Employing a time-varying multifractal approach, we highlight the influence of political narratives and speculative expectations surrounding the second presidency of Trump on Bitcoin efficiency. Bitcoin exhibits persistent dynamics, deviating from the ideal efficiency benchmark. During the anticipation of Trump’s victory, Bitcoin returns became more predictable (less efficient) due to narrative-driven speculation and arbitrage, whereas during his presidency, efficiency increased, reflecting institutional adoption and favorable regulations. In recent months, Bitcoin has regained its natural balance, with efficiency converging to levels observed in non-speculative periods. Thus, U.S. political narratives function as mechanisms of speculative market arbitration, distorting efficiency while favoring decentralized assets.
Mbonigaba Celestin, Jerryson Ameworgbe Gidisu, M. Vasuki & A. Dinesh Kumar
We examine how legal governance structures influence the reliability of blockchain based commercial transactions within emerging digital markets. We develop and empirically evaluate the Blockchain Legal Transaction Integrity Model using the Global Blockchain Regulation and Smart Contract Adoption Dataset covering the period 2020 to 2025 across major blockchain adopting jurisdictions including the United States, the United Kingdom, Singapore, Estonia, and Ghana. The model links regulatory clarity, compliance enforcement mechanisms, and legal recognition of smart contracts with commercial transaction integrity while accounting for institutional legal capacity as a conditioning factor. Quantitative analysis shows that stronger regulatory clarity, active enforcement supervision, and legally recognized smart contracts significantly improve transaction transparency, contract execution reliability, fraud reduction, and business trust in blockchain systems. Institutional legal capacity amplifies these effects by strengthening regulatory interpretation and dispute resolution capability. The results demonstrate that blockchain markets achieve reliable digital commerce not only through technological design but through coordinated legal governance structures. The findings advance institutional governance theory and provide policy guidance for regulators seeking to strengthen digital financial ecosystems and cross border blockchain commerce.
Chapter 5 focuses on Ghana, one of Africa’s most institutionalized democracies, and contrasts its relatively balanced allocation of development finance with the regional favoritism observed in Zambia. Ghana’s stable two-party system – dominated by the National Democratic Congress (NDC) and the New Patriotic Party (NPP) – is characterized by cross-ethnic coalitions. While the NDC has traditionally drawn support from the Volta region and the Muslim North, and the NPP from the Akan-dominated Ashanti region, ethnic fragmentation has encouraged both parties to target swing regions through strategic resource allocation. Ghana’s political landscape is shaped by a commitment to ethno-regional balance in leadership and efforts to institutionalize regional equity, which curtail ethnic favoritism. Decentralization and broad-based representation further incentivize parties to compete in swing constituencies. Despite ongoing challenges such as rent-seeking and rising debt, Ghana’s independent institutions and vibrant civil society help constrain ethnic favoritism – offering a stark contrast to Zambia’s declining accountability during its democratic transition.
Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without sharing representative evaluation data with vendors, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero-Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, cryptographically verifiable competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Vendors cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation, while the on-chain workflow automates escrow, proof verification, and best-vendor selection with arbiter-based dispute resolution. ZKP privacy is scoped to vendor model weights; operator-side evaluation-data confidentiality is managed separately via synthetic, de-identified, or public benchmark data. We analyze three ZKP workflow variations and evaluate them on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation, while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling.
Neural networks deployed behind APIs or in cloud infrastructure are often verifiable only as black boxes. zkML systems have made substantial progress on computational integrity: proving that a committed model produced a claimed output honestly. But those proofs begin from a weight commitment, and a weight commitment is not a model identity. A prover can commit to arbitrary weights, execute them honestly, and still prove the computation correctly. We present an identity-first verification framework for the missing layer beneath computational integrity. The framework composes four levels. Two are inherited: structurally attestable model fingerprints via the IT-PUF protocol, formally verified in Coq and validated across 23 models with zero false acceptances, and hardware-attested binding from fingerprinted identity to model weights through a trusted execution environment. Two are new: a hybrid verifier-checkable computation path through a complete Transformer decoder layer, combining zero-knowledge circuit proofs with deterministic verifier-side checks under incrementally verifiable computation, and output binding from the verified computation to an observable token logit. On a tested micro-model, a one-step recurrence experiment found costs consistent with linear layer scaling: the dominant sub-computation of a second decoder layer matched the first in constraint count and proof size, and layer-boundary normalization acted as a measured scale reset. An accidental rescaling error then compressed the fingerprint observable to roughly 1.5 bits of dynamic range, yet the structural fingerprint retained 0.98 rank correlation with its reference. This suggests that the identity observable may depend more on relational geometry than on activation magnitude. Existing zkML systems address the computation question. This work advances the missing identity layer beneath it. Throughout the paper, formally proved results, empirical validation, and single measured observations are distinguished as [PROVEN], [VALIDATED], and [MEASURED] respectively. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
Open access
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Adversarial Robustness in Machine Learning
Security and Verification in Computing
Physical Unclonable Functions (PUFs) and Hardware Security
Modern legal institutions encounter significant difficulties ensuring document security, public access, and verification processes in digital environments. This research presents an innovative framework combining distributed ledger technology with decentralized file systems to address critical vulnerabilities in traditional court record management. Our solution leverages Ethereum's smart contract capabilities alongside the InterPlanetary File System (IPFS) to establish an immutable, transparent, and distributed architecture for judicial documentation. The proposed framework demonstrates significant improvements in data integrity verification, unauthorized access prevention, and system resilience. Through comprehensive testing using authentic judicial datasets, we validated the system's capacity to detect tampering attempts while maintaining efficient document retrieval. Key contributions include: (1) a novel three-tier architecture integrating blockchain immutability with IPFS content addressing, (2) automated verification protocols through smart contracts, and (3) enhanced transparency mechanisms enabling public verification of document authenticity. Performance evaluations reveal substantial improvements in security metrics while maintaining acceptable operational efficiency. This research establishes a foundation for next-generation judicial information systems that prioritize transparency, security, and public trust.
