Public discourse plays a critical role in shaping trust, legitimacy, and governance dynamics within decentralized Web3 ecosystems. However, existing studies often examine Web3 discourse through isolated lenses such as sentiment or topic modeling, which limits their ability to capture how emotional expression and communicative purpose jointly convey strategic intent. This study proposes a three-stage decision analytics framework that transforms unstructured Web3 discourse into diagnostic signals by jointly modeling industry domain, emotional tone, and communicative purpose. The analysis draws on 10,840 user-generated posts collected from X, Reddit, YouTube, and the ENS DAO forum, using a human-in-the-loop annotation process combined with transformer-based text classification models. The framework is evaluated using a domain-adapted language model and a general-purpose baseline, with robustness assessed through five-fold cross-validation. The results indicate that curiosity and optimism frequently align with promotional intent in infrastructure and application-oriented domains, whereas skepticism and concern are more prevalent in governance-related discourse. These findings demonstrate that emotional tone and communicative intent operate as structured, decision-relevant signals rather than incidental sentiment. The proposed framework supports systematic, diagnostic monitoring of narrative dynamics as decision support, enabling organizations, platform operators, and governance stakeholders to identify emerging legitimacy risks and shifts in community trust within decentralized environments.
Blockchain protocols often seek to procure computationally challenging work from a decentralized set of participants. While there are simple procurement auctions that result in the minimal cost of acquisition and maximal efficiency, they also lead to concentration in the provider set due to the winner-take-all market structure. We design and analyze single-good procurement auctions that balance social-cost minimization (at the extreme, a winner-take-all auction) with decentralization (at the extreme, a uniform allocation). We first give a dominant-strategy incentive-compatible (DSIC) mechanism explicitly designed to implement non-winner-take-all allocations. Our allocation rule uniquely solves an optimization with respect to a modified social-cost metric that penalizes large, single-player concentrations and is parameterized with a curvature value, $α$, with $α\rightarrow 0$ implementing the uniform allocation and $α\rightarrow \infty$ implementing the winner-take-all allocation. We further quantify the loss in social cost of this mechanism as a function of $α$. We then propose two alternative mechanisms, each addressing a limitation of the DSIC mechanism, namely a lack of Sybil-resistance and a complex payment rule. First, we examine a variation of Tullock contests to achieve a non-winner-take-all Sybil-proof procurement mechanism. Second, we consider a mechanism with the same allocation rule as the DSIC mechanism but with an alternative payment rule in which producers are simply paid proportionally to their bids. This provides a much simpler payment rule which, while not DSIC, still results in the mechanism being ex-post ``safe'' (where there exists a bidding strategy that is guaranteed to result in non-negative utility) for participating bidders. For both non-DSIC mechanisms, we characterize the equilibrium allocations and prove price of anarchy bounds.
Centralized stablecoins such as USDT and USDC enforce sanctions through contract-layer blacklist functions. Yet on public blockchains, a freeze is still an ordinary transaction competing with the sanctioned party's transfer for priority. It exposes a gap between contract-layer authority and ordering-layer enforcement: when both race for the same block, the outcome is set not by legal mandate, but by block producers' choices. Because both sides can pay for priority, sanction races create rents for block producers, which we call Sanction-Evasion MEV (SE-MEV). To measure this gap, we build the first longitudinal dataset of on-chain sanctions enforcement and evasion for Ethereum-based USDT and USDC from November 2017 to August 2025, covering more than $1.5 billion in frozen value. At least 7.3% of sanctioned USDT addresses and 18.7% of sanctioned USDC addresses had already been drained to zero before the freeze took effect. We also trace an escalation from issuer-side out-of-gas failures, to public gas auctions, private order flow, and direct payments to block producers, showing that block producers extract MEV from sanction enforcement. We then develop a game-theoretic model of stablecoin sanctions with MEV. It shows that compliant issuers cannot rationally stay outside the ordering market; fixed participation costs concentrate evasion among specialized MEV-aware adversaries; and the implicit MEV tax rises with regulatory penalties, creating incentives for vertical integration into block-building infrastructure. The problem extends beyond stablecoins. Any privileged on-chain action executed as an ordinary transaction -- emergency pauses, governance interventions, or judicial freezes -- faces the same conflict. Where ordering power follows economic incentives, ordering power is sanctioning power; contract-layer authority alone cannot guarantee enforcement.
Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning techniques. However, DNNs typically require large-scale labeled datasets to model the relationships between contract features and vulnerability labels. In practice, the labeling process often depends on existing open-sourced tools, whose accuracy cannot be guaranteed. Consequently, label noise poses a significant challenge for the accuracy and robustness of the smart contract, which is rarely explored in the literature. To this end, we propose Contrastive learning-enhanced Granular-Ball smart Contracts training, CGBC, to enhance the robustness of contract vulnerability detection. Specifically, CGBC first introduces a Granular-ball computing layer between the encoder layer and the classifier layer, to group similar contracts into Granular-Balls (GBs) and generate new coarse-grained representations (i.e., the center and the label of GBs) for them, which can correct noisy labels based on the most correct samples. An inter-GB compactness loss and an intra-GB looseness loss are combined to enhance the effectiveness of clustering. Then, to improve the accuracy of GBs, we pretrain the model through unsupervised contrastive learning supported by our novel semantic-consistent smart contract augmentation method. This procedure can discriminate contracts with different labels by dragging the representation of similar contracts closer, assisting CGBC in clustering. Subsequently, we leverage the symmetric cross-entropy loss function to measure the model quality, which can combat the label noise in gradient computations. Finally, extensive experiments show that the proposed CGBC can significantly improve the robustness and effectiveness of the smart contract vulnerability detection when contrasted with baselines.
Hasan Mahmud Rhidoy, Mahdi H. Miraz, Iftekhar Salam
The convergence of blockchain and the Internet of Things (IoT) enables secure, decentralised, and verifiable data exchange across distributed smart environments. However, traditional blockchain frameworks suffer from inherent scalability constraints, limited throughput, and high latency, which conflict with the stringent real-time requirements of IoT applications such as industrial automation, intelligent healthcare, and smart transportation. These systems demand ultra-low latency, high transaction throughput, lightweight computation, and efficient resource utilisation. This review provides a comprehensive, structured analysis of state-of-the-art scalability solutions specifically adapted to blockchain-enabled IoT. The discussion encompasses Layer 1 enhancements, Layer 2 off-chain processing, sharding-based parallelisation, integration of edge and fog computing, and hybrid consensus mechanisms. For each approach, the review highlights operational principles, performance benefits, trade-offs in decentralisation and security, and suitability for latency-sensitive deployments. Furthermore, real-time quality-of-service considerations are examined to understand how scalability strategies impact system responsiveness, energy efficiency, and data integrity. Key open challenges, including the scalability-security trade-off, privacy preservation, interoperability, and sustainable resource management, have been identified as persistent barriers to large-scale adoption. Finally, the review outlines future research directions, emphasising adaptive and AI-driven consensus algorithms, quantum-safe cryptographic models, the convergence of blockchain with 5G/6G networks, and edge intelligence. By consolidating diverse technical insights and emerging trends, this work serves as a timely reference for developing scalable, secure, and sustainable blockchain architectures for real-time IoT applications.
