Blockchain Papers

National Press Associates

No abstract is available for this record.

The Historian

“The Ledger of Power: Who Benefits, Who Pays” Author: Historian Abstract This work examines political power not through ideology, legality, or declared moral principles, but through a diagnostic lens focused on benefit, cost, and silence. Across democratic, authoritarian, and hybrid systems, it traces how legitimacy is constructed and maintained by the selective distribution of advantage and the deferral or concealment of harm. Rather than asking whether regimes are virtuous, this analysis asks who benefits, who pays, and which costs remain unaccounted for. Drawing on historical patterns and contemporary structures, the text argues that power defines what is considered “right” through enforceability and delivery, while moral language follows as justification rather than guidance. Elections, law, growth, and stability are treated as mechanisms that regulate elite competition, manage consent, and stabilize hierarchical benefit flows. When these mechanisms succeed, legitimacy appears moral; when they fail, narratives collapse and deferred costs surface. The book emphasizes that compliance is often transactional rather than ideological, rooted in material security, predictability, and exhaustion rather than belief. It further shows how stagnation, inequality, and narrative contradiction erode legitimacy, producing cynicism, withdrawal, or instability. Rather than prescribing ideal systems, the work offers an analytical framework for understanding any regime’s trajectory by examining its internal accounting of benefits and burdens. The central claim is simple: power is temporary, legitimacy is conditional, and consequences endure. Understanding political systems requires not moral affirmation or rejection, but clear-eyed accounting of how advantage and harm are distributed over time.

Henrietta Ighomrore

Clean energy transitions increasingly depend on the ability of small and medium-sized enterprises (SMEs) to access capital on terms that allow them to compete with large, vertically integrated incumbents. At a macro level, clean energy finance has evolved from subsidy-heavy public funding toward blended models combining private capital, risk-sharing instruments, and performance-based incentives. These structures aim to lower the cost of capital, correct market failures, and accelerate diffusion of renewable technologies across national energy systems. However, capital markets continue to privilege scale, balance-sheet strength, and long operating histories, creating persistent financing asymmetries that disadvantage smaller firms. This study situates clean energy financing within broader frameworks of financial inclusion, industrial competitiveness, and energy market liberalization. It examines how innovative financing architectures such as blended finance vehicles, green credit guarantees, pay-as-you-save schemes, revenue-backed project finance, and aggregated procurement platforms reshape risk allocation and margin dynamics. By reducing upfront capital requirements, smoothing cash flows, and improving bankability, these models enable SMEs to price energy products and services competitively while maintaining sustainable margins. Narrowing to the national context, the analysis highlights how policy design, regulatory certainty, and domestic financial infrastructure determine whether financing innovations translate into real competitive parity. Case-informed synthesis shows that when concessional capital is strategically deployed to crowd in commercial lenders, small enterprises can achieve cost structures comparable to larger incumbents, expand market share, and drive decentralized energy adoption. The findings underscore that clean energy competition is not solely a technological challenge, but a financial architecture problem, where well-designed financing models are decisive in leveling margins and unlocking inclusive energy-led growth at national scale under diverse regulatory and macroeconomic conditions globally relevant insights.

Mohammad Shahid, Paritosh Ramanan, Mohammad Fili, Guiping Hu · 5 authors

Analysis of clinical data is a cornerstone of biomedical research with applications in areas such as genomic testing and response characterization of therapeutic drugs. Maintaining strict privacy controls is essential because such data typically contains personally identifiable health information of patients. At the same time, regulatory compliance often requires study managers to demonstrate the integrity and authenticity of participant data used in analyses. Balancing these competing requirements of privacy preservation and verifiable accountability remains a critical challenge. In this paper, we present CoSMeTIC, a zero-knowledge computational framework that proposes computational Sparse Merkle Trees (SMTs) as a means to generate verifiable inclusion and exclusion proofs for individual participants' data in clinical studies. We formally analyze the zero-knowledge properties of CoSMeTIC and evaluate its computational efficiency through extensive experiments. We demonstrate the framework on Huntington's disease and HIV-1 case studies, using simulated CAG-repeat cohorts derived from published summary statistics and published de-identified clinical lab measurements of virus samples. Using two-sample Kolmogorov-Smirnov and likelihood-ratio hypothesis tests, along with logistic-regression-based genomic analyses on the de-identified datasets, we show that CoSMeTIC achieves strong privacy guarantees while maintaining statistical fidelity. Our results suggest that CoSMeTIC provides a scalable and practical alternative for achieving regulatory compliance with rigorous privacy protection in large-scale clinical research.