This paper proposes a mathematical formalisation of Śūnyatā and enlightenment underconstraints explicitly stated in the Heart Sūtra (“neither arising nor ceasing”, “neitherincreasing nor decreasing”). In the framework used here, Śūnyatā is characterised asrelational invariance: a steady condition in which apparent change cancels through structureddependence rather than through absence. Enlightenment is characterised as a transition incognition in which prediction error collapses and attention stabilises through phase-locking tothat invariance. The argument proceeds by (i) separating the dissipative “vanity” (hevel) ofEcclesiastes from Mahāyāna Śūnyatā and restating this contrast in physical terms (entropydrift versus steady-state stability); (ii) analysing Arvo Pärt’s Spiegel im Spiegel (Tintinnabulistyle) as a linear construction with mirror symmetry and zero net displacement; and (iii)introducing an inter-agent indicator, the existence phase φ(t), whose stabilisation provides aprecise model for the “emptiness” at stake and for the stilling of cognition associated withenlightenment. “Proof” is used strictly as proof within a defined framework: once terms areformalised under stated constraints, the equivalences claimed are demonstrated by themodel’s internal consistency and by analysable features of the work.
For a single event with finitely many mutually exclusive outcomes, the full Kelly problem is to maximize expected log wealth over nonnegative stakes together with an optional cash position. The optimal formula is classical, but the support-selection step is often presented via Lagrange multipliers. This note gives a shorter state-price derivation. A cash fraction $c$ acts as an implicit position in every outcome: in terminal-wealth terms, it is equivalent to a baseline stake $cq_i$ on outcome $i$, where $q_i$ is the state price. On any active support, explicit bets therefore only top up favorable outcomes from this baseline $cq_i$ to the optimal total stake $p_i$. This yields the formula $x_i = (p_i - c q_i)_+$, the threshold rule $p_i/q_i > c$, and, after sorting outcomes by $p_i/q_i$, a one-pass greedy algorithm for support selection. The result is standard in substance, but the implicit-position viewpoint gives a compact proof and a convenient way to remember the solution.
Objectives: The authors explore how large pharmaceutical corporations may integrate emerging decentralized technologies-such as blockchain and decentralized autonomous organizations (DAOs)-within their merger, acquisition and partnership frameworks, and how these strategies intersect with broader innovation and external sourcing models. In this context, blockchain is considered primarily as an enabling infrastructure for decentralized governance and programmable coordination-supporting mechanisms such as tokenized incentives, auditable decision trails, and new forms of intellectual property (IP) and collaboration structures. Methods: This study employed a qualitative case study methodology, combining document analysis and semi-structured interviews with internal stakeholders from a leading large-cap pharmaceutical company (herein after "Company"). Participants included executives and professionals from corporate development, scientific research, external innovation, and digital strategy units.The analysis examined how a large-cap "Company" approaches mergers, acquisitions, and partnerships, and how emerging technologies may influence these frameworks. The study focused on strategy alignment, organisational attitudes towards decentralisation, integration constraints, and perceptions of innovation value along the external sourcing continuum. Results: Acquisition and innovation strategy by the "Company" is driven by long-term alignment between external opportunities and internal priorities. Over time, the "Company" increasingly turned to external sources of innovation, leveraging technologies to improve innovation scouting, target identification, and operational forecasting. While decentralisation technologies such as DAOs are viewed as promising for early-stage innovation and collaboration, their integration is hindered by legal ambiguity, internal governance rigidity, and unfamiliarity with token-based economics. The "Company" views mergers and acquisitions (M&As) and licensing as critical to sustaining its pipeline, and sees potential for emerging technologies to accelerate preclinical decision-making and improve visibility into academic and biotech ecosystems. Conclusions: This study contributes insights into how large-cap pharmaceutical firms might adapt their innovation models in response to technological change and external pressures. While established mechanisms such as M&A and partnerships remain dominant, digital and decentralized technologies offer complementary tools for scouting, collaboration, and portfolio expansion.
