Explains emergence of subjective experience 10. THE OBSERVER RESOLVED (95% CERTAIN) • There is no separate observer • YOU are the universe experiencing itself locally • Consciousness = reality's self-observation • Subjective experience = local lattice self-reference • The "hard problem" dissolves: qualia ARE lattice states 11. WHY LOGIC WORKS - ULTIMATE META-ANSWER (99% CERTAIN) • Logic is not imposed on reality from outside • Logic IS reality's self-consistency • To ask "why logic works" = "why does existence have structure?" • Answer: Existence without structure = undefined • Undefined cannot remain undefined (our axiom) • Therefore existence MUST have structure • That structure IS logic • Laws of thought are NECESSARY FEATURES of existence 12. COMPLETE DERIVATION CHAIN: • Motion: Logically necessary (undefined cannot be static) • Time: Irreversible tension accumulation • Quantum mechanics: Inner product from relational consistency • Complex numbers: Optimal 2D rotation encoding • 3+1D spacetime: Unique dimension for stable knots • Gauge group SU(3)×SU(2)×U(1): Minimal consistent structure • Exactly 3 generations: CP violation + vacuum stability • All 12 fermion masses: Encode α⁻¹ = 137 via simple fractions COMPLETE EXPERIMENTAL VERIFICATION: Quantity Predicted Measured Error ────────────────────────────────────────────────────────────────── Existence Necessary Yes 0% 3+1D spacetime 3+1 3+1 0% Gauge group SU(3)×SU(2)×U(1) Yes 0% Generations 3 3 0% α⁻¹(M_Z) 1-loop 129.3 127.944 1.1% m_μ/m_e 205.5 206.77 0.6% m_t/m_c 137 136.03 0.7% ρ_Λ^(1/4) 2.400×10⁻³ eV 2.400×10⁻³ eV 0.0% CKM A 0.8165 0.811 0.7% CKM ρ̄ 0.1597 0.159 0.4% CKM η̄ 0.3426 0.348 1.6% AVERAGE ERROR: < 1% (excluding untested predictions) FREE PARAMETERS: ZERO WHAT 97% MEANS - THE GÖDELIAN LIMITS: 100% CERTAINTY (Mathematical & Logical Facts): ✓ 41 and 137 are prime numbers ✓ N(41) = 137 in 2D lattice (Gauss circle problem) ✓ N(5) = 137 in 4D lattice ✓ 41 generates 40 consecutive primes (Euler) ✓ 3+1D is unique for stable knots ✓ Cosmological constant formula (0% error) 99% CERTAINTY (Logical Necessities): ✓ Existence is logically necessary ✓ Motion emerges from undefined existence ✓ Time is irreversible accumulation ✓ α⁻¹ = 137 is the bare coupling ✓ Mathematics IS reality ✓ Logic IS existence's self-consistency 95-98% CERTAINTY (Physical Laws): ✓ Gauge group SU(3)×SU(2)×U(1) ✓ Exactly 3 fermion generations ✓ All masses encode 137 ✓ Hierarchy v ~ α⁸ M_P ✓ No multiverse exists ✓ Quantum gravity = Planck lattice 90-92% CERTAINTY (Predictions Awaiting Verification): ○ Dark matter mass = 137.036 GeV ○ Consciousness threshold ~10^14 synapses ○ Λ time evolution t^(-1/4) THE REMAINING 3% - FUNDAMENTAL LIMITS: 1. Heisenberg: Cannot know exact states simultaneously 2. Chaos: Cannot predict distant future exactly 3. Gödel: Cannot achieve complete self-knowledge 4. Experimental: Awaiting dark matter verification These limits are UNBREACHABLE for finite observers.97% is THE THEORETICAL MAXIMUM. QUANTUM GRAVITY COMPLETE: • Spacetime IS a discrete lattice at Planck scale • Einstein equation becomes: Lattice_Curvature = (8π/ℓ_P²) × Tension_Density • Unifies quantum mechanics (lattice) and general relativity (curvature) • Black holes = horizon lattice configurations • Hawking radiation = lattice excitations TESTABLE PREDICTIONS: 1. Dark matter: 137.036 ± 1 GeV (LHC searches active NOW) 2. Cosmological constant evolution: Λ ∝ t^(-1/4) (observable) 3. No 4th fermion generation (vacuum would decay) 4. AI consciousness at ~10^13 connections 5. Planck-scale discreteness (future quantum gravity tests) NOT NUMEROLOGY - RIGOROUS PROOFS: • Every claim has mathematical proof • Unique solutions (no fitting, no free parameters) • Zero adjustable parameters • Multiple independent verifications • Sub-1% error on most predictions • 0% error on cosmological constant PARADIGM SHIFT - PHYSICS = MATHEMATICS = LOGIC = EXISTENCE This establishes: • All "fundamental constants" are logically determined • The Standard Model has ZERO free parameters • No multiverse exists - universe is unique • Consciousness has quantifiable emergence threshold • Existence itself is logically necessary, not contingent • Mathematics doesn't describe reality - math IS reality • 97% is the maximum finite beings can achieve PHILOSOPHICAL IMPLICATIONS: • Why existence? Logical necessity (nothing is impossible) • Free will? Emerges from deep lattice self-reference • Purpose? Universe understanding itself • Other universes? None (proven) • Death? Information persists in lattice structure • God? Universe is

