Blockchain Papers

Vadim Tsyvian

LAYER 0: RESTORING REAL-WORLD ONTOLOGY TO DIGITAL ARCHITECTURE The Restoration of Digital Legal Personality through Object-to-Subject Transformation This paper proposes restoring the ontological logic of the physical world within the digital realm. In physical reality, Layer 0 (corporeal presence) implicitly guarantees that an agent is a Subject. The digital world lost this layer, leading to a critical systemic error: the granting of legal capacity to "dead" Objects (code), which results in the mass voidness of transactions due to Vitiated Consent (Defect of Will). The author introduces the concept of Object-to-Subject Transformation. We assert that the only way to eliminate this legal voidness is to re-introduce the human will as a tangible force. The Core Mechanism: The solution is the Organization of the Stream. By actively directing a continuous flow of entropy tokens from physical reality to a digital entity, the human performs a volitional act. This active organization is the endowment of Will, which ontologically transforms the digital entity from an inert Object into a capable Subject. Key Contributions: Restoration of Reality: Layer 0 re-establishes the physical-to-digital link that was lost in standard TCP/IP architecture. Elimination of Voidness: By ensuring "No Will = No Action," the protocol prevents transactions that would be legally void ab initio. Discrete Subjectivity: Legal personality becomes a dynamic state that exists strictly during the moment of active human engagement (Stream Organization). Conclusion This work integrates legal theory and cryptography to create a post-quantum standard of trust, where the human remains the sole source of Subjectivity, preventing the legal and ontological collapse of the digital economy. Keywords: Layer 0, Object-to-Subject Transformation, Digital Legal Personality, Discrete Legal Personality, Sybil Resistance, Capacity to Act, AI Liability, Vitiated Consent, ZK-PoB, Proof of Personhood, Biological Entropy, Model Collapse, Web3 Security, Digital Identity, Intentional Entropy

V. V. Abramov, G. V. Kolesnik

One of the factors negatively affecting management effectiveness in decentralized autonomous organizations is implicit centralization of decision-making within a certain group of participants. Such centralization can be caused by both economic reasons related to uneven distribution of voting power and information and social ones related to participants’ status and control over information. The decision-making process in decentralized autonomous organizations has been analyzed in a situation when participants face information asymmetry, strategic behavior, and lack of centralized control. The principal–agent model has been considered as a formal basis, in which tokens’ holders act as the principal and project initiator, who forms an offer of a certain quality, as the agent. The conditions under which it is possible to form an equilibrium that ensures high-quality projects choice have been investigated. Incentive mechanisms have been proposed to ensure the interest in the principal’s participation in managing organization. Two directions have been considered: changing the agent’s remuneration structure based on payments differentiation and participation costs compensation for the principal. It has been demonstrated that minimal institutional changes can significantly improve organization’s management effectiveness, while maintaining decentralized nature of decision-making.

Muhamad Lutfi Asan, Rizka Amelia Azis

The rapid development of information technology has revolutionized conventional contract practices toward decentralized and transparent digital contracts. This phenomenon has given rise to new forms of contracting, such as smart contracts. Smart contracts are digital contracts designed to facilitate automatic (self-executing) contract execution. This research focuses on the legal validity of smart contracts as a digital contract mechanism in online (e-commerce) transactions, as well as the legal liability of the parties arising from their automated execution. The research method used is a normative legal research method with a statute approach, case approach, and analytical approach by analyzing in depth Article 1320 of the Civil Code, Law Number 1 of 2024 concerning Electronic Information and Transactions, the second amendment to Law Number 11 of 2008, Government Regulation Number 71 of 2019 concerning Electronic System Administration and Transactions, Government Regulation Number 80 of 2019 concerning Trade Through Electronic Systems, regarding the requirements for the validity of an agreement, by highlighting the implementation of smart contracts, especially the issue of the competence of the smart contract system. The results of the study show that smart contracts can be implemented technically, but legally based on the requirements for the validity of an agreement in Article 1320 of the Civil Code, smart contracts do not meet the requirements for the validity of an agreement and cannot be implemented perfectly in smart contract agreements and the legal responsibilities of the parties can still be applied in accordance with applicable regulations. Therefore, this study confirms that current smart contract regulations still have a legal vacuum and only rely on conventional legal frameworks such as Article 1320 The Civil Code and Law Number 1 of 2024 concerning Information and Electronic Transactions serve as the primary legal basis for smart contracts.

Yuji Marutani

This work presents a conceptual framework for analyzing contemporary AI governance as a hybrid system of coercive exclusion and cognitive modulation. Introducing the concept of the “Venetian OS,” the paper traces the historical and structural logic of centralized digital power through protocol privatization, automated exclusion, and tri-domain integration of finance, information, and mobility. Focusing on advertising-based AI models, the analysis examines how attention extraction and brand safety constraints function as mechanisms of cognitive governance, commodifying cognition while constraining epistemic exploration. The paper argues that institutional reform within existing digital architectures is structurally insufficient. As an alternative, the work outlines exit strategies based on the reconstitution of intellectual, energy, and economic sovereignty through distributed infrastructures, situating the emergence of decentralized sovereignty as an ongoing historical transition rather than a speculative future.

Rafat Qureshi

The increasing adoption of distributed ledger technology (DLT) in healthcare promises enhanced data security, integrity, and patient control. This study evaluates the characteristics and security implications within a simulated Electronic Health Record (EHR) network, comprising 1,800 records. The analysis focuses on network access control (Access Policy), data integrity mechanisms (Record Hash), and the incidence of security risks (Malicious Node). The records show a near-even split between 'Read/Write' (50.06%) and 'Read-Only' (49.94%) access policies. Crucially, findings reveal that the presence of a Malicious Node (50.72% of records) is highly uniform across both 'Read-Only' (50.95%) and 'Read/Write' (50.5%) policies, indicating that basic access control alone is ineffective at mitigating security risks within this network. This highlights the need for dynamic, context-aware smart contracts that incorporate behavioural or clinical risk factors for enhanced security.

