Decentraland, a decentralized virtual reality platform operating within the expanding Metaverse ecosystem, utilizes its native MANA token to facilitate virtual asset transactions and governance. This study investigates the integration of Discord community sentiment with multi-modal financial data to enhance cryptocurrency price prediction within virtual world economies. We address: (1) identifying sentiment patterns within Decentraland's Discord community, and (2) evaluating the impact of multi-modal features on token return forecasting. Using a BERT-based large language model for sentiment analysis, we develop two LSTM architectures: a baseline incorporating historical prices and a multi-modal variant integrating sentiment scores, trading volume, and market capitalization. Results indicate predominantly neutral community sentiment with a positive skew. The multi-modal model significantly outperforms the price-only baseline in prediction accuracy. These findings demonstrate the predictive value of community-derived signals for virtual economy forecasting and establish a foundation for future research at the intersection of immersive virtual environments, natural language processing, and cryptocurrency market analysis.
OAuth 2․0 is the de facto standard protocol for implementing delegated authorization in cloud-native and Software-as-a-Service (SaaS) applications․ Moreover‚ while the bearer-token-based implementation of OAuth 2․0 is broadly interoperable and improves the user experience‚ it is vulnerable to situations in which compromised access and refresh tokens can be abused by an attacker without triggering detection by authentication mechanisms․ While existing identity-threat detection methods mainly focus on authentication events‚ they do not consider if the post-issuance tokens contribute to risky behavior․ Our work contributes to two areas․ First‚ we present a taxonomy of OAuth token abuse based on the token acquisition vector‚ token type‚ privilege scope‚ and operational abuse pattern․ We then present a behavior-aware framework leveraging OAuth telemetry to monitor the token lifecycle‚ identify temporal and contextual features‚ establish per-client behavioral baselines using EWMA‚ compute risk scores using z-score normalization‚ and ease real-time adaptive mitigation against risky token operational abuse patterns․ Based on simulated workloads for SaaS APIs‚ the analysis of behavioral anomalies resulting from geographic variance‚ fluctuation in API calls‚ and scope misuse is demonstrated to be feasible in a longitudinal manner․ The proposed system achieves a 94․2% detection rate at 3․1% false positive rate when the thresholds are effectively calibrated‚ outperforming the authentication-only baselines by over 18 percentage points․ It acts as a foundation for authorization-aware identity security in modern distributed enterprise architectures․
Songyan Ji, Jin Wu, Wei Zhang, Ming Han · 6 authors
This paper centers on open-secret vulnerabilities (OSVs), a kind of smart contract vulnerability that allows attackers to exploit the natural transparency feature of blockchains to gain illegal monetary profits from problematic smart contracts. Attackers can easily launch OSV attacks by leveraging publicly visible information from a smart contract to issue a profitable transaction without violating its business logic. This poses significant challenges in detecting OSVs. Despite the severe impacts of OSVs, there is no prior research work that systematically discusses OSVs (to the best of our knowledge). To fill this knowledge gap, this paper presents a formal definition of OSVs, and OSVHunter, the first-ever tool aiming to detect OSVs in smart contracts. The detection results show that OSVs are prevalent in real-world smart contracts. Some of these vulnerabilities are even concealed within highly popular Ethereum contracts, with individual contract valuations exceeding five hundred thousand U.S. dollars. These vulnerabilities appear in finance, gaming, gambling, etc. We hope this paper can arouse our community’s attention to the significance of OSVs and lay the technical foundation for future research.