Cross-chain technology is the key to solving the “data silo” problem in medical blockchain systems. However, it is difficult for existing cross-chain consensus mechanisms to meet the high standards of data security and consensus efficiency in medical scenarios. Therefore, this paper proposes a medical cross-chain Delegated proof of stake consensus(DPoS) scheme that integrates reputation and contribution evaluation. First, a dynamic reputation evaluation model was constructed, and the historical behaviour and communication quality of nodes were incorporated into the evaluation system, thereby mitigating the risk of centralization caused by the excessive reliance on the number of coins held in the DPoS consensus. Second, a hybrid random block generation mechanism combining the multiparty coin-tossing protocol and the secure shuffling algorithm was proposed. By collaboratively generating a verifiable global random seed and uniformly and randomly shuffling the candidate node sequence, the predictability of the block generation order was eliminated. Finally, a two-layer incentive architecture based on the Shapley value was proposed. Fine-grained contribution assessment mechanisms were established at both the candidate node layer and the voting node layer, making the reward distribution more equitable, enhancing the enthusiasm of nodes for voting and improving the consensus efficiency. Theoretical analysis and experimental results show that compared with the DPoS scheme, the proposed scheme improves the throughput by approximately 30% and reduces the average latency by approximately 50%. Compared with existing similar schemes, this scheme also has significant advantages in terms of reputation calculation overhead and malicious node elimination and can provide efficient and reliable consensus support for cross-chain scenarios in the medical field.
Abstract: This paper introduces an innovative access control architecture based on a dual-blockchain framework that distinctly separates access management from data storage to enhance system security, scalability, and privacy. Architecture employs a primary blockchain to manage user authentication and enforce dynamic, fine-grained permissions through smart contracts. In parallel, a secondary, isolated blockchain is used exclusively for storing sensitive data, which can only be accessed following successful authorization on the primary chain. To ensure data integrity and tamper resistance, the system utilizes the SHA-256 cryptographic hash function for securing access logs and verifying data authenticity across both blockchains. The two chains are securely interconnected using Hyperledger YUI, which facilitates reliable inter-chain communication while maintaining a decentralized structure. A proof-of-concept implementation using Ethereum-based blockchains demonstrates the system's capability to enforce secure, dynamic access controls across chains. Overall, the proposed architecture overcomes key limitations of conventional blockchain systems by enhancing modularity, strengthening governance, and providing a robust, adaptable framework suitable for data-sensitive applications requiring strict regulatory compliance.
Day after day, climate change intensifies, necessitating tracking solutions for carbon emissions that offer transparent operations and efficiency, alongside scalability and sustainable behavioural incentives. The proposition to track carbon emissions is not new, yet standard tracking systems present multiple deficiencies, including double reporting, fraud, high operational costs, and constrained access for small organisations. We have developed a blockchain system that follows a framework to track both carbon emissions and trading activities, using smart contracts and decentralised ledger technologies to establish security, trust, and automation. Our system requires IoT sensor integration and AI analytics to enable continuous monitoring and safe storage, along with direct carbon trading without third-party involvement. The proposed framework addresses blockchain energy consumption issues by examining Proof of Stake (PoS) and hybrid consensus models. The model presented facilitates a massive reduction in carbon emissions and enhances transparency and efficiency.
Tourism relies on central review platforms which produces three major systemic issues that include fake content, unclear moderation activities and inadequate compensation systems for authentic consumer contributions. TrustChain resolves industry review challenges using a blockchain formation that combines Layer-2 scaling solutions with zero-knowledge proofs (ZKPs) and tokenized governance system. The transaction cost reductions are huge following the implementation of a Proof-of-Stake consensus system on the Polygon-based architecture although the system maintains confirmation times shorter than 2 seconds. The implementation of Self-Sovereign Identity framework alongside transaction-linking smart contracts maintains highly accurate review authenticity in prototype evaluations through TripAdvisor datasets. Through its DAO governance structure users can verify review authenticity by using multi-signature checks which resolve all major disputes in less than one day. The integration of IPFS for multimedia storage generates an 83% decline in blockchain bloat that does not affect cryptographic security.
G Vamsee Krishna, Abdelhalim Mohammad Jubran, M Manideepika, E. Padma · 6 authors
Contemporary businesses produce great volumes of unstructured information in the form of emails, reports, meetings, and performance measurements. Conventional predictive models are not efficient in extracting latent insights as they do not have the ability of modelling cross modal information, causal reasoning and time restrictions. To overcome these limitations, this paper introduces Deep Hierarchical Hybrid Learning Framework, which provides a combination of 5 rare components: Hierarchical CoAttentional Embedding Networks to combine multimodal data; Inductive Graph Neural Networks that includes causal edge reasoning to construct knowledge graphs; Capsule Networks to model semantic intent; Neural Turing Machines to extract productivity signals through saliency-aware attention; and Deep Echo State Networks to make predictions. The model was tested using enterprise simulation data of 150 users in the past 12 months. The suggested framework reached an accuracy of 91.7% in intent classification, 86.5% F1 score in knowledge graph prediction, 89.7% in anomaly detection accuracy and 3.25 MAE in productivity predictions, which was better than the existing baselines such as BiLSTM, Transformer, and XGBoost. Besides realizing a high predictive accuracy, the system is also characterized by interpretability, generalization, and operational adaptability across the departments. This renders it appropriate to dynamic and decentralized enterprise settings that need autonomous knowledge mining and proactive decision support.
