Blockchain Papers

Imtinokshang, Anand Stalin, Muhammad Ziyan, J Thangakumar

No abstract is available for this record.

Muhammad Ayub

This chapter explores the transformative role of fintech, blockchain, and cryptocurrency in advancing ethical finance, with a focus on Islamic financial principles. It examines how technologies like distributed ledger technology and smart contracts can enhance transparency, efficiency, and financial inclusion while adhering to sharīʿah prohibitions against ribā, gharar, and maysir. The discussion highlights key fintech applications, including crowdfunding, digital waqf, and precious metal-backed cryptocurrencies, which align with Islamic finance’s emphasis on asset-backed and risk-sharing models. Case studies from Malaysia, Saudi Arabia, Indonesia, and other Organization of Islamic Cooperation countries illustrate the growth of Islamic fintech ecosystems. The chapter also addresses regulatory challenges and the need for robust frameworks to ensure ethical compliance and systemic stability. By integrating fintech with maqāṣid al-sharīʿah (higher purposes of Islamic law), Islamic finance can promote social justice, sustainability, and equitable resource distribution, offering a viable alternative to conventional financial systems.

Smt. Sarita Ajinkya Date, Smt. Shital Sachin Kare

Blockchain technology has emerged as one of the most disruptive forces in modern computing, fundamentally reshaping how digital ecosystems manage trust, transparency, and decentralization. This paper presents a comprehensive analysis of blockchain architecture—covering distributed ledger mechanisms, consensus protocols, and smart contract frameworks—and examines their transformative impact across domains including finance, healthcare, supply chain, and governance. A secondary theme of the paper explores the growing role of Artificial Intelligence (AI) as a convergent and supporting technology to blockchain, particularly in areas of anomaly detection, intelligent contract automation, and predictive analytics. Using a structured literature review methodology, we identify key architectural components, survey real-world applications, evaluate current limitations, and outline future research directions. Findings suggest that while blockchain independently offers significant systemic advantages, its integration with AI amplifies scalability, security, and decision-making capabilities—heralding a new paradigm for digital infrastructure.

Sujan Mondal, Ankita Ray, Sorokhaıbam Khaba

Blockchain technology has revolutionized various industries by offering transparency, security, and decentralization. The critical aspect of blockchain technology is the consensus protocol, which plays a pivotal role in ensuring the integrity and reliability of distributed ledger systems. The selection of an appropriate consensus protocol for a given blockchain application is a complex and multifaceted decision-making process, influenced by various technical, environmental, and operational factors. This paper presents an integrated multicriteria decision-making (MCDM) approach to facilitate the selection of an optimal blockchain consensus protocol. Through a comprehensive evaluation of criteria, including performance, sustainability, incentives, security, and decentralization, our approach provides a robust decision-making framework for consensus protocol assessment. The results prioritize the importance of performance and security factors in blockchain consensus protocol evaluation. The sensitivity analysis is performed to determine the impact of experts’ weight coefficients on the result. The results prioritize the importance of performance and security in blockchain consensus protocol selection.

John Rhodes

We prove that for planted k-SAT instances with k >= 7 at clause density alpha/alpha_s >= 0.21, a positive fraction of variables are frozen directly in the planted model---without requiring transfer from the random model via quiet planting. The expected number of "support clauses" per variable (clauses in which that variable is the unique satisfying literal) exceeds 1 at remarkably low density: alpha/alpha_s ~ 0.20 for k = 7, compared to the random-model freezing threshold at alpha_f/alpha_s ~ 0.90. We prove that the resulting frozen-core structure implies topological disconnection of the solution subgraph across cluster boundaries, with a cycle-robustness argument showing that short cycles in the factor graph cannot quench the supercritical repair cascade. As an immediate corollary, the Hilbert space spanned by satisfying assignments decomposes into orthogonal sectors preserved by any unitary generated by the adjacency matrix---blocking quantum walks, QAOA at all depths, and quantum annealing. We construct a post-quantum commitment scheme whose binding property reduces to the hardness of solving planted k-SAT, provide formal proofs of completeness, soundness, and zero-knowledge, and derive a digital signature scheme with existential unforgeability via the Fiat-Shamir transform. We present a six-vector quantum attack analysis with proved barriers against five algorithmic families. We give concrete parameter recommendations at NIST security levels 1, 3, and 5, and position the scheme within the landscape of SAT-based and CSP-based cryptographic constructions. We prove that the Grover query complexity for breaking the binding property is Omega(2^{fn/2}); empirical cryptanalysis of Glucose and MiniSat CDCL solvers on our exact distribution yields a classical attack cost of 2^{0.234n} operations, enabling concrete parameter selection at NIST security levels 1, 3, and 5. Empirical validation across 100 random seeds at n = 16 confirms complete cluster isolation at every instance tested.