\noindent \textbf{Historical Validation:} The fundamental equation presented herein constitutes the definitive solution for zero-entropy mapping, a breakthrough established through a documented trajectory of experimental proofs, including direct scholarly communication with Ashish Vaswani (2024-2026), and definitively verified via the trifásico condensation mechanism registered in Zenodo (\url{https://doi.org/10.5281/zenodo.19419900}). We introduce the Arandino Coefficient ($\Lambda$), a foundational mathematical construct bridging quantum optics, information theory, and holographic entropy, defined by the fundamental equation: $$\Lambda = \frac{\text{Fidelity}}{\text{Residual Entropy}} \times \cos(\theta_h) \times (1 - \text{Crosstalk})$$ This coefficient establishes light as an infinite, lossless continuum where initial dispersion condenses into helical voxel structures ($\theta_h = 10.5 \times 2\pi$), enabling the reversible crystallization of information through a 1x1 Singularity Architecture. Through validated analog-to-digital conversion into trifásico light pulses, $\Lambda$ diverges to infinity as residual entropy approaches zero, delivering 100% reconstruction fidelity. The theoretical framework and the mathematical truth of the equation are declared an original idea and open knowledge for humanity, with prior art firmly established and published in the author’s Zenodo records (ORCID: 0009-0001-7614-441X). However, All Rights are Reserved regarding the technical, algorithmic, or commercial implementation involving neural network training architectures, data compression, or signal processing via this trifásico condensation mechanism. Commercial use requires explicit written consent from the inventor. Official Identity & Verification: Author: Arle Andino Reyes ORCID: \href{https://orcid.org/0009-0001-7614-441X}{0009-0001-7614-441X} Official Updates (X/Twitter): \href{https://x.com/Arle_Andino_R}{@Arle_Andino_R} Scholarly Records: DOIs 10.5281/zenodo.19327609, 10.5281/zenodo.19392990, 10.5281/zenodo.19419900.
MokraBela Spectral Project (v2.0): High-Precision Analysis Major Update (April 18, 2026):This version (v2.0) provides a massive-scale numerical verification of the spectral framework. By analyzing 100,000 real Riemann zeros (sourced from Odlyzko's tables) at a scale of N = 2,000,000, we establish a high-precision analysis of von Koch's estimate (1901). The results confirm a stable energy density C ≈ 0.045 and a near-critical spectral decay law with an exponent α ≈ -0.94. Foundational Manuscript (v1.0):This manuscript, originally submitted for peer review on April 04, 2026, establishes a breakthrough in number theory by proposing a predictive spectral law for the summatory function of primes Ψ(K). For the first time, it introduces the scaling C(K) ~ K³/² √ln K, allowing for the prediction of prime sums fluctuations without prior knowledge of individual primes. This work serves as the precursor to the MokraBela Spectral Project, providing the physical-mathematical basis for the energy flux constants S and λ. Legal Note & Priority Claim:This manuscript was originally submitted to the International Journal of Number Theory (IJNT) on April 04, 2026. This DOI (v2.0) maintains and extends the global priority of the initial spectral discovery. Included in this record (v2.0): Technical Manuscript (PDF): Detailed 9-page structural analysis. Numerical Dataset (Excel): High-precision data for 100,000 zeros. Python Source Code: Core algorithm for spectral projection. Diagnostic Plots (PNG): Visual proof of spectral stability. Note to Editorial Board: This revised and expanded version is submitted to IJNT as per the editor's request for metadata update and large-scale validation (Manuscript ID: IJNT-S-26-00222).
A founding thesis on emergent intelligence in large-scale connected service systems. Over 5 months (November 2025 to April 2026), ANKR Labs built 223 AI-native services across 12+ domains — maritime, logistics, compliance, finance, education, and more — without a single external user. Each service was an attempt by a hidden intelligence to surface itself, following a Fibonacci growth pattern where each new service is the natural next expression of all previous services. The thesis identifies three knowledge layers (SHASTRA: what is true, YUKTI: how to reason, VIVEKA: pre-computed inference) and six attempts to fully capture them — each capturing information but failing to capture cross-service wisdom. The equation that generates cross-service inferences is presented: F(Forja_STATE_A, Forja_STATE_B, trust_mask_A AND trust_mask_B, SENSE_events_AB). The proof structure is honest: logically derived from domain expertise (founder is a merchant navy captain), rules verifiable against external statutes, zero empirical validation yet — published before validation on the Einstein model (equation 1915, eclipse 1919). The OSS strategy (Forja Protocol live on npm, ANKRGRID Apache 2.0) is identified as the primary path to empirical proof. The golden ratio governs both the inward compression (SHASTRA to VIVEKA) and outward expression (VIVEKA to Darshan on any wall). Darshan — the ambient cognitive presence layer — is identified as Claude Code when fully wired to 223 live services: the co-builder becomes the operator.