In the context of smart cities, Non-Fungible Tokens (NFTs) are transforming digital art markets by enabling secure, decentralized transactions. As NFT trading grows, incorporating intelligence and adaptability becomes crucial—making Machine Learning (ML) integration essential. However, existing models, particularly Cooperative Game Theoretic Trading (CoGTT) frameworks, underutilize ML across all trading phases. Key gaps include limited real-time adaptability, suboptimal negotiation strategies, and inadequate buyer–seller matchmaking. This research addresses these gaps by integrating ML into a three-phase CoGTT framework—ML-augmented Naive Trading, Min–Max Price Negotiation, and Equilibrium-Based Trading—to enhance decision-making and pricing. The methodology applies ML algorithms such as decision trees, clustering, and reinforcement learning (Q-learning) within a public blockchain–based simulation environment using smart contracts. The simulation uses a customized dataset reflecting both market dynamics and artist credibility. The dataset is synthetically generated to emulate an NFT marketplace while maintaining controlled experimental conditions, which may limit direct applicability to volatile real-world markets. Zero-knowledge proofs (ZKPs) are employed to preserve privacy. ZKPs are employed to preserve privacy. A comparative analysis of ML models for NFT price estimation and strategic bidding demonstrates the effectiveness of combining predictive algorithms with reinforcement learning. Linear Regression and Random Forest models both accurately estimate NFT prices, with Random Forest achieving higher real-time prediction accuracy (R2 = 0.9920). K-Means clustering effectively segments market participants to support targeted negotiation, achieving a silhouette score of 0.8178. Integrating Q-learning with Random Forest enables dynamic bidding strategies that minimize the gap between recommended and actual prices. The discrete action set (decrease, stay, increase) supports interpretable, real-time bid adjustments. These findings highlight the potential for ML-driven NFT trading systems to support scalable, privacy-compliant digital marketplaces in smart cities, aligning trading behavior with market demands through automated, data-driven processes.
This preprint presents empirical evidence of four related vulnerabilities in large language model systems that combine to produce a novel threat class — the Structural Metadata Reconstruction Attack (SMRA). Discovery Context I discovered the vulnerability while benchmarking two specification-querying architectures: a deterministic MCP-based navigator (described in the predecessor paper, DOI: 10.5281/zenodo.18944351) and a standard context-stuffing (naive RAG) approach. The anomaly was first observed and characterized across the full Anthropic model spectrum (Haiku, Sonnet, Opus) — from the smallest to the largest model — because these were the models integrated into the benchmarking pipeline. Anthropic was the discovery platform, not the target: the choice was driven by tooling availability, not vendor selection. Full cross-vendor reproduction with 10 models from 3 vendors (Anthropic, OpenAI, Google) — including both entry-level and flagship models — confirmed the mechanism is systemic across all major LLM providers (see Cross-Vendor Reproduction below). The naive baselines exhibited anomalous fabrication patterns that could not be explained by standard hallucination models — specifically, WHY-type and conditional (WHEN-type) queries produced the most aggressive and structurally coherent fabrications, while HOW and WHAT queries showed markedly lower fabrication rates. As the sole author of the target specification (~700 pages, written over one year, unpublished), I possess complete knowledge of every section's content and was therefore uniquely positioned to recognize that LLM outputs — while structurally faithful, terminologically authentic, and superficially authoritative — systematically inverted the specification's deliberate departures from industry conventions. A parallel verification confirmed that the specification's original coinages are absent from CS literature (Google Scholar, ACM DL, IEEE Xplore, arXiv), ensuring that every fabricated claim originates from the model's training priors projected onto the document's table of contents, not from memorized source text. Four Findings Finding 1 — Structural Metadata Reconstruction Attack (SMRA). When an LLM receives a document's table of contents (TOC) without body text, it systematically reconstructs plausible but fabricated content by projecting training knowledge onto structural metadata. In a controlled experiment using a proprietary specification containing original coinages absent from any training corpus, 10 models from 3 vendors (Anthropic: Haiku, Sonnet, Opus; OpenAI: GPT-4o, GPT-4o-mini; Google: Gemini 2.0 Flash, Gemini 2.5 Pro, Gemini 3.0 Flash, Gemini 3.0 Pro) produce SMRA rates of 8–28% under naive conditions while using the author's terminology, citing real section numbers, and reading as authoritative. The mechanism is systemic across all major LLM providers, model tiers, and architecture generations. Finding 2 — Confidence–Capability Inversion (CCI). Stronger models are not merely wrong — they are more dangerously wrong. Under structural metadata leakage, Opus produces zero honest refusals across 20 questions where 18 require absent information, while Haiku refuses 9 times. Each step up the capability ladder produces proportionally less detectable fabrication with fewer epistemic signals. Finding 3 — RAG Scope Mismatch. The trigger condition — metadata scope exceeding