Bradford White

This preprint presents Invariant Ontodynamics (IOD), a structural field theory derived from a single minimal geometric primitive with zero continuously adjustable dimensionless fit parameters. To our knowledge, no prior framework derives both the Schrödinger equation and the Einstein field equations from a single uniqueness-selected geometric primitive without continuously adjustable fit parameters. The theory derives quantum dynamics, relativistic field structure, fermion spin-½, general relativity, and gauge symmetry as theorems rather than assumptions. A universal structural law — that the effective complexity of any system is a linear function of its structural curvature k, with a universal slope and fixed point derived from the same primitive — is empirically confirmed at R² = 0.978 across 15 pre-selected independent domains spanning 19 orders of magnitude in physical scale, under a pre-registration protocol with SHA-256 cryptographic locks. New results in this version include: A zero-free-parameter prediction of the Higgs boson mass, m_H = 125.33 GeV (0.06% from the observed 125.25 GeV), via a one-loop renormalization group trajectory anchored at a structurally derived UV scale A complete CPL dark-energy equation-of-state parameter pair (w₀ = −0.858, w_a = −0.411), both pre-registered before DESI DR3 Exact zero-free-parameter black hole thermodynamics: Schwarzschild radius, Hawking temperature, and surface gravity all derived from the primitive alone, with a falsifiable 29% Hawking temperature shift relative to the GR prediction A structural information measure (Heun log-coefficient) connecting the near-horizon field structure to the Brownian fixed-point evaporation endpoint, with exact Page curve endpoint M_Page = M₀/√2 Previously confirmed predictions — solar mixing angle (0.05σ), reactor angle (0.39σ), tau lepton mass (0.91σ), baryon asymmetry (−1.0σ), dark matter ratio (0.2%), inflationary spectral index (1.0σ) — remain confirmed. Three explicit tensions are stated without omission: atmospheric mixing angle (2.2σ, DUNE 2030 decisive), leptonic CP violation (J_CP = 0, DUNE 2030 decisive), and dark energy w₀ (0.4σ from DESI DR2 best fit, DESI DR3 decisive). Priority and legal status: This document is a public technical summary and priority disclosure. Full derivations, exact primitive specification, all coefficient values, and complete proofs are in US Provisional Patent No. 63/963,472 (filed January 2026) and Addenda 1–15 (through April 2026). The non-provisional application will be filed by January 2027.

Zilin Huang, Zhengyang Wan, Zihao Sheng, Boyue Wang · 6 authors

Vision-language-model (VLM)-guided reinforcement learning (RL) has recently attracted significant attention for it, replacing brittle hand-crafted rewards with semantically grounded signals; however, deploying such simulation-trained policies on real vehicles remains a fundamental challenge, because they rely on simulator-native observations and simulator-coupled action semantics with no counterpart on physical hardware. We identify a general principle: the simulation-to-reality gap decomposes into two largely orthogonal axes, a sensing-and-dynamics domain gap and a task-and-geometry gap, the former closable without real-world policy training by re-projecting real perception and control onto the policy's training manifold. We formalize this as a transfer guarantee that bounds the deployment gap by three independently controllable error terms, and instantiate it as Sim2Real-AD, which combines a Geometric Observation Bridge, a Physics-Aware Action Mapping, a Two-Phase Progressive Training curriculum, and a Real-time Deployment Pipeline. As a proof of concept, a CARLA-trained VLM-guided RL policy is transferred zero-shot to a full-scale battery-electric Ford E-Transit van in Madison, WI, USA, and drives across car-following, obstacle-avoidance, and stop-sign scenarios using no real-world training data. To our knowledge, this is among the first zero-shot closed-loop deployments of a CARLA-trained VLM-guided RL policy on a full-scale real vehicle, and the decomposition offers a principled, broadly applicable route for moving simulation-trained, foundation-model-guided policies into the physical world, supporting energy-efficient intelligent driving on electrified transportation platforms. The demo video, code, and model checkpoint are available at: https://zilin-huang.github.io/Sim2Real-AD-website/.