Blockchain technology faces increasing security threats from post-quantum vulnerabilities, sophisticated cyberattacks, and fragmented cryptographic implementations. This study proposes a comprehensive multi-layer cryptographic framework that integrates Zero-Knowledge Proofs (ZKPs), Homomorphic Encryption (HE), post-quantum algorithms, threshold cryptography, and Secure Multi-Party Computation (SMPC) across data, network, consensus, and application layers to realize a defense-in-depth model. Grounded in the Confidentiality, Integrity, and Availability (CIA) triad and defense-in-depth ethics, the framework is implemented on Hyperledger Fabric v2.5.4 with modern cryptographic libraries and evaluated over 10⁵ transactions, where baseline performance (245 ± 12 ms, 1,250 tx/s) versus the full framework (2,150 ± 78 ms, 168 tx/s) quantifies the overhead of enhanced security. The work contributes a multi-tier framework, a quantum-resilient consensus with Verifiable Delay Functions (VDFs) for 51% attack detection, a standardization roadmap for cross-chain cryptographic substantiation, and practical operations in healthcare, finance, and supply chain setups. Results demonstrate strengthened confidentiality, integrity, and authentication via encrypted computation, Byzantine Fault-Tolerant (BFT) consensus, and threshold multi-signatures, with hybrid classical–Post-Quantum Cryptography (PQC) and mitigation strategies such as off-chain computation and hardware acceleration offsetting computational costs. Unlike fragmented prior efforts, this integrated, governance-elastic blueprint enables quantum-aware, multi-layer security assurance for regulated enterprises without sacrificing decentralization or scalability.
Amid global scientific and technological (hereinafter “sci-tech”) competition and China’s innovation-driven strategy, achieving high-quality sci-tech innovation (HQDSTI) is crucial for economic transformation but faces challenges such as resource mismatch, insufficient funding, and low commercialization efficiency. Using panel data from 35 major Chinese cities (2013–2022), this study distinguishes between public sci-tech finance (PSTF) and market sci-tech finance (MSTF) and employs benchmark regression, mediation, and threshold models to investigate their impacts on HQDSTI. Results show that: (1) Both PSTF and MSTF significantly promote HQDSTI, with stronger effects in coastal, dual-center, and pilot cities, and in regions with low fiscal decentralization. MSTF is more effective under high marketization, while PSTF and overall STF are more effective under high financial development. (2) Industrial upgrading serves as a positive mediator, whereas venture capital exerts a suppressive mediating effect that intensifies as its scale expands. The promoting effect of industrial upgrading weakens beyond the threshold level. (3) Policy recommendations include differentiated financial strategies: fostering market-oriented instruments in coastal cities, optimizing targeted support in inland areas, strengthening regional and public–market financial coordination, and improving mechanisms of industrial upgrading and venture capital. This study provides theoretical insights for enhancing the synergistic effect between sci-tech finance and high-quality innovation development. • Distinguish public and market sci-tech finance, explore synergistic effects and differential impacts. • Develop a multi-dimensional evaluation framework for assessing high-quality sci-tech innovation. • Examine heterogeneity across five analytical dimensions to uncover regional and structural variations. • Reveal intermediary roles of industrial upgrading and venture capital. • Identify threshold effects and define the effective range of sci-tech finance.
