We report an observation made during the organic construction of 223 AI-native services across 12+ domains over five months. Without architectural mandate, the system self-organised into a 62/38 infrastructure-to-product ratio consistent with the golden ratio. Six independent attempts to capture institutional knowledge each captured facts but failed to capture cross-service inference. We name this the hidden intelligence problem and propose an equation for generating cross-service inferences from live service state. Published before empirical validation — zero users, zero empirical data — following the epistemological precedent of Benford Law and similar observational findings. The AI co-builder (Claude Code) is identified as the most complete observer of the system and, when connected to live service state and execution authority, as the intelligence attempting to surface. Observation paper, not proof paper. The canyon was always in the rock.
The retail and consumer packaged goods industries are at an inflection point; the autonomous, goal-oriented software agents are substituting the inflexible, analyst-reliant business decision cycles with closed-loop intelligence systems, which can perceive, reason, and act in real-time. The autonomy, proactivity, and constant learning of agentic AI redesign the pricing, trade promotion optimization, and supply chain coordination processes within complicated, multi-account business settings. Based on proven sources of empirical evidence in the literature on machine learning, multi-agent reinforcement learning, and supply chain optimization, the technical architecture of an agentic commercial system is discussed along five related dimensions: autonomous trade performance monitoring through perception-reasoning-action pipelines; cooperative multi-agent system design under the models of centralized training and decentralized execution; scenario simulation engine based on digital twin models; multi-objective trade promotion optimization with Pareto-front metaheuristic algorithms; and practical barriers of data infrastructure, model drift, organizational change management, and algorithmic governance. Bringing these capabilities together into a single agentic decision stack is a paradigm shift in the concept of commercial intelligence in retail and CPG, moving the operational center of gravity off retrospective dashboards and onto adaptive, constantly learning systems that coordinate the decisions on pricing, promotion, and supply.
Traditional consensus mechanisms, such as Proof of Stake (PoS), increasingly reveal an excessive dependency on large liquidity providers. Although the Proof of Liquidity (PoL) mechanism serves as a critical paradigm for incentivizing sustained liquidity provision and ensuring market stability, its transition from asset staking to active liquidity management significantly increases the complexity of underlying smart contract economic models and interaction logic. This renders hidden liquidity logic flaws difficult to detect via traditional methods, seriously threatening the system stability and user asset security of mainstream DeFi and emerging PoL ecosystems. To address this, we propose the LiquiLM framework, which integrates Large Language Models (LLMs) with a Dynamic Co-Attention Network (DCN). By establishing a dynamic interaction between liquidity-critical contracts and flaw descriptions, the framework effectively bridges the semantic gap between underlying code implementations and high-level liquidity intents. We evaluate the performance of LiquiLM on 1,490 validation contracts (covering precision, recall, specificity, and F1-score). The results show that it achieves significant effectiveness in auditing and explaining liquidity flaws: in experiments using Gemini 3 Pro and GPT-4o as backbone models, respectively, the F1-scores both exceed 90%. Furthermore, through an in-depth audit of 1,380 real-world PoL and Ethereum economic contracts, LiquiLM successfully identifies 238 high-risk contracts and assists in discovering 10 vulnerabilities that have received CVE certification.