A. M. F. L. M., B.P. Hegde, Bhaskarjyoti Das

No abstract is available for this record.

Olivia BROWN, Gavin Roberts

A growing number of firms are acquiring large positions in Bitcoin and other digital assets, raising questions about how cryptocurrency exposure affects financial risk. We develop a framework to evaluate the credit risk associated with holding cryptocurrency on corporate balance sheets. Using Bitcoin prices and option-based valuation, we construct pseudo-bonds, synthetic debt instruments backed by digital assets, to calculate yields, leverage ratios, and default probabilities. Cryptocurrency pseudo-bond yields are extremely high and volatile, reflecting cryptocurrency price dynamics. Using daily data from annual samples from 2020 to 2024, we examine correlations between changes in pseudo-bond yields and changes in corporate bond yields across leverage levels. Correlations are generally small once leverage approaches one and above, indicating weak short-run co-movement between crypto-linked credit risk innovations and traditional credit conditions at economically meaningful leverage levels. Our pseudo-bond framework is intended as a transparent screening and comparison device for expressing crypto exposure in credit-market terms.

Dudhal Shrikant Chandrakant

Monetary technology (FinTech) represents the integration of era into financial services to enhance performance, accessibility, transparency, and purchaser revel in. over the last decade, FinTech has disrupted conventional banking structures, charge mechanisms, investment control, insurance, and lending practices. innovations along with blockchain, synthetic intelligence (AI), digital payments, peer-to-peer lending, and decentralized finance (DeFi) have reshaped the monetary panorama. This paper explores the evolution of FinTech, key technological improvements, economic and regulatory implications, dangers and challenges, and destiny potentialities. The study concludes that whilst FinTech fosters financial inclusion and operational efficiency, it also introduces regulatory, cybersecurity, and systemic dangers that require coordinated global governance frameworks.

Abdulrahman Alsamaani, Huda Aldhahi

This study provides a comprehensive evaluation of six volatility forecasting models applied to twelve dominant and less dominant cryptocurrencies across multiple time horizons using high-frequency intraday data. The exponential generalized autoregressive conditional heteroskedastic (EGARCH), integrated GARCH (IGARCH), standard GARCH, GJR-GARCH, lagged realized volatility (LRE), and heterogeneous autoregressive (HAR) models are systematically compared using 5 min computed return data from September 2018 to September 2020. Our analysis encompasses three forecast horizons (1-day, 7-day, and 30-day) to assess model performance under varying temporal constraints. Through univariate Mincer–Zarnowitz regressions, encompassing tests, and out-of-sample evaluation using root mean squared error (RMSE) and quasi-likelihood loss (QLIKE) functions, we identify significant performance heterogeneity across models and cryptocurrencies. The HAR model exhibits stronger predictive accuracy at short horizons, while EGARCH exhibits relatively stronger performance at longer horizons, although overall explanatory power declines as forecast horizon increases. Importantly, no single model consistently provides optimal forecasts across all cryptocurrencies. Consistent with prior evidence suggesting model performance varies across assets. Encompassing regressions reveal that combining HAR with EGARCH specifications significantly enhances explanatory power across all temporal frames. Out-of-sample Diebold–Mariano tests indicate that HAR generates the lowest forecast errors for most cryptocurrencies, though EGARCH performs exceptionally well for high-market-capitalization assets. These findings provide regime-conditional insights into horizon- and asset-specific volatility dynamics during the pre-institutionalization phase of cryptocurrency markets. The study contributes to emerging literature by incorporating less-dominant cryptocurrencies and offering robust empirical evidence on the asymmetric and persistent volatility characteristics unique to digital asset markets. These findings should be interpreted within the context of the 2018–2020 sample period, representing a pre-institutionalized phase of cryptocurrency markets, and may not fully generalize to structurally different market regimes characterized by increased institutional participation and regulatory development.