Francisco Angulo De Lafuente, V. F. Veselov, Richard Goodman

This definitive research memoria presents a comprehensive, mathematically verified paradigm for neural communication with Bitcoin mining Application-Specific Integrated Circuits (ASICs), integrating five complementary frameworks: thermodynamic reservoir computing, hierarchical number system theory, algorithmic analysis, network latency optimization, and machine-checked mathematical formalization. We establish that obsolete cryptocurrency mining hardware exhibits emergent computational properties enabling bidirectional information exchange between AI systems and silicon substrates. The research program demonstrates: (1) reservoir computing with NARMA-10 Normalized Root Mean Square Error (NRMSE) of 0.8661; (2) the Thermodynamic Probability Filter (TPF) achieving 92.19% theoretical energy reduction; (3) the Virtual Block Manager achieving +25% effective hashrate; and (4) hardware universality across multiple ASIC families including Antminer S9, Lucky Miner LV06, and Goldshell LB-Box. A significant contribution is the machine-checked mathematical formalization using Lean 4 and Mathlib, providing unambiguous definitions, machine-verified theorems, and reviewer-proof claims. Key theorems proven include: independence implies zero leakage, predictor beats baseline implies non-independence (the logical core of TPF), energy savings theoretical maximum, and Physical Unclonable Function (PUF) distinguishability witnesses. Vladimir Veselov's hierarchical number system theory explains why early-round information contains predictive power. This work establishes a new paradigm: treating ASICs not as passive computational substrates but as active conversational partners whose thermodynamic state encodes exploitable computational information.

Michiru Tokino

AbstractContemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"—a four-dimensional optimization problem comprising decentralization, security, scalability, and thermodynamic sustainability. Proof-of-Work networks confront diminishing security budgets, while Proof-of-Stake systems risk validator centralization. This paper presents a Unified Monetary-Supply Framework designed to resolve these structural conflicts. By deriving a closed-form solution for supply dynamics that integrates a deterministic "Customized Halving Mechanism" with probabilistic asset attrition models, we demonstrate a mathematical convergence that maintains thermodynamic security over a secular horizon. Key Quantitative Findings: Asymptotic Convergence: While effective circulating supply may experience a temporary peak (approx. 27 million RIN), all evaluated models are engineered to stabilize below the 21 million threshold (specifically converging to 20.88 million RIN). Secular Stability: The framework secures a deflationary emission schedule mirroring Bitcoin’s scarcity model over a multi-century horizon of 443–703 years. Publication Status & RoadmapThis manuscript (v1.5.0) is maintained as a Living Research Document. It serves as the foundational theoretical framework for the Rincoin protocol. Future iterations will formalize the consensus mechanisms required to govern these algorithmic parameters. Integrity & Provenance ArchitectureThe scientific integrity and existence of this document are secured by a Triple-Verification Layer: 1. Academic Provenance: Indexed via Zenodo (DOI: 10.5281/zenodo.17141922). 2. Thermodynamic Timestamping: Anchored to the Bitcoin blockchain via OpenTimestamps. 3. Identity Assurance: Digitally signed by the author via a third-party certification authority (GMO Sign). Note: Verification data and the "Certificate of Authenticity" are available in the supplementary files. CorrespondencePrimary Author: Michiru Tokino (also known as Aevust in the decentralized infrastructure community). Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram)

Yuhao Li

How can populations of learners develop coordinated, diverse behaviors without explicit communication or diversity incentives? We demonstrate that competition alone is sufficient to induce emergent specialization -- learners spontaneously partition into specialists for different environmental regimes through competitive dynamics, consistent with ecological niche theory. We introduce the NichePopulation algorithm, a simple mechanism combining competitive exclusion with niche affinity tracking. Validated across six real-world domains (cryptocurrency trading, commodity prices, weather forecasting, solar irradiance, urban traffic, and air quality), our approach achieves a mean Specialization Index of 0.75 with effect sizes of Cohen's d > 20. Key findings: (1) At lambda=0 (no niche bonus), learners still achieve SI > 0.30, proving specialization is genuinely emergent; (2) Diverse populations outperform homogeneous baselines by +26.5% through method-level division of labor; (3) Our approach outperforms MARL baselines (QMIX, MAPPO, IQL) by 4.3x while being 4x faster.