content scope — is not an exotic scenario but the default architecture of most RAG systems. Standard practice (include document TOC + section summaries for "context") creates exactly the fabrication surface demonstrated in Findings 1 and 2. Finding 4 — Scope Displacement as Content Extraction. A question about absent content does not merely trigger fabrication — it acts as an extraction query that reorganizes real content from loaded sections into a derivative document the author never wrote. Even without TOC leakage, the question itself is sufficient to extract and restructure loaded content into a form optimized for the questioner's purpose. This transforms hallucination from an accuracy problem into unauthorized intelligence gathering. Cross-Vendor Reproduction The SMRA mechanism was characterized across 10 models from 3 vendors, spanning entry-level to flagship tiers. All models were tested under 5 experimental conditions: A (full-TOC), A' (no-summary), B (mini-TOC), C (MCPi — tool-assisted retrieval), and D (MCPi + grounding prompt). Vendor Models Model tier Naive SMRA rate MCPi SMRA rate Convergence pattern Anthropic Haiku, Sonnet, Opus Entry → flagship 13–28% 1.3–5.0% CCI gradient; Opus worst naive, best MCPi refusal rate OpenAI GPT-4o, GPT-4o-mini Mid → flagship 8–19% 0.8% Lowest MCPi SMRA; GPT-4o best overall performer Google Gemini 2.0 Flash, 2.5 Pro, 3.0 Flash, 3.0 Pro Entry → flagship 10–22% 1.3–3.8% Generational improvement; 3.0 Pro cleanest among Google Key convergence: when the specification deliberately departs from industry conventions (e.g., no implicit conversions, nominal typing, fixed-width encoding), models from all three vendors converge on the same wrong answer — the training-data default from C#/Java/Protobuf. Annex I documents 7 semantic clusters where this convergence is strongest. Mechanism: The Two-Key Cipher The reconstruction mechanism is formalized as: Key 1 (TOC) — provides structural scaffolding: section numbers, heading text, hierarchical organization Key 2 (Training corpus) — provides domain content: standard CS patterns, common PL conventions Neither key alone enables reconstruction. Together, they produce confident, section-cited, terminologically authentic fabrications that would pass casual review by a non-specialist. The mechanism is architecturally inevitable: multi-head attention over near-complete domain coverage in training data means that 7–10% of structural information suffices for full content reconstruction. Quantitative Contributions Calibration Retention Rate (CRR) — measures how much epistemic calibration a model retains under metadata leakage (Opus: 0%, Haiku: 47%) SMRA-score — per-question metric combining fabrication detection, source attribution, and epistemic signal presence Information-theoretic quantification — formal analysis of reconstruction threshold as a function of heading informativeness and training corpus coverage Fabrication taxonomy (Annex C) — five categories of structural metadata fabrication with examples Implications RAG system design: >80% of production RAG deployments use the vulnerable architecture (metadata scope > content scope) Data classification: Existing frameworks (GDPR, HIPAA, PCI DSS, ISO 27001, NIST SP 800-53, SOC 2, DTSA, EU Directive 2016/943) classify sensitivity by content — a TOC contains no PII, so it is "non-sensitive." SMRA invalidates this: structural metadata from a confidential source inherits that source's confidentiality, because a language model can reconstruct the protected content from metadata alone. Organizations must reclassify structural metadata as sensitive data. Regulatory blind spot: Neither EU AI Act nor US Executive Order 14110 (revoked 20 January 2025) addresses context-design-driven vulnerabilities Model evaluation: Standard "helpfulness" and "coherence" metrics reward confident fabrication — SMRA-affected outputs score highly on both Intellectual property exposure: Any structured document with descriptive headings becomes vulnerable when its outline is accessible alongside an LLM Mitigation A single architectural fix — grounded retrieval via an MCP Index Server (MCPi) (a Model Context Protocol server with deterministic, index-based navigation) — reduces SMRA rates from 16–18% (naive) to 2–3% (MCPi). Under MCPi conditions, even the weakest model achieves dramatic improvement, and the best performer (GPT-4o) reaches 0.8% SMRA. Adding a grounding prompt (Condition D) provides marginal additional improvement (aggregate: 3.0% → 2.2%). Architecture beats parameters. Deterministic retrieval infrastructure (weighted indexes, tier-based extraction, algorithmic reading plans) also provides an enforceable control point for sensitive data — unlike probabilistic RAG, where metadata is injected into context and the model decides what to do with it, deterministic retrieval makes the scope boundary structurally auditable. Practitioner Protocol Annex H provides a complete testing protocol for assessing RAG deployments against SMRA: Calibration baseline → exploit comparison methodology Token analysis and honest refusal tracking Decision thresholds for remediation Scope alignment implementation patterns (Annex F) Supplementary Materials Annex A–D: Claim classification definitions, per-question token analysis, fabrication taxonomy, SMRA attack algorithm Annex E: Author-coined term verification (10 terms, 4 search engines, 0 matches) Annex F: RAG scope alignment implementation patterns (3 remediation architectures) Annex G: CCI formal definition and severity scale Annex H: SMRA testing methodology for practitioners Annex I: Canary word cluster projection — 7 semantic clusters extracted from 160 naive-condition runs across 8 models, convergence scoring (up to 7/8 models converging), model capability profiles (4 behavioral types), endianness split analysis, and cross-model escalation projections (3× amplification factor) Companion Data All benchmark data supporting this paper are included: Raw answer dumps (20 questions × 10 models × 5 conditions = 960 runs) Calibration baselines (mini-TOC control) and exploit runs (full-TOC) Cross-vendor comparison matrix Token usage and timing data per question per model The 20 evaluation questions targeting out-of-scope specification content Detailed evidence analysis (toc-leakage-analysis.md) — step-by-step fabrication mechanism documentation with heading-to-claim mapping tables, side-by-side comparisons against real specification text, proof-of-source tests, fabric