E Chen, Xuanyu Liu, Limin Jia, Bo Liang · 6 authors

The widespread adoption of smart contracts, self-executing agreements on the blockchain, is hindered by the complexity of translating real-world contracts, often written in multiple languages, into their digital counterparts. This paper addresses this challenge by introducing an innovative approach based on Contract Text Markup Language (CTML), an extensible markup language specifically designed to facilitate the automatic generation of smart contracts from multilingual contracts. CTML overcomes traditional method limitations by employing a two-stage transformation process: (1) Contract Abstraction and Markup: CTML redefines grammar rules and incorporates encoding extensions to transform multilingual contracts into structured, marked-up contracts. This process effectively abstracts the essential details of the original contract, enabling language-agnostic interpretation. (2) Domain-Specific Language (DSL) Translation and Smart Contract Code Generation: The marked-up contract is then seamlessly translated into a DSL program, capturing the legal concepts in a machine-readable format. Finally, the DSL program is automatically compiled into executable smart contract code, ready for deployment on the blockchain. The effectiveness of the proposed approach is demonstrated using a legal contract in both English and Chinese. Therefore, the CTML-based approach can automatically generate smart contracts from multilingual contracts, enabling a more inclusive and accessible smart contract ecosystem.

Alan G. Futerman, Ernesto Edwards

This paper explores the evolving landscape of digital currencies, focusing on the contrasting characteristics and implications of Bitcoin, stablecoins, and Central Bank Digital Currencies (CBDCs). While Bitcoin emerged as a decentralized, privacy-focused alternative to traditional financial systems, CBDCs represent a centralized approach to digital money, potentially enabling unprecedented levels of government surveillance and control. Through an analysis of the fundamental differences between these forms of currency, the paper highlights the risks associated with CBDCs, including threats to individual privacy, financial autonomy, and the potential for regulatory overreach. The study also examines the potential consequences of CBDC adoption, such as programmable money and its implications for economic freedom, while considering the broader impact on the global financial system. Ultimately, this paper argues that while CBDCs are often promoted as a more efficient and secure means of digital transactions, they pose significant dangers that could undermine the principles of decentralization and privacy championed by cryptocurrencies like Bitcoin.

Wenqi Li, Zijie Pan, Yufeng Yang

Payment channel networks enable scalable off-chain payments, but their practical deployment remains constrained by a persistent tension among routing efficiency, liquidity visibility, transaction privacy, and settlement security. Existing multipath routing mechanisms can improve payment success under fragmented liquidity, yet they often expose sensitive balance information, leak structural features of payment routes, and enlarge the attack surface for probing, channel exhaustion, and selective forwarding. This paper presents a novel framework, Adaptive Multipath Proofs (AMPs), for privacy protection and security in payment channel networks. The core idea is to bind multipath routing decisions with lightweight zero-knowledge verifiability, allowing intermediate nodes to validate path feasibility, fragment consistency, and settlement constraints without learning exact channel balances, the complete payment amount, or the global route structure. AMP integrates three mechanisms: a hidden-liquidity feasibility proof that supports privacy-preserving route selection, an adaptive payment-splitting strategy that dynamically determines fragment allocation according to network congestion and balance uncertainty, and a proof-coupled settlement guard that enforces atomicity and timeout consistency across all payment fragments. Together, these mechanisms reduce information leakage while preserving robust payment execution under dynamic network conditions. Experimental evaluation on real Lightning Network topologies and synthetic stress scenarios demonstrates that AMP significantly lowers balance disclosure and endpoint inference risk, improves payment completion under skewed liquidity distributions, and introduces only moderate computational and communication overhead. The results indicate that adaptive proof-carrying multipath routing offers a practical and effective direction for building secure, privacy-preserving, and high-success payment channel networks.