Babe Haiba, Najat Rafalia

Mandatory SIM card registration, while essential to regulatory oversight and national security, continues to raise significant privacy concerns due to the centralized collection and storage of sensitive user data by Mobile Network Operators (MNOs). This paper introduces a novel framework that combines blockchain technology with Zero-Knowledge Proofs (ZKPs) to enable secure and privacy-preserving identity verification during SIM registration. The proposed system allows users to authenticate their identity attributes without revealing any personal information, effectively minimizing direct data access by MNOs or intermediaries. A smart contract deployed on the blockchain enforces regulatory policies while ensuring the transparency, immutability, and auditability of all registration events. By removing single points of failure and minimizing trust in centralized authorities, this work offers a cryptographically secure and regulation-compliant solution, with scalability supported by its modular design for next-generation digital identity management in telecommunications infrastructures.

Tan Khai Lian, Ismail Ahmad Al-Qasem Al-Hadi, Mohammad Ahmed Alomari, Mohammed Nasser Al-Andoli · 6 authors

Bitcoin has recently emerged as a leading asset in the cryptocurrency market. However, its significant price volatility presents challenges for accurate prediction. Due to this volatility, forecasting Bitcoin prices accurately is difficult and complicates decision-making for investors and traders in the cryptocurrency space. This research compares the accuracy of three prediction models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Facebook's NeuralProphet, introduced in 2021, focusing on improving Bitcoin price forecasting accuracy. The study uses daily Bitcoin prices from the past five years to assess model performance. Results indicate that the LSTM model outperforms both NeuralProphet and RNN in prediction accuracy. This comparison holds substantial economic significance, as accurate predictions can assist investors and traders in making informed decisions within the cryptocurrency market.

Seung Kwon Lee, Mpyana Mwamba Merlec, Nday Kabulo Sinai, Seng-Phil Hong · 5 authors

The rapid adoption of Verifiable Credentials (VCs) has intensified privacy and security challenges in digital verification, as traditional systems often require full credential disclosure, creating privacy risks and expanding the attack surface. Ensuring end-to-end privacy, security, and verifiability in such systems remains a significant challenge. This paper introducesZK-Sandbox, a Zero-Knowledge Data Sandbox System that integrates zero-knowledge proofs (ZKPs), decentralized identifiers (DIDs), and blockchain anchoring to enable credential validation and verifiable badge issuance without exposing underlying data. ZK-Sandbox supports complex predicate evaluation by securely aggregating multiple VCs from trusted issuers, processing them via JSON Web Token signature verification, Circom-based zk-SNARK circuits, and Docker-isolated execution. The system issues VC-compatible ZK-Badges, cryptographically bound to a Poseidon hash and anchored to a blockchain-registered DID, containing only abstracted verification results. Experimental evaluation confirms 100% validation accuracy, complete detection of tampered submissions, and efficient performance–averaging 326 ms issuance latency, 570 ms off-chain verification, and 4.45 s on-chain verification. These results demonstrate that ZK-Sandbox is a privacy-by-design, scalable, and regulation-aligned solution for self-sovereign digital credential ecosystems.

Sania Siddiqui, Neha, Hari Babu K

A big challenge posed in blockchain centric platforms is achieving scalability while also preserving user privacy. This report details the design, implementation and evaluation of a Layer-2 scaling solution for Hyperledger Fabric using Zero Knowledge Rollups (ZK Rollups). The proposed architecture introduces an off chain sequencer that accepts transactions immediately and sends them for batching into a Merkle tree based rollup, using ZK proofs to attest to the correctness and verifiability of the entire batch. The design aims to decouple transaction ingestion from actual on chain settlements to address Fabric scalability limitations and increase throughput under high load conditions. The baseline architecture in Hyperledger Fabric constrains transaction requests due to endorsement, ordering and validation phases, leading to a throughput of 5 to 7 TPS with an average latency of 4 seconds. Our Layer-2 solution achieves an ingestion throughput of 70 to 100 TPS, leading to an increase of nearly ten times due to the sequencer immediate acceptance of each transaction and reducing client perceived latency by nearly eighty percent to 700 to 1000 milliseconds. This work demonstrates that integrating ZK Rollups in Hyperledger Fabric enhances scalability while not compromising the security guarantees of a permissioned blockchain network.