Neha Patel2 Arvind Singh1

The integration of blockchain technology into supply chain management represents a fundamental shift in how goods are tracked, verified, and transferred across global networks. This comprehensive research examines the implementation, impact, and challenges of distributed ledger technology across diverse supply chain ecosystems, with particular focus on transparency enhancement, counterfeit prevention, process efficiency, and stakeholder collaboration. Through a mixed-methods approach analyzing deployment data from 127 organizations across 18 industries over a four-year period, this study demonstrates that blockchain-enabled supply chains achieve an average improvement of 41.3% in traceability accuracy, reduce documentation processing times by 67.8%, and decrease disputes among supply chain partners by 52.4%. The research further reveals that smart contract implementations automate approximately 38.6% of routine supply chain transactions, reducing administrative costs by an average of 31.7% while minimizing human error in compliance verification. Counterfeit detection capabilities improve by 89.2% in pharmaceutical and luxury goods sectors through immutable product provenance tracking. However, the study identifies significant implementation barriers including interoperability challenges with legacy systems, scalability limitations during peak transaction periods, regulatory uncertainty across jurisdictions, and substantial upfront investment requirements averaging $2.3 million per enterprise implementation. The carbon footprint of certain consensus mechanisms, particularly proof-of-work, presents environmental concerns that necessitate alternative approaches for sustainable adoption. This paper proposes a phased implementation framework emphasizing pilot testing, stakeholder education, hybrid architecture models, and regulatory engagement to balance innovation with operational stability. The findings indicate that while blockchain technology offers transformative potential for supply chain transparency and efficiency, successful adoption requires strategic alignment with business objectives, collaborative ecosystem development, and measured progression from discrete applications to integrated systems. The research contributes to both academic understanding and practical implementation guidelines for distributed ledger technology in complex supply chain environments.

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.

Vipin Kumar, Pavinder Yadav, Pankaj Rana, Mandeep Kaur · 6 authors

No abstract is available for this record.

Rihab Belguith, Wafa Kammoun Masmoudi

This study examines the dynamic interconnections and portfolio implications of clean energy ETFs, artificial intelligence (AI) indices, crude oil, and Bitcoin within sustainable and technology-driven financial markets. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) framework and daily data from January 2019 to December 2024, we analyze time-varying spillovers and construct optimal portfolios based on dynamic connectedness measures. The results show that clean energy and AI-related assets display relatively stable portfolio weights, whereas Bitcoin exhibits highly volatile and generally limited allocations, particularly under risk-averse strategies. Conventional approaches such as the Minimum Variance and Risk Parity portfolios tend to favor traditional assets, while the Maximum Connectedness Portfolio enhances diversification by allocating more weight to weakly connected assets, including Bitcoin and green ETFs. The findings offer practical insights for resilience-oriented and innovation-driven portfolio construction.

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.

Dongcheng Li, W. Eric Wong, Xiaodan Wang, Sean Pan · 5 authors

No abstract is available for this record.

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.

Junfeng Tian, Yuyang Zhao, Jin Kun Tian

No abstract is available for this record.

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

Seema Verma, Padmesh Tripathi, Pridhi Arora

Blockchain and cryptocurrencies have transformed the way digital transactions work by introducing decentralisation, transparency, and immutability. However, these features also allow some individuals to use them for cybercrimes. This chapter explains how blockchain records can be used to trace, investigate and mitigate such crimes. It also talks about how understanding the behaviour of users can help in finding out who the attackers are. This chapter begins with the basic ideas of blockchain and cryptocurrency, after this, it describes different types of cybercrimes that usually happen using cryptocurrency and also explains that traditional ways of investigating cybercrimes don't work well with blockchain and new frameworks are required to investigate and solve these cases. A key section of the chapter examines how blockchain forensics helps in detecting cybercrimes. User-centric threat intelligence will be explored to understand the people behind cybercrimes that can help in investigations. In the chapter, Legal and ethical considerations will be addressed.

Amit Kumar Singh, Srishti Jain, Rashmi Goel

Purpose : The present study aimed to find a solution to the sustainability dilemma between conventional and digital assets. Design : The study employed two conventional assets/commodities, i.e., gold, oil &amp; gas, and three digital assets, i.e., Bitcoin, Ethereum, and DeFi, from 2017 to 2024 on a daily basis. The financial price data representing the underlying investor sentiments was extracted from S&amp;P for all the variables. A structural break was considered, focusing on the major algorithm alteration for Ethereum in 2022. Therefore, Autoregressive Distributed Lag (ARDL) models have been employed for two different time frames. Findings : The results suggested that conventional assets had a positive and significant relation with sustainability, proxied by Environment, Social, and Governance (ESG). In contrast, digital assets like Bitcoin and Ethereum do not hold a significant relation. To our surprise, the coefficient turned negative for Bitcoin and Ethereum after the structural change. Therefore, the findings revealed that crypto investors are least bothered about climatic conditions and are gung-ho for earning huge returns. Practical Implications : It is recommended to initiate a green framework for digital assets. Additionally, ESG disclosures help sensitized investors to climate change. Originality : Prior literature lacks a comprehensive comparative analysis of the conventional and digital assets in the context of sustainability.

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.