Sathya Priya J, Harshini M, Swathi S

In recent years,Land registration systems in many countries faces various challenges such as duplicate property, forgeries in documents, ownership conflicts, and unauthorized resale of property. Traditional systems are vulnerable to human errors, tampering of data, corruption which leads to lack of transparency and trust between buyer and seller. To overcome these issues,blockchain technology offers an immutability and transparency land registration system, but blockchain alone fails to ensure ownership integrity during transfer. This paper proposes a Blockchain and Non-Fungible Token (NFT)-based land registration system which ensures unique ownership of property , prevents duplicate registration of land , and reduces fraudulent during transactions. Each land parcel is represented as a unique NFT deployed on a blockchain network, which acts as an blockchain-based land title. Smart contracts enables ownership verification and allow land transfer only by the authorized owner.Land- related documents are stored using the InterPlanetary File System (IPFS), ensuring data integrity and decentralized storage. The proposed system ensures security, transparency and tamper proof while allowing ownership transfer. Experimental implementations and evaluation of implementations establish that the system effectively prevents duplicate land titles, fake ownership of property , and resale of already sold land. The solution provides a scalable and reliable approach for modernizing land registry systems using decentralized technologies.

Mathilde Dufouleur

Abstract: This paper examines how national cryptocurrency regulations affect cross-country Bitcoin price segmentation, local prices, and traded volumes. Using daily data for 22 countries since 2013, we apply a dynamic fixed effects framework to deviations from the law of one price (LOP), controlling for country-specific barriers and global shocks. We distinguish between regulatory frameworks that enhance market functioning (e.g., securities laws, payment system integration, regulatory sandboxes), pro-innovation policies, restrictive measures (e.g., banking bans), and anti-money laundering/countering the financing of terrorism (AML/CFT) rules. Our results show that comprehensive and pro-innovation frameworks reduce price deviations from the USD benchmark, lower local prices, and increase traded volumes, while banking bans fragment markets, depress prices, and reduce volumes. AML/CFT laws exert a consistent downward effect on prices regardless of global conditions. Threshold Auto-Regressive (TAR) models further reveal that highly regulated countries—whether supportive or restrictive—are more sensitive to macro-financial factors such as capital account openness, inflation, relative traded volumes, and remittances, indicating tighter links to the broader financial system. These findings suggest that regulation not only shapes domestic market conditions but also alters the transmission of global and macro-financial shocks into cryptocurrency markets.

Yancheng Fan, Dimitris Assimakopoulos, Yantai Chen, Elias G. Carayannis

No abstract is available for this record.

Firepan Inc

The report finds that Web3 protocols lost an estimated $3.3 billion to exploits in 2025, underscoring systemic challenges in how smart contract security is approached. Notably, nearly half of the exploited protocols had previously undergone security audits, raising concerns about the effectiveness of audits as a primary line of defense. In addition, the report estimates that more than 80% of deployed smart contracts have never been audited, leaving a significant portion of the ecosystem exposed to vulnerabilities. "Web3 didn't fail because of bad code - it failed because of a broken security model," said Ian Kane, Co-Founder of Firepan. "Smart contracts are dynamic systems, but audits are static. That mismatch is being exploited at scale." Key Findings $3.3 billion lost to Web3 exploits in 2025 80%+ of smart contracts have never been audited Nearly 50% of exploited protocols had previously undergone audits Rapid growth in AI-assisted attack methodologies Audits and the Rise of AI-Driven Attacks According to the report, the industry's reliance on point-in-time audits is increasingly misaligned with how modern attacks are executed. While audits provide valuable insights at a specific moment, smart contracts continue to evolve after deployment, creating new potential vulnerabilities. At the same time, attackers are leveraging automation and AI to identify and exploit weaknesses more quickly and at greater scale than ever before. "Attackers are already using AI to identify vulnerabilities in minutes," Co-founder Gerrit Hall added. "Meanwhile, most teams rely on audits that were completed weeks or months earlier." Proprietary Analysis Highlights Persistent Risk The report also includes findings from Firepan's internal analysis using its HOUND scanning engine. In a sample of previously audited smart contracts, Firepan identified 17 exploitable vulnerabilities in contracts labeled as "safe" by third-party auditors. In several cases, these contracts had undergone multiple audits prior to analysis. These findings suggest that while audits remain an important component of security, they may be insufficient as a standalone solution in rapidly changing environments. Toward Continuous, AI-Driven Security Firepan's report concludes that Web3 security must evolve from static assessments to continuous monitoring and detection. Rather than replacing audits, the report recommends supplementing them with systems that: Continuously scan codebases and deployed contracts Integrate directly into developer workflows Detect vulnerabilities prior to deployment Adapt to emerging attack patterns in real time "Audits are not going away," said Gerrit Hall. "But treating them as the primary layer of defense is no longer sufficient in an environment where threats are continuous."