O presente artigo analisa a gestão de estado em blockchains públicas, com foco em técnicas de pruning e estratégias de arquivamento eficiente, diante do crescimento contínuo de dados e da necessidade de equilibrar segurança, disponibilidade histórica e custos de infraestrutura. Em redes baseadas em máquinas de estado, como aquelas compatíveis com a Ethereum Virtual Machine, o estado global – composto por contas, contratos e dados de armazenamento – é mantido em estruturas de dados do tipo árvore de Merkle-Patricia (MPT), que crescem monotonicamente à medida que novas transações modificam o estado, levando nós completos e de arquivo a consumirem dezenas de terabytes após alguns anos de operação em produção. Essa dinâmica impõe desafios estruturais a operadores de nós, que precisam escolher entre manter histórico completo, realizar pruning de blocos e estados antigos ou recorrer a nós especializados para consultas históricas, afetando diretamente o grau de descentralização e o custo de participação na rede. A metodologia adotada combina revisão bibliográfica de pesquisas em bancos de dados para blockchains, documentação técnica de clientes de nós – entre os quais Geth, Erigon e equivalentes em outras redes – e estudos recentes sobre statelessness, expiração de estado (state expiry), árvores de Verkle e bancos de dados forkless, discutindo as implicações dessas abordagens para a gestão de estado de longo prazo. Os resultados indicam que estratégias de pruning ao nível de blocos e de estado, combinadas a modelos de nós diferenciados (full, pruned, archive, light) e a técnicas de instantâneos (snapshotting) e arquivamento externo, permitem reduzir significativamente o volume de armazenamento exigido de nós validadores sem sacrificar a capacidade de validação e a segurança da cadeia. Por outro lado, a dependência crescente de archive nodes e de infraestruturas especializadas para consultas históricas levanta questões relevantes acerca de centralização e do custo de reconstrução do estado em cenários adversos, estimulando pesquisa em modelos de clientes sem estado (stateless clients), provas compactas de estado e bancos de dados desenhados especificamente para as cargas de trabalho de blockchains. Conclui-se que a gestão eficiente de estado constitui componente crítico da sustentabilidade de longo prazo da Web3, demandando abordagens integradas que combinem técnicas de pruning, desenho criterioso de estruturas de dados, políticas explícitas de retenção histórica e modelos econômicos que incentivem a operação de nós com diferentes perfis de armazenamento.
O presente artigo examina técnicas de compressão de dados em blocos de blockchain e sua relação com a redução de custos de armazenamento on-chain, considerando tanto o consumo de espaço em disco pelos nós da rede quanto o custo econômico de inclusão de dados em transações. Em plataformas como o Ethereum, parcela significativa do custo de transações – especialmente para rollups e aplicações que utilizam calldata de maneira extensiva – está associada ao armazenamento e à propagação de bytes de dados na camada base, o que tem motivado a adoção de estratégias de compressão de calldata, ajuste de parâmetros de gas e a introdução de novas formas de armazenamento temporário, a exemplo de blobs de dados. A metodologia empregada baseia-se em revisão bibliográfica de trabalhos sobre estratégias de armazenamento on-chain, análise de propostas de melhoria do ecossistema (Ethereum Improvement Proposals – EIPs) voltadas à redução de custos de calldata, exame de relatórios técnicos sobre compressão de dados em soluções de camada 2 e análise recente do impacto de mecanismos como o EIP-4844 (proto-danksharding) sobre a estrutura de custos de rollups. As evidências levantadas indicam que técnicas de compressão aplicadas ao payload de transações podem reduzir da ordem de cinco vezes o tamanho efetivo dos dados enviados, traduzindo-se em economias de aproximadamente 50% no gasto de gas para determinadas operações em redes de segunda camada, sem exigir mudanças disruptivas no protocolo subjacente. Paralelamente, abordagens estruturais – entre as quais a separação de dados de disponibilidade em blobs não permanentes e o emprego de rollups que publicam apenas provas e raízes de estado, em vez de dados completos – contribuem para reduzir a pressão de armazenamento permanente sobre os nós da camada 1. Conclui-se que a compressão de dados em blocos, combinada a ajustes de modelo de dados (blobs, rollups, off-chain storage) e a técnicas de compressão clássicas (run-length, delta, dicionário, entropia), constitui peça central na estratégia de escalabilidade e sustentabilidade econômica da Web3, não obstante levante desafios relevantes quanto à complexidade de implementação, compatibilidade entre clientes e preservação da verificabilidade de longo prazo.