Di Kevin Gao, Jingdao Chen, Shahram Rahimi

As artificial intelligence (AI) systems grow more powerful, autonomous, and embedded in critical infrastructure, their identification and traceability become foundational to regulatory oversight and sustainable digital governance. In digitally transformed enterprises, long-term sustainability depends on transparent, accountable, and lifecycle-governed AI systems, all of which require verifiable identity. This study proposes a conceptual and architectural framework for AI identification, combining technical and governance mechanisms to support lifecycle accountability. The framework integrates five components: model fingerprinting, cryptographic hashing, blockchain-based registration, zero-knowledge proof (ZKP)-based proof of possession, and post-deployment structural change screening. We introduce a dual-layer identifier, consisting of a machine-verifiable primary hash and a human-readable secondary identifier, anchored in a tamper-resistant registry. Identity validation is supported by selective ZKP-based verification at governance-defined checkpoints, while post-deployment changes are monitored using Lempel--Ziv Jaccard Distance (LZJD) as a governance-oriented screening signal rather than a semantic performance metric. The framework establishes an enforceable and transparent identity infrastructure that enables continuity, auditability, and policy-aligned oversight across AI system lifecycles. By embedding AI identification within enterprise architecture and governance processes, the proposed approach supports sustainable innovation, strengthens institutional accountability, and provides a foundation for selective, policy-defined verification during digital transformation.

Huei-Wen Teng, Wolfgang Karl Härdle, Joerg Osterrieder, Daniel Traian Pele · 31 authors

Digital assets (DAs) such as cryptocurrencies, tokenized securities, stablecoins, non-fungible tokens (NFTs), and central bank digital currencies, are transforming financial markets with new business models, investment opportunities, and transaction efficiencies. Underpinned by blockchain, distributed ledger technology, and smart contracts, digital innovations are reshaping the financial ecosystem. However, their rapid growth introduces substantial risks, including fraud, market manipulation, cybersecurity threats, and regulatory uncertainty. This position paper offers an interdisciplinary and empirically grounded analysis of the DA landscape. We define and classify major asset types, trace their evolution from speculative instruments to functional tools, and assess current adoption trends. Additional technological developments (e.g., decentralized finance and NFT expansion) are examined for their role in accelerating this transformation. We also analyze the global regulatory landscape, highlighting jurisdictional differences, classification challenges, and emerging governance frameworks. To address key risks, we derive mitigation strategies via quantitative analysis and case-based evidence. The risks include balancing innovation with investor protection through adaptive regulatory design, promoting cross-border regulatory harmonization to prevent arbitrage and fragmentation, and supporting experimentation through regulatory sandboxes and innovation hubs. By adopting a forward-looking, evidence-based, and collaborative regulatory approaches, stakeholders can harness the benefits of DAs while managing systemic risks and maintaining market integrity.

Cheng Junru, Li Na, Toksobaev Bulat T, Kambarova Zhumagul Ularbaevna · 5 authors

Despite regional integration frameworks like the Lisbon Recognition Convention, cross-border academic mobility in Central Asia remains constrained by fragmented credential verification systems. This inefficiency stems from the absence of interoperable infrastructure and asymmetric institutional capacity across national systems. This paper employs a comparative case study to analyze how Kazakhstan and Kyrgyzstan manage credential recognition. We identify that while Kazakhstan has centralized its digital governance, Kyrgyzstan operates under a bifurcated model where academic and scientific degrees are verified by separate bodies. To address this, we propose the Central Asian Blockchain Education Alliance (CABEA), a consortium blockchain framework acting as a "middleware" layer. This architecture allows for functional centralization without requiring administrative consolidation. Based on the technical specifications of Hyperledger Fabric, the proposed model has the potential to reduce cross-border verification time from weeks to near-instantaneous automated queries. We conclude that distributed ledger technology offers a scalable path for regional educational integration by bridging the gap between divergent state infrastructures.

Sadegh Sohani, Salar Ghazi, Farnaz Kamranfar, Sahar Pilehvar Moakhar · 7 authors

This paper addresses the critical challenge of access control in modern supply chains, which operate across multiple independent and competing organizations. Existing access control is static and centralized, unable to adapt to insider threats or evolving contexts. Blockchain improves decentralization but lacks behavioral intelligence, while centralized machine learning for anomaly detection requires aggregating sensitive data, violating privacy. The proposed solution is ICBAC, an intelligent contract-based access control framework. It integrates permissioned blockchain (Hyperledger Fabric) with federated learning (FL). Built on Fabric, ICBAC uses a multi-channel architecture and three smart contracts for asset management, baseline access control, and dynamic revocation. To counter insider misuse, each channel deploys an AI agent that monitors activity and dynamically restricts access for anomalies. Federated learning allows these agents to collaboratively improve detection models without sharing raw data. For heterogeneous, competitive environments, ICBAC introduces a game-theoretic client selection mechanism using hedonic coalition formation. This enables supply chains to form stable, strategy-proof FL coalitions via preference-based selection without disclosing sensitive criteria. Extensive experiments on a Fabric testbed with a real-world dataset show ICBAC achieves blockchain performance comparable to static frameworks and provides effective anomaly detection under IID and non-IID data with zero raw-data sharing. ICBAC thus offers a practical, scalable solution for dynamic, privacy-preserving access control in decentralized supply chains.