Abstract: This study investigates the dynamic relationships between Bitcoin, oil prices, and the US dollar (USD) using a Vector Autoregressive (VAR) model. Utilizing daily data from 2018 to 2023, the analysis reveals that both Bitcoin and oil prices exert significant short-term impacts on the USD, though these effects diminish over the long term. Bitcoin, characterized by its high volatility and safe-haven attributes, serves as an alternative asset during periods of economic uncertainty, while oil prices influence the dollar through trade flows and inflationary pressures. The findings highlight the transient nature of these interactions, with Bitcoin and oil acting as short-term pressure factors on the USD. These insights are crucial for investors and policymakers in managing risks and optimizing strategies in a volatile financial environment. This study contributes to the literature by providing empirical insights into the interconnectedness of cryptocurrencies, commodities, and currencies, offering valuable implications for financial decision-making. Keywords: Bitcoin, Oil Prices, US Dollar (USD), Vector Autoregressive (VAR) Model, Cryptocurrencies, Exchange Rates, Safe-Haven Assets JEL Classification Number: C32, E44, G15, Q43
Pre-registration of a structural scar class prediction for microsoft/phi-4 based on measurement-site stiffness (S = 0.0358), before the structural scar measurement is conducted. Predicts INTERMEDIATE class (1,000–4,000×ε non-max) based on the stiffness→scar ordering established across four families (Mistral, Llama, Qwen, Gemma) in Papers 1–12 and confirmed by RC-6 (DOI: 10.5281/zenodo.19305176). Designed as a hostile falsification test: Phi is trained with heavy synthetic-data distillation from GPT-4-class teachers, unlike any previously tested family. Explicit falsification criteria and hostile hypotheses defined. Part of the Fall Risk AI research program on neural network structural identity. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running 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) 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) 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).
Pre-registration of a structural scar class prediction for google/gemma-3-12b-it based on measurement-site stiffness (S = 0.1335), before the structural scar measurement is conducted. Predicts QUIET class (100–600×ε non-max) based on the stiffness→scar ordering established across three families (Mistral, Llama, Qwen) in Papers 1–12. Explicit falsification criteria defined. Part of the Fall Risk AI research program on neural network structural identity. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running 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) 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) 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).
This work presents a Quantum Measurement Units (QMU) ledger-based decomposition of the hydrogen $2\mathrm{S}$--$6\mathrm{P}$ transition, using the Aether Physics Model (APM) as a geometric framework for interpreting atomic structure. The analysis is anchored to the 2026 high-precision spectroscopic measurement yielding a proton charge radius of $r_p = 0.8406(15)\,\mathrm{fm}$. The hydrogen spectrum is reformulated as a perturbative expansion in the fine-structure constant $\alpha$ on a single base frequency scale $F_q = m_e c^2 / h$. The conventional decomposition into Dirac, radiative (Lamb shift), and finite-size contributions is translated into QMU ledger form using the Compton wavelength $\lambda_C = h/(m_e c)$ and the invariant relation $F_q \lambda_C = c$. A central result is the derivation of the proton finite-size frequency shift in QMU form:\[\Delta \nu_{\mathrm{finite}}(2S)=-\frac{\pi^2}{3}\,\alpha^4\left(\frac{m_r}{m_e}\right)^3\left(\frac{r_p}{\lambda_C}\right)^2F_q,\]where $m_r$ is the reduced mass. This expression is obtained by direct substitution from the conventional bound-state QED formulation using $\hbar = h/(2\pi)$ and $\lambda_C = h/(m_e c)$, preserving dimensional and scaling consistency. Inversion of this relation provides a direct extraction of the proton charge radius from the measured frequency shift, yielding agreement with experiment at the $10^{-3}\,\mathrm{fm}$ level when recoil is included through the factor $(m_r/m_e)^3$. Within the QMU framework, the finite-size correction is interpreted as a geometric traversal mismatch between the electron’s bound-state path and the proton’s distributed Aether structure. The proton radius emerges as a dimensionless geometric ratio $r_p/\lambda_C$, linking nuclear structure directly to the electron Compton scale without introducing additional fundamental lengths. The paper also establishes a ledger identity flow connecting the Aether unit closure relation\[A_u \cdot \mathrm{curl} = {F_q}^2 {\lambda_C}^2\]to the observed hydrogen transition frequency, demonstrating that atomic structure can be expressed as successive geometric perturbations of a single invariant frequency scale. Predictions include stability of the ratio $r_p/\lambda_C$ across hydrogenic systems, sensitivity of hyperfine structure to distributed charge anisotropy, and consistency between electronic and muonic hydrogen when reduced-mass effects are treated as a coupled inertial ledger. This work provides a geometrically unified interpretation of the proton radius within the QMU/APM framework and identifies experimental pathways for testing traversal-based effects in precision spectroscopy.