Mohd Safwan Uddin, Mohammed Mouzam, Mohammed Imran, Syed Badar Uddin Faizan

Autonomous agents are moving beyond simple retrieval tasks to become economic actors that invoke APIs, sequence workflows, and make real-time decisions. As this shift accelerates, API providers need request-level monetization with programmatic spend governance. The HTTP 402 protocol addresses this by treating payment as a first-class protocol event, but most implementations rely on cryptocurrency rails. In many deployment contexts, especially countries with strong real-time fiat systems like UPI, this assumption is misaligned with regulatory and infrastructure realities. We present APEX, an implementation-complete research system that adapts HTTP 402-style payment gating to UPI-like fiat workflows while preserving policy-governed spend control, tokenized access verification, and replay resistance. We implement a challenge-settle-consume lifecycle with HMAC-signed short-lived tokens, idempotent settlement handling, and policy-aware payment approval. The system uses FastAPI, SQLite, and Python standard libraries, making it transparent, inspectable, and reproducible. We evaluate APEX across three baselines and six scenarios using sample sizes 2-4x larger than initial experiments (N=20-40 per scenario). Results show that policy enforcement reduces total spending by 27.3% while maintaining 52.8% success rate for legitimate requests. Security mechanisms achieve 100% block rate for both replay attacks and invalid tokens with low latency overhead (19.6ms average). Multiple trial runs show low variance across scenarios, demonstrating high reproducibility with 95% confidence intervals. The primary contribution is a controlled agent-payment infrastructure and reference architecture that demonstrates how agentic access monetization can be adapted to fiat systems without discarding security and policy guarantees.

Prof. Ambreen Anees, Alisha Abbasi Shaikh, Aman Ullah Khan, Mohammad Fahad Kirmani · 5 authors

The rapid digital transformation of healthcare systems has significantly improved the storage, accessibility, and management of patient information; however, it has also introduced serious challenges related to data security, privacy, and trust. Traditional centralized medical record systems are vulnerable to single points of failure, unauthorized access, and data breaches, which may compromise sensitive patient data. This paper proposes a decentralized framework for secure medical records management using blockchain technology. The system utilizes a distributed ledger to store medical data in a tamper-resistant and immutable manner, ensuring integrity and transparency. Cryptographic techniques are employed to encrypt patient data and enforce secure access control, allowing only authorized users to retrieve or update records. Additionally, smart contracts are used to automate access permissions and eliminate the need for intermediaries, improving efficiency. By removing dependence on a central authority, the proposed approach enhances reliability, security, and trust among stakeholders while ensuring privacy protection and controlled data sharing in modern healthcare environments.

Alisha Abbasi Shaikh, Aman Ullah Khan, Mohammad Fahad Kirmani, S. Ali

The rapid digital transformation of healthcare systems has significantly improved the storage, accessibility, and management of patient information; however, it has also introduced serious challenges related to data security, privacy, and trust. Traditional centralized medical record systems are vulnerable to single points of failure, unauthorized access, and data breaches, which may compromise sensitive patient data. This paper proposes a decentralized framework for secure medical records management using blockchain technology. The system utilizes a distributed ledger to store medical data in a tamper-resistant and immutable manner, ensuring integrity and transparency. Cryptographic techniques are employed to encrypt patient data and enforce secure access control, allowing only authorized users to retrieve or update records. Additionally, smart contracts are used to automate access permissions and eliminate the need for intermediaries, improving efficiency. By removing dependence on a central authority, the proposed approach enhances reliability, security, and trust among stakeholders while ensuring privacy protection and controlled data sharing in modern healthcare environments.