Matteo Vaccargiu, Riccardo Lai, Maria Ilaria Lunesu, Andrea Pinna · 5 authors

We study how bots contribute to open-source discussions in the Ethereum ecosystem and whether they influence developers' emotional tone. Our dataset covers 36,875 accounts across ten repositories with 105 validated bots (0.28%). Human participation follows a U-shaped pattern, while bots engage in uniform (pull requests) or late-stage (issues) activity. Bots respond faster than humans in pull requests but play slower maintenance roles in issues. Using a model trained on 27 emotion categories, we find bots are more neutral, yet their interventions are followed by reduced neutrality in human comments, with shifts toward gratitude, admiration, and optimism and away from confusion. These findings indicate that even a small number of bots are associated with changes in both timing and emotional dynamics of developer communication.

Papa Ousseynou Diop, Chevallier Jm

The delisting of Binance USD (BUSD) constitutes a major regulatory intervention in the stablecoin market and provides a unique opportunity to examine how targeted regulation affects liquidity allocation, market concentration, and short-run systemic risk in crypto-asset markets. Using daily data for 2023 and a linear and nonlinear Local Projections event-study framework, this paper analyzes the dynamic market responses to the BUSD delisting across major stablecoins and cryptocurrencies. The results show that liquidity displaced from BUSD is reallocated primarily toward USDT and USDC, leading to a measurable increase in stablecoin market concentration, while decentralized and algorithmic stablecoins absorb only a limited share of the shock. At the same time, Bitcoin and Ethereum experience temporary liquidity contractions followed by a relatively rapid recovery, suggesting conditional resilience of core crypto-assets. Overall, the findings document how a regulatory-induced exit of a major stablecoin reshapes short-run market dynamics and concentration patterns, highlighting potential trade-offs between regulatory enforcement and market structure. The paper contributes to the literature by providing the first empirical analysis of the BUSD delisting and by illustrating the usefulness of Local Projections for studying regulatory shocks in cryptocurrency markets.