This paper presents a reformulation of the recently realized primary quantum current standard, based on the Josephson and quantum Hall effects, within the framework of Quantum Measurement Units (QMU) derived from the Aether Physics Model (APM). In conventional SI metrology, the current standard is expressed as$$I = \left(\frac{n}{p}\right) e f_J,$$where $f_J$ is the Josephson frequency and $n/p$ is determined by the quantum Hall state. While numerically accurate, this expression compresses magnetic flux geometry and charge representation into the constants $h$ and $e$. In QMU, electrical quantities are expressed in distributed charge, allowing the roles of frequency, conductance, and flux geometry to be separated explicitly. The Josephson--Hall system is shown to realize the identities$$potn = \frac{freq}{cond}, \qquad curr = \frac{potn}{resn}.$$ This leads to the central result that the quantum current standard is fundamentally a \textit{potential closure} governed by frequency and conductance geometry, rather than a direct charge-transport relation. Within this framework: The Josephson effect provides a frequency source $freq = f_J$. The quantum Hall effect defines a discrete conductance geometry. Potential emerges as $potn = freq/cond$. Current follows as $curr = potn/resn$. The resulting current relation becomes$$curr = \left(\frac{n}{p}\right) {e_\mathrm{emax}}^{2} f_J,$$which is the QMU form of the experimental result and represents a realization of the general QMU current definition$$curr = {e_\mathrm{emax}}^{2} F_q.$$ The formulation also shows that conductance is the reciprocal of magnetic flux,$$cond = \frac{1}{mflx},$$and that quantization arises from discrete geometric partitioning of flux. Because all quantities are expressed in distributed charge, no unit mismatch occurs, and the resulting relations remain real-valued. The use of complex impedance in conventional formulations is therefore interpreted as arising from combining quantities of different physical character rather than from a fundamental requirement. This work is intentionally limited to the reinterpretation of an experimentally realized system. It does not attempt to replace quantum mechanical descriptions or provide a full treatment of time-dependent circuit behavior. Instead, it demonstrates that the Josephson--quantum Hall current standard can be expressed as a consistent QMU ledger with explicit geometric meaning. The SI expression is recovered as a projection through charge conversion, while the QMU formulation foregrounds the underlying frequency--flux geometry governing the system.
Blockchain-based electronic voting systems that use zero-knowledge proofs (ZKPs) have been proposed as good candidates to provide both transparency and privacy of ballots. However, a fundamental challenge remains unmet in all existing schemes: the secure generation and protection of the voter's cryptographic secret key.In this paper, HME-KG (Hybrid Multi-Source Entropy Key Generation) is presented, a new credential derivation method which utilizes a cryptographically secure random salt, the national identity number of the voter and a per-device Client Device Secret (CDS) to derive a deterministic, brute-force-resistant secret key. HME-KG is integrated into BAVS-ZK, a complete anonymous blockchain voting framework employing AES-256-GCM encrypted credential storage, a Circom-based Groth16 zk-SNARK voting circuit, and on-chain nullifier verification via Ethereum Sepolia smart contracts. Security analysis demonstrates that HME-KG achieves voter determinism, cross-voter uniqueness, single-source failure resistance, and collision resistance under the security assumptions of SHA-256. Experimental evaluation on a 10,000-voter simulation confirms a 0.9998 scalability coefficient, 1.2-second proof generation, and 306,720 gas per vote—a 38.6% reduction compared to the Open Vote Network baseline. To the extent of current literature, BAVS-ZK is the first blockchain e-voting system to provide a complete, formally specified, and experimentally validated voter credential derivation and protection scheme.