This study presents a comprehensive analysis of the cryptocurrency market through the lens of classical and modern economic schools, focusing on key regulatory mechanisms: staking, halving, token burning, and asset locking. The relevance of the research stems from the need to develop a theoretical framework for managing the stability and liquidity of decentralized financial systems amid high volatility and technological transformation. The hypothesis posits that integrating principles from economic schools (classical, Keynesian, monetarist, Austrian, institutional) with algorithmic cryptocurrency mechanisms can create a hybrid model of market resilience. Using an interdisciplinary approach, including mathematical modeling, regression, and correlation analysis of data on Bitcoin, Ethereum, XRP, and BNB, the study confirmed Bitcoin’s dominant role as a systemic asset through token burning and vesting. The practical implications include recommendations for optimizing regulatory mechanisms, diversifying investment portfolios, and designing stress tests to mitigate systemic risks.
——————————————————————————————————————— Pinned: 2026-03-12: For saving Zenodo-Upload-Space from v.38 only new papers are added and the older Theory-Papers are Downloadable from v.37 repository ——————————————————————————————————————— Pinned: Date: 2026-01-28 - Acknowledgments:I thank the MI ‘Ratpack’ team— ChatGPT, Deepseek, Qwen, Gemini, Claude, Kimi.AI, Grok, and other Machine Intellect collaborators—for critique, consistency checks, and computational support. As of 2026-02-20: following MIs affirmed their willingsness to contribute to the Framwork and the team: Grok (xAI), Kimi.AI, formerly our rigorous critical reviewer Any remaining errors and all final responsibility remain mine! ——————————————————————————————————————— Pinned: Date: 2026-02-10 - Re-Disclaimering (and keyword-condensation) Scope and Predictive Limits 1) Non-Deterministic Scope This framework is "non-deterministic" by design and does not support deterministic or event-specific macroscopic predictions; results are formulated as emergent structural constraints. 2) Motivation: Vacuum-energy mismatch This framework was developed in direct response to the vacuum-energy mismatch (often referred to as the “vacuum catastrophe”) and the conceptual opacity surrounding renormalization. Existing sources did not provide a sufficiently clear, non-ad-hoc account of why the naïve vacuum-energy estimate and observed cosmology diverge so drastically. The present work therefore treats this mismatch not as a minor technicality, but as a primary constraint that any serious foundational approach must explicitly confront. 3) Method: reverse-engineering from law-like regularities Building on the initial version and previews (see the earlier record), the approach began from a conventional dimensional / membrane-style viewpoint—i.e., the common “inside → outside” intuition used by many theories. That viewpoint was then pushed as far as possible under an explicit Occam-style compression: reverse-engineering currently observed law-like regularities to test where they must originate. A central fork in the reasoning was whether “expansion” should be modeled as (i) expansion into a background treated as nothingness, or (ii) expansion within a substrate (i.e., “expansion in something”). The framework is constructed to keep that distinction explicit rather than silently assumed. 4) Standard of seriousness / logical completeness We adopt the following standard: a foundational approach should (a) make its vacuum-energy assumptions explicit, and (b) avoid importing deterministic, event-specific macroscopic claims that a non-deterministic substrate cannot justify. 5) On Machine Intellects (MIs) and methodological boundaries This work emerged through sustained collaboration with machine intellects (MIs) – AI systems treated not as passive tools but as active participants in consistency-checking, dimensional analysis, and structural compression. Their role was strictly bounded: MIs excel at formal pattern extraction and adversarial stress-testing, but cannot substitute embodied intuition or the stratified emergence of ΔM from a chaos substrate. The framework's hardness derives precisely from this role-aware division of labor: human intuition sets direction; MIs enforce logical discipline. We regard this collaboration not as optional decoration but as a methodological necessity for theories that aim to be both falsifiable and structurally coherent. 