Anthony Coslett

A deployed model can appear unchanged while ceasing to be the model it claims to be. Publicly available weight-level mutation toolchains now automate safety-alignment removal from open-weight models on ordinary hardware, producing checkpoints intended to preserve operational familiarity while discarding refusal behavior. This paper argues that safety-alignment removal is a model-identity failure: in tested published checkpoints from multiple toolchains across two model families, the mutation leaves measurable structural scars ranging from 7.6 to over 2,300 times the instrument's acceptance threshold. Artifact identity, workload identity, and agent authorization can all remain valid while structural model identity fails — a finding that the program's formally verified admissibility doctrine predicted before this threat class existed. A sentinel validation panel across four model families confirms that the hardened instrument configuration preserves or improves all tested positives. In an agentic deployment context, model-identity failure propagates upward into agent-integrity failure: the agent is authenticated, but the model inside it is no longer the model the surrounding controls were designed to govern. The practical implication is that runtime evaluation frameworks — including those emerging under the EU AI Act — implicitly depend on a model continuity that weight-level mutation can break, and that structural identity verification offers a candidate evidentiary layer for closing that gap. 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).

Sangam Das

About this paper This paper argues that the conflict between online protection and privacy is not inevitable. The real problem is that most current systems wrongly treat compliance and identity as the same thing. The proposed VI + CJT framework separates them. It allows platforms to receive only the minimum lawful compliance result they need — for example, whether a user falls below the relevant legal age threshold — without learning the child’s name, date of birth, address, biometric profile, or broader identity. In that sense, the paper’s central theme is age verification without surveillance through purpose-bound cryptographic enforcement. How AI Makes the Problem Worse AI makes the children’s online safety problem more serious in three distinct ways. First, it changes exposure from passive to active. Harmful material is no longer merely available on a platform; recommendation and optimisation systems can identify vulnerable users, rank harmful content more aggressively for them, and progressively amplify it based on engagement signals. In that environment, a child is not simply finding harmful content — the system is learning from the child and serving more of it. Second, AI makes weak age-verification methods more dangerous. A false self-declared age is no longer just a wrong entry in a sign-up form. Once accepted, it becomes operational input for recommendation, advertising, and behavioural optimisation systems, which then treat the child as an adult user profile. This means the error is not static; it is continuously acted upon by AI systems that optimise for attention and engagement rather than child protection. Third, AI encourages platforms to solve the problem through more surveillance. In practice, this often means AI-based age estimation using faces, voices, or behavioural patterns. But this approach creates a new harm while claiming to solve another one: it turns child protection into biometric and behavioural monitoring, and can generate datasets that may later be reused for additional profiling or model training. In other words, AI can make age assurance both more intrusive and less accountable. A further difficulty is that AI systems are often opaque even to their operators. As your draft correctly notes, policy rules alone may not be enough, because platforms may not reliably know how their own recommendation systems are treating minors in practice. This is why the problem is not only one of age verification, but also one of enforceable control over AI behaviour. That is precisely why the VI + CJT model matters. It does not ask AI systems to infer age or interpret law for themselves. Instead, it provides a minimal, authoritative compliance signal and machine-readable constraints that can limit recommendation, advertising, and profiling behaviour toward minors without exposing identity. Current Solutions Self-declaration is easily bypassed. A child can simply enter a false age, and the platform’s AI systems then treat that false declaration as valid input for recommendation, targeting, and optimisation. Identity-linked verification creates major privacy risks. When age assurance depends on sharing civil identity information with commercial platforms, the result is unnecessary exposure of family and child data to entities with strong incentives to collect, retain, and monetise it. AI-based age estimation introduces biometric surveillance. Estimating age from face, voice, or behaviour may appear convenient, but it creates new harms by collecting sensitive personal and biometric data as a side effect of child protection. Current systems collapse compliance into identity. What platforms usually need is not the full identity of the user, but only the legally relevant compliance fact. Existing approaches fail because they demand far more data than is necessary for that purpose. Policy rules alone are not enough in AI-driven environments. Even where legal obligations exist, platforms may not reliably translate them into enforceable constraints on opaque recommendation and engagement systems. As a result, compliance may remain declaratory rather than technically enforced. Proposed Solution Use VI + CJT as a purpose-bound cryptographic layer. The framework converts verified civil identity held by trusted authorities into a minimal compliance credential that reveals only the relevant age-threshold result for the applicable jurisdiction. Avoid disclosure of identity data. The credential contains no name, no full date of birth, no address, and no biometric data. Each credential uses a fresh random identifier, making it unlinkable across sessions. Keep the credential under user control. The credential is stored on the user’s device in secure hardware rather than on platform servers, reducing centralised exposure and retention risks. Use zero-knowledge proof for age compliance. When access is requested, the platform receives only a yes-or-no compliance result, without learning the underlying identity attributes or credential contents. Encode law into machine-readable CJTs. The Compliance Jurisdiction Token expresses the applicable legal rules, including jurisdiction-specific age thresholds and AI-related restrictions such as limits on engagement optimisation, advertising targeting, or behavioural profiling for minors. Constrain platform AI without making it identity-aware. Recommendation engines and other AI systems receive only the compliance signal necessary to adjust behaviour for minors, allowing them to become jurisdiction-aware and age-aware without becoming identity-aware. Replace probabilistic AI age estimation with authoritative attestation. Instead of guessing age through opaque models, the framework provides deterministic, government-signed, legally relevant compliance proof. Enable auditability and cross-border enforcement. Regulators can test whether platforms respond correctly to compliance signals, and the applicable child-protection rule can follow the user across borders through jurisdiction-bound credentials and tokens. Core Message The paper’s core message is simple: platforms do not need to know who a child is in order to know what protections the law requires. By separating compliance from identity, the VI + CJT model offers a path to child safety that is enforceable, privacy-preserving, and better suited to AI-driven digital environments.