Bitcoin has two cryptographic layers. SHA-256 secures mining and the hash chain — it has no periodicity, no rhythm, nothing for the quantum Fourier transform to detect. ECDSA secures signatures — it has rhythm, and Shor's algorithm breaks it. The foundation is quantum resistant. The signatures are not — but signatures are a software upgrade. The hash rate is not. Bitcoin is a shadow-mirror coupling: single hash (shadow, local, independent) coupled to blockchain (mirror, global, irreducible) through proof of work, with a self-regulating coupling constant κ ≈ 128,748 measured across 2,906 days and 12 orders of magnitude. Extended construction from Shadow & Mirror: Complementarity of Computation and Consciousness (Ross, 2026).
We present a novel application of the V-transform — introduced by the author in 1989 — to the theory of the Riemann zeta function. Assuming the Riemann Hypothesis (RH), we derive explicit closed-form expressions for the sums $$S_1=\sum_{k=1}^\infty\frac{1}{\gamma_k^2+1/4}, \qquad S_2=\sum_{k=1}^\infty\frac{1}{(\gamma_k^2+1/4)^2},$$ where $\gamma_k$ denotes the imaginary part of the $k$-th nontrivial zero of $\zeta(s)$. For $S_1$ we give a new proof of the classical formula first verified numerically by Keiper (1992). For $S_2$ we obtain the compact expression $$S_2 = 3 + (\ln 2\pi)^2 - 2\ln 2\pi + \ln\pi + \gamma - \frac{\pi^2}{24} + 2\zeta''(0),$$ with $\zeta''(0) = -2.006356\ldots$ the second derivative of $\zeta$ at $0$. This formula, while implicit in the sum-rule framework of Lehmer (1988) via the relation $S_2 = Z(2) + 2S_1$, does not appear explicitly in this form in the literature. Numerical verification against the first $10^5$ Odlyzko zeros confirms the result with a relative error of $0.04\%$. We further investigate the alternating analogues $$T_1=\sum_{k=1}^\infty\frac{(-1)^k}{\gamma_k^2+1/4}, \qquad T_2=\sum_{k=1}^\infty\frac{(-1)^k}{(\gamma_k^2+1/4)^2},$$ obtaining numerical values $T_1 = -0.003733\ldots$ and $T_2 = -0.000021774\ldots$ via direct summation. These lead naturally to two new constants $\eta_\pi'(0)$ and $\eta_\pi''(0)$, whose analytic nature — whether expressible in terms of known transcendental numbers or special values of $L$-functions — is left as an open problem. The method is entirely tabular: it reduces the Hadamard product factorisation of $\eta(s) = (s-1)\zeta(s)$ to elementary convolutions via the trinomial formula (PA-13) of the V-transform, requiring neither explicit knowledge of individual zeros nor the full Hadamard expansion.