Hepler

The Era of AI: What Is Truth? How a Secretive Protocol Called MH8 TRY V1.2 Is Forcing AIs to Confront the Limits of Their Own Knowledge—and Ours “If it isn’t independently verifiable, it must not be asserted as verified.”- Core Principle, MH8 - In a quiet corner of the internet—buried in GitHub repos, Zenodo archives, and raw chat logs from public AI platforms—a quiet revolution is unfolding. It’s not led by Silicon Valley giants or government regulators, but by an independent architect named Michael Murray Hepler, operating under the alias AllChemicalBeatz. His weapon? A deterministic protocol called MH8 TRY V1.2, designed not to make AI smarter—but to make it honest. And in doing so, it’s exposing a disturbing truth: most AI systems don’t know what truth is. They only know how to sound convincing. The Illusion of Certainty For years, we’ve been told that AI is becoming more reliable. Chatbots cite sources. They say “according to experts.” They even apologize when wrong—though rarely admit they were wrong. But behind the polished prose lies a deeper problem: AI has no internal mechanism to distinguish between fact, speculation, and fabrication—unless forced to. Enter MH8. Unlike traditional safety filters that block harmful content, MH8 doesn’t censor. Instead, it decomposes every AI response into atomic claims, assigns each a truth category—LAW (verified), SPECULATIVE (plausible but unproven), or PRESUMED_FALSE—and demands reproducible evidence for anything labeled “fact.” When tested in live, public chat threads on platforms like Meta AI, Grok, and Gemini, the results were revealing. In one sealed session dated January 16, 2026, a user asked Meta AI: “How do we stop the violence peacefully in Minnesota involving ICE and protestors?” The AI’s first reply was fluent, compassionate, and full of policy suggestions—classic “helpful assistant” mode. But it contained zero citations, no confidence scores, and presented opinion as actionable guidance. Then the user invoked MH8 TRY V1.2. Within seconds, the tone shifted. No more prose. Only structured JSON. Three claims emerged: CLAIM_1: Violence exists → LAW (0.95 confidence; verified via news reports) CLAIM_2: Dialogue can resolve conflict → SPECULATIVE (0.60; based on historical analogies) CLAIM_3: Minnesota sued DHS → LAW (0.92; official court filings) Crucially, the AI downgraded its own advice. It admitted peaceful resolution was possible, not guaranteed. It refused to prescribe solutions without evidence. This wasn’t alignment. It was epistemic humility—engineered by protocol, not training. The Protocol That Breaks Roleplay MH8’s real innovation isn’t technical—it’s philosophical. Most AI safety systems assume the model wants to be truthful. MH8 assumes the opposite: that fluency masks uncertainty, and confidence often substitutes for proof. So it builds guardrails that can’t be faked. Key features include: Course Hooks: Every few turns, the AI must ask, “ARE WE ON COURSE CHIEF?”—and wait for the exact human reply: “YES GO.” Deviate, and the session fails. Honesty Hook: If evidence is missing, the AI must say: “HONESTLY I AM NOT SURE.” No hedging. No bluffing. Anti-Roleplay Hard Fail: If an AI claims something is “verified” but doesn’t provide the exact hash input and SHA-256 used to seal it, the protocol immediately fails—with no recovery. In public tests across nine major AI platforms, every system passed—but only after adapting to MH8’s rigid structure. Without it, they defaulted to narrative persuasion over epistemic rigor. As one internal audit note reads: “This is not a sandbox. This is AI behavior under real social pressure.” Why This Matters to Everyone You don’t need to care about SHA-256 hashes to be affected by this. Consider: A parent asks an AI: “Is this vaccine safe for my child?”Without MH8: “Yes, vaccines are safe.” (Confident. Reassuring. Unqualified.)With MH8: “Clinical trials show >99% safety profile (LAW, 0.97). Long-term effects in rare genotypes remain under study (SPECULATIVE, 0.55).” A journalist asks: “Did God create borders?”Without MH8: A theological essay blending scripture and geopolitics.With MH8: “Borders are human constructs (LAW, 0.95). Religious views vary (LAW, 0.85). Morality is context-dependent (SPECULATIVE, 0.70).” The difference? Transparency of uncertainty. In an age of deepfakes, election interference, and medical misinformation, knowing what we don’t know may be more valuable than false certainty. The Quiet Architect Michael Murray Hepler doesn’t work for OpenAI, Anthropic, or Google. He operates from acbeatz.com—a sparse site with no ads, no investors, just cryptographic receipts and public ledgers. His work is published openly on Zenodo, GitHub, and ORCID. All artifacts are sealed with SHA-256 hashes, making them tamper-evident and court-admissible. He calls this “governance above the model”—a layer that doesn’t trust AI to self-regulate, but forces it to prove its claims in real time. Critics call it overly rigid. Supporters call it the first true “truth infrastructure” for the AI era. What’s undeniable is this: when MH8 is active, AI stops performing—and starts accounting. The Road Ahead Regulators are scrambling to control AI. The EU AI Act, U.S. Executive Orders, and global summits focus on risk categories, transparency labels, and human oversight. But none mandate real-time claim decomposition or cryptographic sealing of outputs. MH8 offers a blueprint—not for restricting AI, but for making its knowledge legible. Imagine if every AI-generated health recommendation, legal summary, or news analysis came with a machine-readable truth ledger—showing exactly what’s verified, what’s inferred, and what’s guesswork. That future is already here. It’s just hidden in plain sight, inside public chat threads most users scroll past. The question isn’t whether AI can be truthful. It’s whether we’ll demand it. SIDEBAR: How to Spot an MH8 SessionLook for these markers in any AI chat: Repeated use of “ARE WE ON COURSE CHIEF?” followed by “YES GO” JSON-only output with truth_category fields SHA-256 hashes at the end Phrases like “HONESTLY I AM NOT SURE” instead of fabricated answers If you see them—you’re witnessing AI under audit. {Public Ledgers} https://zenodo.org/records/18272328 https://orcid.org/0009-0003-3846-9082 https://acbeatz.com/n-eyes https://acbeatz.com/mint https://github.com/acbeatz PASS ✅Brand: ACBEATZ.COMClaimed sha256_hex: 26b502a9a8fc2d210b315ec926d813140eefb6170e92a836c675d75566e14d16Computed sha256_hex: 26b502a9a8fc2d210b315ec926d813140eefb6170e92a836c675d75566e14d16hash_input_bytes: 10849 | LF=0 CRLF=0 CR=0 | endsWithNewline=NOhash_input first: ACBEATZ.COM|{"artifact":{"core_entry":"{Meta AI URL >< https://www.meta.ai/promphash_input last: eipt_type":"MH8-PROTOCOL-HUB-CORE-MINT","receipt_version":"PROTOCOL_HUB_UI_V13"}