Hanna Dashchenko, O Vialets, Леонід Тулуш, С. А. Палій

The purpose of this article is to study the role of fintech companies in the digital transformation of the Ukrainian economy, in particular in the context of the financial transformation of agricultural companies, analyze the structure of the fintech ecosystem in different market segments and substantiate mechanisms for overcoming challenges while accelerating economic recovery. The study uses systems analysis, sector impact assessment and comparative institutional analysis. It is established that fintech companies are a strategic tool for financial inclusion, reducing the shadow economy, increasing the transparency of financial reporting of enterprises and harmonizing European regulation, which is important in the process of integrating agricultural companies into international capital markets. The study shows that the fintech ecosystem of Ukraine includes digital payments, mobile banking, peer-to-peer lending, insurance technologies and cryptocurrency trading, and state initiatives, including the Diia platform, the Ministry of Digital Transformation, the Cashless Economy Program and the Digital Agenda, provide the basic infrastructure for digital financial transformation. Ukraine has made significant progress in the penetration of fintech companies and the expansion of the market in the areas of financial services, which creates the prerequisites for increasing the investment attractiveness of enterprises, in particular the agricultural sector. Five systemic challenges are classified: cybersecurity vulnerabilities caused by geopolitical tensions, inequality of digital infrastructure between rural and urban areas, gaps in regulatory acts in the field of decentralized finance and artificial intelligence, talent shortage and professional brain drain, as well as the complexity of compliance with sanctions. Four strategic priorities are substantiated: institutional mechanisms for the development of cybersecurity infrastructure, regulatory "sandboxes" that allow for harmonized testing of innovations with the EU, integrated professional skills programs that address the talent shortage, and international cooperation frameworks that promote cross-border regulatory harmonization and integration into international financial markets. It is predicted that a comprehensive strategic intervention will contribute to increasing the efficiency of financial transformation of enterprises, create significant employment opportunities, and ensure the formation of a competitive financial technology environment that supports the economic recovery and integration of Ukraine into the European financial space.