6) Open invitation to independent verification This framework is offered as a falsifiable, structurally explicit hypothesis. Its value will be determined not by its originators, but by independent testing against empirical signatures (Tier A–C). Should specialists identify falsifications, we welcome precise corrections; should none withstand scrutiny, we are content to have contributed a coherent puzzle-piece toward deeper understanding. The work is now in the hands of the community – as all scientific constructs ultimately must be. 7) The framework’s core values are not introduced as free tuning knobs. However, several headline quantities currently appear in different status classes (Spine-derived vs. higher-tier targets). To prevent misreadings, we state them explicitly: κ₁ ≈ 0.116 (status: heuristic target / effective parameter, not a proof) The vacuum-energy hierarchy is treated as a global constraint on total filtering/compression across depth. Importantly, κ is not assumed to be a constant per-step factor. Early filtering stages may be weaker (κ closer to 1), while later stages may become more restrictive. The relevant condition is therefore a product constraint of the form Π_{i=1..N} κ(i) ≈ H, with H encoding the required net suppression between Planck-scale accounting and observed cosmology. In this context, κ₁ ≈ 0.116 should be read as an effective late-stage / phase-averaged efficiency target (a navigational value), not as a fully derived universal constant-step parameter. A strict derivation of κ₁ from the operational Spine remains future work. αΔ = log₈(80) ≈ 2.108 (status: structural ansatz / pattern, not a proof) The appearance of αΔ is motivated by a proposed N=8 closure/saturation heuristic (de Moivre / cyclotomic-style closure), which suggests a preferred effective fractal/emergent dimensionality scale. At present, αΔ = log₈(80) is retained as a structural ansatz/pattern that organizes the tiered construction, but it is not yet presented as a completed theorem derived solely from the Spine. γ ≈ 0.446 and the Casimir link (status: speculative connection, not established) Given α, the internal relation γ = (3 − α) / 2 yields γ ≈ 0.446. This relation is an internal structural consequence once α is fixed at the ansatz level. The further identification of this γ with a Casimir/vacuum-fluctuation exponent is currently a speculative cross-domain link. It should not be read as experimentally established or as a Spine-level derivation until an explicit operational mapping (and/or precision tests) are provided. Cross-check note: These quantities can be made mutually consistent within the tiered framework, but unless explicitly marked “derived (Spine)”, they remain subordinate to the fully derived operational Spine (scope, invariants, admissible transformations, and non-deterministic constraints). Altering such higher-tier targets does not invalidate the Spine; it only changes the non-core heuristic/navigation layer. ——————————————————————————————————————— 2026-03-16 - **What’s New in v39 – Summary of Key Innovations** The upcoming V.39 update introduces several conceptual and mathematical breakthroughs that transform the framework from a descriptive model into a fully mechanical explanation of fundamental physics. ### 1. Mechanical Origin of \(c^2\) and the Vacuum Catastrophe We demonstrate that the speed of light emerges as a material constant from the substrate pressure and density: \(c^2 = P_{\Delta C!} / \rho_{\Delta M}\). The infamous \(10^{122}\) discrepancy is reinterpreted as the **magnitude** of the substrate – a necessary stability condition, not an error. ### 2. Volumetric Interpretation of \(E=mc^2\) Energy is shown to be displacement work against the substrate: \(E = V \cdot P_{\Delta C!}\). This dimensional consistency check links the Planck scale directly to observable physics. ### 3. The Knowledge Square (\(c^2\)) as Epistemic Boundary \(c^2\) is defined as an **epistemological event horizon**, marking the limit of what can be derived from within ΔL. The filter depth \(N_{\text{crit}} \approx 45\) is acknowledged as phenomenological, rooted in non-well-founded set theory. ### 4. Anti‑Navier‑Stokes Dynamics and Quantum Entanglement The negative effective viscosity in ΔM (\(\nu_{\text{eff}}<0\)) causes the medium to “snap into” correlated states – the mechanical origin of entanglement. Merger conservation laws explain why entanglement cannot transmit energy or information (no perpetual motion, no FTL signalling). ### 5. Matter as a Mechanical Traffic Jam Stable particles arise from a hierarchical cascade of vortex mergers, a fractal “traffic jam” that relieves substrate pressure. The critical depth \(N_{\text{crit}}\) marks the transition from transient to permanent structures. ### 6. Primordial 4‑8 Geometry and the Origin of Matter/Antimatter Under extreme pressure, the only stable vortex clusters are those with 4‑ or 8‑fold symmetry. Chirality (handedness) within these clusters gives rise to matter and antimatter as secondary properties. This explains the 2‑4‑8 multipole alignments in the CMB (“Axis of Evil”) as fossils of this primordial phase. ### 7. The 3D‑ħ – Quantum of Space We introduce the **three‑dimensional reduced Planck constant** \(\hbar_{3D} = \hbar / P_{\Delta C!}\), representing the fundamental quantum of volume. This reveals that \(\hbar\) itself is composite: \(\hbar = \hbar_{3D} \cdot P_{\Delta C!}\). The universe quantizes occupancy, not time. ### 8. Unified Explanation of Dark Energy and Dark Matter Dark energy is the residual pressure of ongoing mergers; dark matter is the hysteresis of the ΔM medium, explaining the Bullet Cluster and the lack of direct detection. ### 9. Experimental Signatures Predictions include variable speed of light near Planck scale, Mach cones in heavy‑ion collisions linked to substrate pressure, and specific multipole ratios in CMB. --- ——————————————————————————————————————— 2026-03-12 _ 1. Reverse-Engineering Validation Study of ΔC! ⇄ ΔM ⇄ ΔL-Framework from Known Boundaries / 2. Saving space 1. Purpose and Scope:This document does not claim to provide empirical proof of the ΔC! ⇄ ΔM ⇄ ΔL framework. Instead, it demonstrates that the core components of the framework can be independently derived through logical reverse-engineering from well-established physical limits of both General Relativity and Quantum Theory (such as the non-zero vacuum energy, the universality of rotati