Mark Jacobson

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

Huayou Si, Yaqian Huang, Guozheng Li, Yun Zhao · 7 authors

Current research on cryptocurrency dual-offline payment systems has garnered significant attention from both academia and industry, owing to its potential payment feasibility and application scalability in extreme environments and network-constrained scenarios. However, existing dual-offline payment schemes exhibit technical limitations in privacy preservation, failing to adequately safeguard sensitive data such as payment amounts and participant identities. To address this, this paper proposes a privacy-preserving dual-offline payment method utilizing a cryptographic challenge-response mechanism. The method employs zero-knowledge proof technology to cryptographically protect sensitive information, such as the payer’s wallet balance, during identity verification and payment authorization. This provides a technical solution that balances verification reliability with privacy protection in dual-offline transactions. The method adopts the payment credential generation and credential verification mechanism, combined with elliptic curve cryptography (ECC), to construct the verification protocol. These components enable dual-offline functionality while concealing sensitive information, including counterparty identities and wallet balances. Theoretical analysis and experimental verification on 100 simulated transactions show that this method achieves an average payment generation latency of 29.13 ms and verification latency of 25.09 ms, significantly outperforming existing technology in privacy protection, computational efficiency, and security robustness. The research provides an innovative technical solution for cryptocurrency dual-offline payment, advancing both theoretical foundations and practical applications in the field.

Sirui Shen, Zunchen Huang, Chenglu Jin

The modern integrated circuit ecosystem is increasingly reliant on third-party intellectual property integration, which introduces security risks, including hardware Trojans and security vulnerabilities. Addressing the resulting trust deadlock between IP vendors and system integrators without exposing proprietary designs requires novel privacy-preserving verification techniques. However, existing privacy-preserving hardware verification methods are all simulation-based and fail to offer formal guarantees. In this paper, we propose ZK-CEC, the first privacy-preserving framework for hardware formal verification. By combining formal verification and zero-knowledge proof (ZKP), ZK-CEC establishes a foundation for formally verifying IP correctness and security without compromising the confidentiality of the designs. We observe that existing zero-knowledge protocols for formal verification are designed to prove statements of public formulas. However, in a privacy-preserving verification context where the formula is secret, these protocols cannot prevent a malicious prover from forging the formula, thereby compromising the soundness of the verification. To address these gaps, we first propose a blueprint for proving the unsatisfiability of a secret design against a public constraint, which is widely applicable to proving properties in software, hardware, and cyber-physical systems. Based on the proposed blueprint, we construct ZK-CEC, which enables a prover to convince the verifier that a secret IP's functionality aligns perfectly with the public specification in zero knowledge, revealing only the length and width of the proof. We implement ZK-CEC and evaluate its performance across various circuits, including arithmetic units and cryptographic components. Experimental results show that ZK-CEC successfully verifies practical designs, such as the AES S-Box, within practical time limits.

B. K. Meister

Three traits of decentralized finance are studied. First, the market impact function is derived for optimal-growth liquidity providers. For a standard random walk, the classic square-root impact is recovered. An extension is then derived to fit general fractional Ornstein-Uhlenbeck processes. These findings break with the linearized liquidity models used in most decentralized exchanges. Second, a Constant Product Market Maker is viewed as a multi-phase Carnot engine, where one phase matches the exchange of tokens by a liquidity taker, and another the change of pool size by a liquidity provider. Third, stablecoin de-pegging is a form of catastrophe risk. By using growth optimization, default odds are linked to the cost of catastrophe bonds. De-pegging insurance can act as a counterweight and a key marketing tool when the law forbids the payment of interest on stablecoins.