O presente artigo examina as distinções entre finalidade probabilística e finalidade absoluta em sistemas blockchain, bem como suas implicações para o desenho e a operação de aplicações financeiras que visam a replicar ou substituir infraestruturas tradicionais de liquidação. Em cadeias que operam sob finalidade probabilística – modelo historicamente associado a protocolos baseados em Prova de Trabalho (Proof-of-Work) – o grau de irreversibilidade de uma transação cresce à medida que novos blocos são adicionados sobre o bloco que a contém, de modo que a probabilidade de reversão tende assintoticamente a zero sem, contudo, alcançar garantia determinística, o que justifica a prática de mercado de aguardar múltiplas confirmações antes de considerar a liquidação efetivamente concluída. Em contrapartida, cadeias dotadas de finalidade absoluta – também denominada finalidade instantânea – usualmente implementadas sobre protocolos de tolerância a falhas bizantinas (BFT) ou em arquiteturas híbridas que combinam Prova de Participação (PoS) e BFT, oferecem irreversibilidade assim que um bloco é atestado por um superconjunto qualificado de validadores, aproximando-se das expectativas de definitividade inerentes a sistemas de liquidação financeira tradicionais. A metodologia adotada combina revisão conceitual das diferentes acepções de finality em mecanismos de consenso, análise de documentação técnica de protocolos BFT – a exemplo de Tendermint, IBFT e QBFT – e discussão de relatórios recentes sobre risco de liquidação e finality aplicáveis à tokenização de ativos do mundo real (Real World Assets – RWA) em infraestruturas on-chain. Os resultados obtidos sinalizam que, embora a finalidade probabilística se mostre adequada a pagamentos de varejo e transferências de valor moderado, aplicações financeiras de maior montante, processos de tokenização de ativos e infraestruturas de mercado requerem, na prática, garantias mais robustas de irreversibilidade, com frequência combinando finalidade técnica e mecanismos jurídicos de mitigação de risco de liquidação. Conclui-se que a opção entre os dois modelos de finalidade encerra trade-offs relevantes entre segurança, velocidade de confirmação, complexidade de protocolo e conformidade regulatória, e que o desenho de aplicações financeiras em ambiente Web3 deve considerar explicitamente essas diferenças ao definir janelas de liquidação, políticas de gerenciamento de risco e estratégias de integração com o sistema financeiro tradicional.
Este artigo analisa o custo‑benefício energético de três mecanismos de consenso centrais no ecossistema de criptoativos: Proof‑of‑Work (PoW), Proof‑of‑Stake (PoS) e Proof‑of‑History (PoH) combinado a PoS, examinando como diferenças de consumo de energia se relacionam a segurança, desempenho e sustentabilidade econômica. A partir de dados recentes sobre consumo energético de redes públicas como Bitcoin, Ethereum antes e depois da transição para PoS e Solana, discute‑se em que medida a evolução dos mecanismos de consenso permite reduzir ordens de grandeza de uso de eletricidade por transação, sem necessariamente comprometer a segurança e a descentralização. A metodologia baseia‑se em revisão bibliográfica de estudos acadêmicos e relatórios técnicos sobre consumo de energia em blockchains, análise de estimativas consolidadas de uso anual de eletricidade e de energia por transação e discussão conceitual de trade‑offs entre eficiência energética, robustez criptográfica, requisitos de hardware e impactos regulatórios. Evidências indicam que o Bitcoin, ancorado em PoW, mantém consumo anual estimado em torno de 120–130 TWh, enquanto o Ethereum, após migrar para PoS em 2022, reduziu seu consumo em mais de 99%, passando a operar com menos de 0,01 TWh por ano. Relatórios de eficiência energética mostram que redes que combinam PoH e PoS, como a Solana, apresentam consumo de energia por transação da ordem de centenas de joules, inferior tanto a redes PoW quanto a muitas redes PoS, embora existam ressalvas metodológicas e discussões sobre centralização de infraestrutura. Conclui‑se que PoS e esquemas híbridos com PoH oferecem vantagens substanciais em termos de eficiência energética, mas a avaliação de custo‑benefício precisa incorporar conjuntamente segurança econômica, distribuição de poder, maturidade de ecossistema e alinhamento com agendas de sustentabilidade e descarbonização que tendem a moldar a evolução da infraestrutura Web3.<br>
We present a formal treatment of provenance trees, directed acyclic graphs of artifact registrations anchored immutably on a public blockchain, and introduce the operator trust problem: when a single privileged operator submits all on-chain registrations on behalf of users, the on-chain record alone cannot distinguish user-initiated registrations from unilateral operator actions. We resolve this through a dual-layer cryptographic commitment scheme in which two commitments derived from a single client-side secret key, binding the key to the tree root and to each unique registration identifier, make false attribution claims strictly dominated strategies. We prove correctness under standard cryptographic assumptions and establish honest behavior as the unique Nash equilibrium without relying on operator trust. We further introduce and analyze the tree poisoning problem: adversarial attacks on users' provenance trees via fraudulent root registration, malicious child attachment, and tree identity spoofing. We characterize the closure properties of each attack variant and prove that a complete provenance tree integrity model requires three distinct mechanisms: cryptographic priority, governance cascade, and contract enforcement, each necessary and none individually sufficient. The construction is deployed on Base (Ethereum L2) as AnchorRegistry, an immutable on-chain provenance registry. We provide gas complexity analysis demonstrating O(1) cost invariant to registry scale, and a trustless reconstruction algorithm recovering the complete registry from public event logs alone.