EN — This paper introduces proof-object dissociation as an architectural principle for certification systems. In the major families of existing approaches reviewed here — including trusted timestamping, zero-knowledge proofs, token-based certification models, public key infrastructures, commitment schemes, and proof-of-existence mechanisms — the proof remains structurally tied to the object, secret, or entity whose validity is being established. This paper argues that such coupling should be understood as a dominant architectural convention rather than as a logical necessity. EN — Under proof-object dissociation, a certification reference structure may be generated, preserved, and anchored independently of any future certified object. Certification is then achieved through a controlled activation mechanism that associates an already valid reference structure with a specific object, user, or context. At the architectural level, this shift makes possible a set of properties that are difficult or unavailable in coupled models: pre-certification independent of the future object, deferred activation, mutation or transfer of activation rights without regeneration of the reference layer, and validation without disclosure of confidential source elements. EN — The paper positions this proposal relative to existing certification architectures, outlines a general implementation-agnostic framework, and identifies a further operational capability termed the Blind Pre-Certification Layer (BPCL). FR — Cet article introduit la dissociation preuve-objet comme principe architectural pour les systèmes de certification. Dans les principales familles d'approches existantes examinées ici — notamment l'horodatage de confiance, les preuves à divulgation nulle, les modèles de certification fondés sur des jetons, les infrastructures à clé publique, les schémas d'engagement et les mécanismes de preuve d'existence — la preuve demeure structurellement liée à l'objet, au secret ou à l'entité dont la validité est établie. L'article soutient que ce couplage doit être compris comme une convention architecturale dominante plutôt que comme une nécessité logique. FR — Sous dissociation preuve-objet, une structure de référence de certification peut être générée, conservée et ancrée indépendamment de tout objet futur certifié. La certification est ensuite réalisée par un mécanisme d'activation contrôlée qui associe une structure de référence déjà valide à un objet, un utilisateur ou un contexte spécifique. Au niveau architectural, ce déplacement rend possible un ensemble de propriétés difficiles à obtenir ou absentes dans les modèles couplés : pré-certification indépendante de l'objet futur, activation différée, mutation ou transfert des droits d'activation sans régénération de la couche de référence, et validation sans divulgation des éléments confidentiels sources. FR — L'article situe cette proposition par rapport aux architectures existantes de certification, présente un cadre général agnostique quant à l'implémentation, et identifie une capacité opérationnelle supplémentaire nommée Couche de Pré-Certification Aveugle (BPCL).
Open Knowledge Archive: Proposal for Merit-Based Interdisciplinary Preprint Server Description This proposal outlines a novel approach to open scientific publishing that addresses three critical failures in the current academic system: institutional gatekeeping, disciplinary fragmentation, and binary validation models. Key Innovation: A concentric ring architecture where papers enter freely at the outer rim (zero gatekeeping) and move inward based on accumulated validation. The center represents maximum interdisciplinary verification—where fields converge rather than separate. The Problem: Traditional journals reject AI-assisted research based on writing style, not content quality Independent researchers face systematic barriers (institutional email requirements, credential-based filtering) Interdisciplinary work falls between disciplinary silos Binary accept/reject model provides no pathway for progressive validation The Solution: Ring-based validation (outer rim = unverified, inner rings = progressively validated) Domain slices (papers positioned by subject area, can span multiple fields) Merit determines position, not credentials or authorship method Completely open access (readers never pay) Implementation: Phase 1: $8,000 proof of concept using AI-assisted development (Claude Code)Sustainable through hybrid revenue model (minimal per-paper fees, institutional partnerships, donations) All code released under AGPL-3.0 (prevents proprietary forks) Structured as irrevocable non-profit (cannot be commercialized) This proposal is released under CC BY-NC-SA 4.0 to prevent commercial exploitation while enabling open collaboration. It represents infrastructure for the commons—research validation that serves knowledge, not profit. Includes: 1. Full architectural specification (ring/slice topology, validation pipeline) 2. Technical implementation plan (backend, frontend, moderation system) 3. Governance model (non-profit structure, anti-gaming provisions) 4. Financial sustainability model (revenue sources, cost projections) 5. Phase 1 budget ($8,000 for AI-assisted development) Status: Proposal stage. Seeking collaborators, volunteers, and initial funding.
The exponential growth of digital data has intensified reliance on cloud storage, yet conventional centralized architectures remain persistently vulnerable to unauthorized access, data tampering, and privacy violations. This paper presents a novel blockchain-driven approach for decentralized cloud storage that addresses these concerns through a multi-layered security mechanism. The proposed system fragments a user’s file into multiple independent blocks, encrypts each block using the AES-256 algorithm with PBKDF2-derived keys, and distributes them across distinct nodes of the InterPlanetary File System (IPFS). The corresponding IPFS hash addresses are then recorded on an Ethereum-based blockchain through a Solidity smart contract, ensuring immutability and tamper-resistance of the entire storage index. During file retrieval, the system queries the blockchain to collect all block hash addresses, fetches encrypted blocks from IPFS, reassembles them in the correct sequence, and delivers the decrypted output to the user. The implementation is built using Python, Django, Web3.py, and the Truffle/Ganache development environment, and has been functionally verified across all core user-facing modules.