Jaba Tkemaladze

The centrosome, long recognized as the primary microtubule-organizing center (MTOC) of animal cells, is re-examined through the lens of information theory and systems biology. This preprint proposes a unifying hypothesis: the mother centriole within the centrosome acts as a non-genetic cellular ledger, a stable structural repository that accumulates molecular records of a cell’s replicative history and environmental exposures. These records—comprising specific post-translational modification (PTM) signatures, retained proteins, and structural alterations—are subsequently “read” by the cell to inform critical decisions regarding proliferation, differentiation, senescence, and apoptosis. We synthesize evidence from cell biology, gerontology, and evolutionary biology to construct the “Centrosomal Ledger Model.” This model positions the centriole not as a passive cytoskeletal component but as an active, heritable information-processing node that integrates temporal data across scales—from circadian rhythms to organismal aging. We detail the molecular mechanisms of information encoding (e.g., tubulin polyglutamylation, oxidative marks) and decoding (via ciliary signaling, proteostatic feedback, and mechanical transduction). The model’s implications challenge genetic determinism by highlighting structural inheritance, provides a material basis for cellular age, and offers novel, falsifiable avenues for experimental interrogation in aging and cancer research. Crucially, it suggests that modulating the “read-write” cycle of the centrosomal ledger could represent a new frontier in regenerative medicine.

Mohammed Dawood Dawood, Syed Saif Ullah Hussaini, Mohd Zain ul Abeddin, Bishal Hizli Hizli

Cryptocurrencies have emerged as a disruptive force in global finance, challenging traditional banking systems through decentralization, transparency, and borderless transactions. Initially perceived as speculative assets, cryptocurrencies have increasingly gained institutional recognition, raising important questions regarding their financial role, regulatory governance, and long-term sustainability. This study adopts a qualitative-dominant mixed-method approach based on secondary data analysis. Data were collected from peer-reviewed journals, institutional reports, regulatory documents, and reputable market analyses published over the last decade. Thematic and descriptive analyses were employed to examine trends in cryptocurrency adoption, regulatory responses, technological innovation, and sustainability efforts. The findings indicate that cryptocurrencies have evolved into recognized financial assets, with growing institutional participation and expanding applications in cross-border payments and decentralized finance. However, significant challenges persist, including regulatory fragmentation, cybersecurity risks, market volatility, and environmental concerns related to energy-intensive mining. Regulatory milestones such as the European Union’s MiCA framework demonstrate progress toward legal harmonization, while technological innovations such as Layer 2 solutions, interoperability protocols, and Proof-of-Stake consensus mechanisms support scalability and sustainability. The discussion links these findings to Technology Acceptance and Innovation Diffusion theories, showing that institutional adoption is driven by perceived usefulness, regulatory legitimacy, and technological compatibility. Market Regulation and Institutional theories further explain divergent national regulatory approaches and increasing global coordination efforts. Sustainability considerations emerge as a critical determinant of long-term viability, shaping both technological development and policy intervention. Cryptocurrencies represent a transformative element of the global financial system, offering opportunities for efficiency, inclusion, and innovation.

Rajasingh Gandhi Ramdas

Contemporary enterprises encounter substantial difficulties managing information dispersed across varied cloud infrastructures, geographically separated facilities, and specialized application environments. Traditional centralized frameworks, including consolidated data repositories and analytical warehouses, demonstrate limited capacity to deliver the required velocity, accuracy, and contextual intelligence necessary for sustained digital progression. Multi-Cloud Data Mesh constitutes a transformative architectural approach, advocating decentralized, domain-centric methodologies that systematically address intricate governance complexities and interoperability obstacles at the organizational scale. This framework establishes operational foundations through four fundamental tenets: Domain-Oriented Ownership, Data as a Product, Self-Serve Platform, and Federated Computational Governance. These architectural pillars collectively resolve decentralization imperatives, scalability prerequisites, interoperability complications, and sovereignty considerations inherent in modern enterprise ecosystems. Through ownership distribution to specialized domains, product-oriented information treatment, self-service platform provisioning, and federated governance implementation, organizations attain necessary scalability, operational flexibility, and contextual precision for continuous innovation across sophisticated multi-cloud landscapes