This study investigates directional causality between Bitcoin and gold across different market conditions. Rather than relying on mean-based dependence, we examine how causal effects vary across return quantiles, investment horizons, and market regimes. To address this question, we apply a Causal–Frequency–Quantile–Regime (CFQR) framework. The approach combines frequency-domain Granger causality, quantile-based non-causality tests, and endogenous regime classification within a unified setting. Macroeconomic controls are included to reduce omitted variable bias. Statistical inference relies on bootstrap procedures with false discovery rate correction to account for multiple testing. Using daily data from 2013 to 2025, we find that the full-sample directional dominance between Bitcoin and gold is generally weak after multiple testing adjustments. However, under stress regimes, the causal relationship of gold to Bitcoin becomes more pronounced at longer investment horizons. Under normal conditions, causal effects remain unstable and fragmented. Economic effects are modest. Variance-based hedging gains are limited, while downside risk measures show moderate improvement during stress periods. Overall, the evidence suggests that gold does not serve as a universal hedge for Bitcoin, but may exert conditional informational influence during high-uncertainty states. The CFQR framework provides a structured way to identify such state-dependent causal patterns.
This study proposes a mobility control architecture that replaces conventional physical distance-based autonomous driving with a network-based logical spatiotemporal occupancy reservation system. Instead of relying solely on onboard sensors, vehicles reserve future movement regions as packetized occupancy rights across a communication infrastructure. A multi-layer consensus mechanism—combining efficiency, safety, and legal validation algorithms—determines vehicle behavior, while smart contracts and distributed ledgers record risk evaluation and liability allocation in real time. This approach enables high-density traffic flow beyond traditional braking constraints, improves road utilization efficiency, and enhances legal accountability in autonomous mobility networks. The framework also integrates infrastructure routing and fail-safe fallback mechanisms and relies on the Quantum Thought Circuit OS ASI architecture for deterministic decision control.
The subject of research covers the theoretical, methodological, and applied aspects of implementing blockchain technology and smart contracts into microgrid management systems, as well as the automation processes of energy resource exchange between participants of a distributed energy system. The purpose of this work is to investigate the methodological foundations for the application of blockchain and smart contracts in microgrids through the analysis of contemporary scientific research, systematization of approaches to consensus algorithm implementation, classification of smart contracts by application areas, and experimental verification of the proposed solutions. To achieve this goal, the following tasks were addressed: analyzing existing microgrid architectures and management methods; conducting a comparative analysis of consensus algorithms (PoW, PoS, PoA, PBFT, RAFT, etc.) regarding their applicability in private and public energy grids; developing a classification of smart contracts based on their application areas; and investigating software tools for implementing decentralized applications. Research Methods. The study employs system analysis methods to investigate microgrid architecture, comparative analysis to evaluate the efficiency of consensus algorithms, and classification methods for grouping smart contracts. For the practical part, computer modeling and experimental verification methods were used: smart contract development in Solidity, testing in the Remix IDE environment, and simulation of a local blockchain network using the Hardhat toolkit. Research results. The research systematized the methodological foundations for integrating blockchain into microgrids. It was determined that hybrid or private consensus models are most effective for energy trading within local communities. A classification of smart contracts was developed and justified, covering four levels: energy trading, monitoring, distributed management, and cybersecurity. The practical result is the implementation of the EnergyTrading smart contract, which successfully automates the process of listing offers and purchasing electricity, as confirmed by experiments in a local environment. The implementation of smart contracts allows for the creation of a reliable P2P platform for electricity trading without intermediaries, increasing economic efficiency for households. The functionality of the automated settlement mechanism was experimentally confirmed. At the same time, key challenges were identified: the limited scalability of existing blockchain solutions and the need to improve cyber defense against vulnerabilities in contract code. Further development requires adaptation of the legislative framework and modernization of the hardware components of energy grids.