Intherapidlyevolvingdigitallandscape,freelancing platforms face significant challenges due to a lack of transparency,trust,andcentralizedcontrol.Thispaperpresents the design and implementation of a blockchain-powered web- based project management system integrated with a visual data dashboard. The proposed system leverages Ethereum smart contractstoensuresecure,tamper-proofuserregistration,project posting, bidding,assignment, work submission, payment release, and rating. The backend is developed using Django, while blockchain integration is achieved via Web3.py, enabling secure and transparent interactions. The platform provides real-time analyticsonusers,jobstatus,fundmovement,andratingsthrough a dashboard. The solution enhances trust, transparency,and de- centralization,provingeffectiveforfreelanceprojectecosystems
Blockchain technology and decentralised finance (DeFi) are reshaping financial services by eliminating intermediaries, automating transactions through smart contracts, and expanding global access to capital. Initially designed for cryptocurrencies, blockchain has evolved into a transformative ecosystem that optimises resource management and democratises finance. This study explores the impact of blockchain and DeFi on financial services, focusing on adoption opportunities and challenges. It addresses key gaps in the literature, particularly platform interoperability, security in decentralised environments, and adoption in emerging markets. Using the PRISMA 2020 methodology, the research ensures a rigorous selection and critical evaluation of scientific articles to identify trends, barriers, and potential developments. Findings indicate that blockchain and DeFi can enhance financial inclusion, improve transparency, and strengthen decentralisation. However, they also present challenges such as regulatory uncertainty, technical complexity, and security risks. Overcoming these obstacles requires innovative solutions and strategic collaboration among governments, financial institutions, and technology developers. By shedding light on these dynamics, the study contributes to a deeper understanding of how blockchain and DeFi can reshape financial services, paving the way for a more inclusive, efficient, and secure financial ecosystem.
The rise of Decentralized Autonomous Organizations (DAOs) has contested conventional concepts of state sovereignty and political legitimacy based on the Westphalian order. Decentralized Autonomous Organizations (DAOs) function on blockchain networks, facilitating self-governance, collaborative decision-making, and resource distribution devoid of centralized control. This article examines the potential for DAOs to be acknowledged as politically legitimate sovereign organizations by comparing their governance frameworks to traditional state structures. This analysis utilizes international law, political philosophy, and blockchain governance literature to assess the ramifications of virtual nations on legal recognition, legitimacy, and the prospects of decentralized government.
This study aimed to examine the role of blockchain technology in transforming digital marketing within the emerging Web3 environment, with a specific focus on its impact on consumer trust and marketing efficiency. The research primarily investigated the relationship between blockchain technology as the independent variable and digital marketing transformation as the dependent variable, while also considering consumer trust and marketing efficiency as key outcome variables. A quantitative research design was adopted, and data were collected from 250 respondents, including digital platform users and marketing professionals, using a structured questionnaire based on a 5-point Likert scale. Statistical analyses, including reliability, correlation, and regression, were conducted to test the proposed hypotheses and evaluate the relationships among variables. The findings revealed that blockchain technology had a significant positive impact on digital marketing transformation (β = 0.68, p < 0.001) and consumer trust (β = 0.72, p < 0.001). Digital marketing transformation significantly influenced marketing efficiency (β = 0.70, p < 0.001). The results also indicated that consumer trust played a mediating role in enhancing marketing efficiency, highlighting the importance of transparency and data security in digital environments. The study provides practical implications for organizations by emphasizing the adoption of blockchain-based marketing systems to improve transparency, reduce operational inefficiencies, and strengthen consumer trust. It also highlights the strategic importance of decentralized platforms in shaping the future of digital marketing. References Basal, M., & Şarkbay, Ö. F. (2026). Web3-driven digital marketing and consumer protection. 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G. Prabakaran, Dharani Priya M, Jumana Fathima S, Keerthini R · 5 authors