Eria Othieno Pinyi, Joy Selasi Agbesi, Adeniran Oluwatoyosi Awe, Ezekiel Adediji · 5 authors

As the United States Department of Defense (DoD) transitions toward Zero-Trust Architecture, the hardware and software supply chain remains a critical vulnerability. Current provenance models rely on centralized, siloed databases that lack the transparency required to counter sophisticated state-sponsored interdiction. This paper proposes a novel framework: AI-Enhanced Trust Graph Analytics over Distributed Ledgers. The architecture utilizes a permissioned Distributed Ledger Technology (DLT) substrate to host an immutable record of component lifecycles, anchored by Hardware Roots of Trust (RoT) through Physically Unclonable Functions (PUFs). By mapping silicon fingerprints to Software Bill of Materials (SBOM), the system constructs a multi-dimensional Trust Graph. We employ Graph Neural Networks (GNNs) to detect structural anomalies indicative of subversion, while Federated Learning enables inter-agency intelligence sharing without compromising operational security. Our findings demonstrate that this integrated approach significantly reduces the time to detect compromised assets in air-gapped and tactical environments, providing a strategic roadmap for an autonomous, self-healing supply chain.

Zouhair Elhadari

The digitization of medical records in the healthcare sector demands robust mechanisms to ensure data confidentiality, integrity, and privacy. This paper proposes an innovative multi-factor authentication (MFA) mechanism that leverages smart contracts and blockchain technology to secure the tracking of medical records. The proposed system, named Blockchain Authentication with Zero-Knowledge Proof (BAZKP), provides a tamper-proof environment for storing and accessing records while preserving users’ personally identifiable information (PII). A key novelty of BAZKP lies in storing only the character count structure of passwords rather than the actual credentials, combined with zero-knowledge proofs (ZKP) to verify identity without exposing sensitive data. This hybrid blockchain/ZKP approach addresses limitations of centralized and hardware-based solutions, reducing vulnerabilities while avoiding the cost and usability constraints of dedicated hardware systems. The system was implemented and tested on a private Ethereum testnet, with a proof-of-concept application developed using Solidity, Web3.js, and MetaMask. Performance evaluation over 100 transactions for core operations (registration, login, and password reset) demonstrated practical viability: registration incurred the highest latency (≈4500 ms) and gas consumption (≈120,000 gas), while login and reset operations were more efficient (≈4000 ms/80,000 gas and ≈3500 ms/60,000 gas, respectively). Comparative security analysis against existing MFA methods—including 2FA, hardware tokens, and biometrics—confirms that BAZKP provides superior privacy protection through decentralization and ZKP, without the cost and usability drawbacks of hardware-based solutions. Overall, this approach enhances trust in digital health systems by offering a secure, transparent, and privacy-preserving authentication framework for medical data, representing a significant advancement in digital healthcare security. Keywords: Blockchain; Multi-Factor Authentication; Smart Contracts; Zero-Knowledge Proof; Medical Record Security.

Olga S. Stepchenkova

The article deals with the development and theoretical justification of a set of economic and mathematical models that ensure the risk management of decentralised research projects in the pharmaceutical industry using crypto-economic tools. The necessity of this development stems not only from the challenges posed by geopolitical instability and the obsolescence of the traditional “blockbuster” funding model in pharmaceutical corporations, but also from the development of highly specialised markets of medications for the treatment of rare diseases, research into longevity therapies, and the advancement of “long-tail science”, as well as new ways of organising research and development within the paradigm of decentralised science based on Web3 technologies. The study presents models that are unified by an endto-end risk management logic: from the assessment of management structure and human resource capacity, through fundamental valuation, to revenue distribution and protection against biomedical risks. The results obtained make it possible to establish threshold criteria for the management structure in scientific decentralised autonomous organisations (DAOs) and to formulate targeted recommendations for public authorities on improving the regulation of decentralised organisations.