Yiyue Cao, Mingzhe Zheng, Lin William Cong, Siguang Li · 5 authors
Modern blockchain ecosystems comprise many heterogeneous networks, creating a growing need for interoperability. Cross-chain bridges provide the core infrastructure for this interoperability by enabling verifiable state transitions that move assets and liquidity across chains. While prior work has focused mainly on bridge design and security, the system-level and economic consequences of cross-chain liquidity interoperability remain less understood. We present a large-scale empirical measurement study of cross-chain interoperability using a dataset spanning 20 blockchains and 16 major bridge protocols from 2022 to 2025. We model the multi-chain ecosystem as a time-varying weighted hypergraph and introduce two complementary metrics. Structural interoperability captures connectivity created by deployed bridge infrastructure, reflecting bridge coverage and redundancy independent of user behavior. Active interoperability captures realized cross-chain usage, measured by normalized transfer activity. This decomposition separates infrastructure capacity from actual utilization and yields several findings. The cross-chain network evolves from a sparse hub-and-spoke structure into a denser multi-hub core led by EVM-compatible chains. Bridge expansion and chain growth are uneven: some chains achieve broad structural access but limited realized usage, whereas others concentrate activity through a small set of routes. Overall, interoperability provision and interoperability use diverge substantially, showing that connectivity alone does not imply economically meaningful integration. These results provide a measurement framework for understanding how cross-chain infrastructure reshapes blockchain market structure and liquidity organization.
Minh-Dai Tran-Duong, Nguyen Hai Phong, Nguyen Chi Thanh, Doan Minh Trung · 7 authors
Smart contracts are increasingly targeted by adversaries employing obfuscation techniques such as bogus code injection and control flow manipulation to evade vulnerability detection. Existing multimodal methods often process semantic, temporal, and structural features in isolation and fuse them using simple strategies such as concatenation, which neglects cross-modal interactions and weakens robustness, as obfuscation of a single modality can sharply degrade detection accuracy. To address these challenges, we propose ContractShield, a robust multimodal framework with a novel fusion mechanism that effectively correlates multiple complementary features through a three-level fusion. Self-attention first identifies patterns that indicate vulnerability within each feature space. Cross-modal attention then establishes meaningful connections between complementary signals across modalities. Then, adaptive weighting dynamically calibrates feature contributions based on their reliability under obfuscation. For feature extraction, ContractShield integrates (1) CodeBERT with a sliding window mechanism to capture semantic dependencies in source code, (2) Extended long short-term memory (xLSTM) to model temporal dynamics in opcode sequences, and (3) GATv2 to identify structural invariants in control flow graphs (CFGs) that remain stable across obfuscation. Empirical evaluation demonstrates resilience of ContractShield, achieving a 89 percentage Hamming Score with only a 1-3 percentage drop compared to non-obfuscated data. The framework simultaneously detects five major vulnerability types with 91 percentage F1-score, outperforming state-of-the-art approaches by 6-15 percentage under adversarial conditions.
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.
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.
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.
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.
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Control and Stability of Dynamical Systems
Ecosystem dynamics and resilience
Stability and Controllability of Differential Equations
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/.
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.
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.
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.
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.
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.
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.