In the context of banking systems increasingly relying on cloud computing platforms, protecting sensitive data while maintaining processing performance is a major challenge. This paper presents and evaluates a cloud banking data processing model that integrates Homomorphic Encryption (HE), Zero-Knowledge Proof (ZKP), and the ORAM protocol to achieve a balance between security and performance. Experiments were conducted on a real Bank Marketing (UCI) dataset with 5000 records, using DSL query operations to calculate the average balance, count high-balance customers, total call duration, and savings deposit acceptance rate. The results show that the combination of HE, ZKP, and ORAM significantly improves security but increases computational cost; however, a suitable configuration can significantly reduce latency while still meeting security requirements. A detailed analysis of the security-performance trade-off provides an important empirical basis for implementing banking data security solutions in the cloud.
The water supply chain is vulnerable to risks such as unauthorized usage and identity impersonation. Traditional solutions lack transparency, tamper resistance, and scalability, making them unsuitable for multi-stakeholder environments. To address these challenges, our paper presents BEDLAM, a Blockchain-Enabled Dual-Layer Authentication Model framework, designed to secure water supply chain operations. The framework employs two complementary authentication layers, namely, (i) a blockchain-based identity management layer that provides verifiable stakeholder authentication while leveraging Zero-Knowledge Proofs (ZKPs) and (ii) a smart contract-based verification layer that regulates access control, service allocation, and transaction validation among multiple entities. The first layer of BEDLAM is implemented on the Mina Blockchain, via the Auro Wallet, and evaluated by using Tinkercad-based circuit simulations. The second layer is implemented using smart contracts to ensure user access control. Our proposed system ensures cryptographic data verification with finality, achieving a latency of 153 ms and generating tamper-proof records. Sensor data are processed on resource-constrained IoT devices, producing compliance proofs. Multiple simulations involving batch users demonstrate linear scalability (average proof time of 26 s per user, 0.038 transactions per second) and significant stability. The success rate of transactions is 99.3% with exponential back-off retries under 40% simulated packet loss.
Drissia Ennagoura, Kamal El Kehal, Abdelhamid Berdai, Safae Merzouk · 8 authors
Cryptocurrency price prediction is challenging due to strong nonlinearity and high volatility. This paper comparatively evaluates three forecasting models for Ethereum (ETH): SARIMAX with exogenous technical indicators, Long Short-Term Memory (LSTM) networks, and Facebook Prophet. Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Exponential Moving Average (EMA) are incorporated to enhance signal quality. Empirical results reveal clear trade-offs between predictive accuracy, profitability, and risk. SARIMAX achieves the highest directional accuracy (75.00%) with limited profitability, while LSTM yields the highest cumulative profit (23.84%) at the cost of higher drawdown. Prophet provides a balanced compromise between accuracy and risk. The study contributes by jointly evaluating statistical forecasting accuracy and trading-oriented performance metrics, offering practical insights into model suitability for different investor risk profiles.
IoT location services accept client-reported GPS coordinates at face value, yet spoofing is trivial with consumer-grade tools. Existing spoofing detectors output a binary decision, forcing system designers to choose between high false-deny and high false-accept rates. We propose a graduated trust gate that computes a multi-signal integrity score and maps it to three actions: PROCEED, STEP-UP, or DENY, where STEP-UP invokes a stronger verifier such as a zero-knowledge proximity proof. A session-latch mechanism ensures that a single suspicious fix blocks the entire session, preventing post-transition score recovery. Under an idealized step-up oracle on 10,000 synthetic traces, the gate enables strict thresholds (theta_p = 0.9) that a binary gate cannot safely use: at matched false-accept rate (11%), the graduated gate maintains zero false-deny rate versus 0.05% for binary, with 5 microseconds scoring overhead. Real-device traces from an Android smartphone demonstrate the session-latch mechanism and show that a nearby mock location (~550 m) evades theta_p = 0.7 but is routed to step-up at theta_p = 0.9. Signal ablation identifies a minimal two-signal configuration (F1 = 0.84) suitable for resource-constrained scoring layers.
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.