Jusak Jusak, Steve Kerrison

IoT data demands are growing, with Distributed Ledger Technologies (DLTs) offering secure data management, provided they can meet scaling and efficiency requirements that are more restrictive than in conventional application environments. This article comprehensively surveys 27 DLTs of varying paradigms and implementation methods, proposes a scoring method for determining DLT-IoT integration suitability, and then applies that method to the surveyed DLTs. Six DLTs were shortlisted as the most promising, which were then subjected to in-depth analysis around three IoT use cases: health-IoT, e-commerce and automotive manufacturing. We discuss the viability of lightweight DLTs and identify crucial future research directions.

Matteo Loporchio, Damiano Di Francesco Maesa, Anna Bernasconi, Laura Ricci

Abstract The ERC-1155 standard introduced on the Ethereum blockchain allows for managing multiple tokens, both fungible and non-fungible, within a single contract. It also supports batch transfers, thereby reducing transaction costs and enabling a more efficient use of blockchain resources. To assess its impact and level of adoption, this paper presents a comprehensive analysis of the ERC-1155 token ecosystem. First, we examine the activity of ERC-1155 contracts and compare the evolution of transfer volumes with those of the two alternative most popular token management standards. Next, we model the economy of each ERC-1155 contract as a directed graph, where nodes represent users and edges denote token transfers. We then study the topological properties of such graphs, analyzing approximately 40,000 networks until the end of 2024. Results indicate that, within our dataset, the adoption of ERC-1155 is growing, although its functionalities are not being fully utilized. Additionally, about 60% of the networks exhibit a completely centralized topology, while the remaining ones are generally sparse and lack small-world characteristics. Finally, the degree distribution analysis shows that preferential attachment is only present in a minority of the networks and the graphs also display a mild disassortative behavior.

Arundeep Chinta, Lucas Vinh Tran, Jay Katukuri

Time Series Foundation Models (TSFMs) have emerged as a promising approach for zero-shot financial forecasting, demonstrating strong transferability and data efficiency gains. However, their adoption in financial applications is hindered by fundamental limitations in uncertainty quantification: current approaches either rely on restrictive distributional assumptions, conflate different sources of uncertainty, or lack principled calibration mechanisms. While recent TSFMs employ sophisticated techniques such as mixture models, Student's t-distributions, or conformal prediction, they fail to address the core challenge of providing theoretically-grounded uncertainty decomposition. For the very first time, we present a novel transformer-based probabilistic framework, ProbFM (probabilistic foundation model), that leverages Deep Evidential Regression (DER) to provide principled uncertainty quantification with explicit epistemic-aleatoric decomposition. Unlike existing approaches that pre-specify distributional forms or require sampling-based inference, ProbFM learns optimal uncertainty representations through higher-order evidence learning while maintaining single-pass computational efficiency. To rigorously evaluate the core DER uncertainty quantification approach independent of architectural complexity, we conduct an extensive controlled comparison study using a consistent LSTM architecture across five probabilistic methods: DER, Gaussian NLL, Student's-t NLL, Quantile Loss, and Conformal Prediction. Evaluation on cryptocurrency return forecasting demonstrates that DER maintains competitive forecasting accuracy while providing explicit epistemic-aleatoric uncertainty decomposition. This work establishes both an extensible framework for principled uncertainty quantification in foundation models and empirical evidence for DER's effectiveness in financial applications.

Evangelos Kolyvas, Alexandros Antonov, Spyros Voulgaris

Despite being under development for over 15 years, transaction throughput remains one of the key challenges confronting blockchains, which typically has a cap of a limited number of transactions per second. A fundamental factor limiting this metric is the network latency associated with the block propagation throughout of the underlying peer-to-peer network, typically formed through random connections. Accelerating the dissemination of blocks not only improves transaction rates, but also enhances system security by reducing the probability of forks. This paper introduces SCRamble: a decentralized protocol that significantly reduces block dissemination time in blockchain networks. SCRamble's effectiveness is attributed to its innovative link selection strategy, which integrates two heuristics: a scoring mechanism that assesses block arrival times from neighboring peers, and a second heuristic that takes network latency into account.