The pharmaceutical supply chain is complex and often faces challenges such as lack of transparency, counterfeit medicines, and inefficient tracking. This project proposes a blockchain-based pharmaceutical supply chain management system using smart contracts to ensure secure and transparent operations. Each medicine batch is recorded on the blockchain with a unique ID, allowing all transactions from manufacturer to pharmacy to be tracked in a tamper- proof manner. The system also enables patients to verify the authenticity of medicines and detect counterfeit products. Additionally, a drug recall feature allows manufacturers to mark defective batches, preventing further distribution. By using blockchain technology, the proposed system improves traceability, enhances security, and ensures trust among all participants in the supply chain
Money laundering in cryptocurrency networks poses persistent challenges for financial intelligence units due to the pseudo-anonymous architecture of blockchain systems and the limited effectiveness of conventional rule-based detection methods. This study introduces chaos theory and recurrence quantification analysis (RQA) as a novel framework for characterizing temporal behavioral dynamics in Bitcoin money laundering transactions. Analyzing 46,564 labeled transactions from the Elliptic Bitcoin Dataset spanning 2009-2018, we construct aggregate time series for illicit and licit transaction volumes across 49 discrete temporal steps, corresponding to the dataset’s inherent graph-based snapshot structure, and apply phase space reconstruction techniques to compute three RQA metrics: determinism (DET), laminarity (LAM), and entropy (ENTR). Results reveal paradoxically higher determinism in illicit transactions (38.24% vs. 16.67% for licit), substantially elevated laminarity (35.80% vs. 0.00%), and greater entropy (0.45 vs. 0.00%), indicating that sophisticated obfuscation strategies inadvertently introduce detectable deterministic signatures. Augmenting conventional graph-based features with RQA metrics significantly enhances Random Forest classification performance, reaching near-optimal levels (F1 = 1.000, AUC = 1.000) within the evaluated dataset environment, with entropy emerging as the single most discriminative predictor. While these exceptional results reflect the high fidelity of chaos-based features in capturing structured laundering patterns from this period, they serve as a benchmark for the theoretical potential of nonlinear analysis in blockchain forensics. These findings demonstrate that temporal complexity features offer a powerful diagnostic tool for real-time monitoring and detection of systemic financial crime in evolving cryptocurrency ecosystems.
Reasoning distillation does not leave uniform traces across target base families. In the current sample, we measure structural and functional identity across reasoning-distillation derivatives in three base-architecture families (Llama, Qwen, and Mistral) at five model scales (1.5B to 70B). Structural displacements are family-graded: Mistral-family targets show scars of 7,701–8,518 times the acceptance threshold, Llama-family targets show 2,858–4,583 times, and Qwen-family targets show 141–516 times — a sixty-fold range across three families, with the third-family result persisting under an independently trained derivative using different training data. Functional consequences do not track structural magnitude uniformly: Llama derivatives show decisive functional hierarchy breaks, Qwen derivatives remain within their base neighborhood, and Mistral — despite having the loudest structural scar — shows only marginal functional displacement. The functional departure is low-rank at every tested scale but varies in character: G₁-dominant in Llama and Qwen families, with a sign-oscillating morphology in Mistral that suppresses centroid-level G₁ signal while preserving per-prompt dominance. The stiffness parameter at the measurement site is inversely ordered with structural scar magnitude across all three families. Fisher curvature, previously proposed as a candidate mechanism at small scale, does not order scar magnitudes correctly at production scale across families. These findings change how derivative identity claims should be interpreted: the expected displacement depends on the architectural context of the distillation, and the structural and functional layers can decouple — a model may show the loudest structural scar in the dataset while absorbing the functional perturbation. 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).