Samuel Oleksak, Richard Gazdik, Martin Peresini, Ivan Homoliak
Proof of Work (PoW) is widely regarded as the most secure permissionless blockchain consensus protocol. However, its reliance on computationally intensive yet externally useless puzzles results in excessive electric energy wasting. To alleviate this, Proof of Useful Work (PoUW) has been explored as an alternative to secure blockchain platforms while also producing real-world value. Despite this promise, existing PoUW proposals often fail to embed the integrity of the chain and the identity of the miner into the puzzle solutions, not meeting the necessary requirements for PoW and thus rendering them vulnerable. In this work, we propose a PoUW consensus protocol that computes client-outsourced SNARK proofs as a byproduct, which are simultaneously used to secure the consensus protocol. We further leverage this mechanism to design a decentralized marketplace for outsourcing SNARK proof generation, which is, to the best of our knowledge, the first such marketplace operating at the consensus layer while meeting all necessary properties of PoW.
With the proliferation of decentralized applications (DApps), the conflict between the transparency of blockchain technology and user data privacy has become increasingly prominent. While Decentralized Identity (DID) and Verifiable Credentials (VCs) provide a standardized framework for user data sovereignty, achieving trusted identity verification and data sharing without compromising privacy remains a significant challenge. This paper proposes a novel, comprehensive framework that integrates DIDs and VCs with efficient Zero-Knowledge Proof (ZKP) schemes to address this core issue. The key contributions of this framework are threefold: first, it constructs a set of strong privacy-preserving protocols based on zk-STARKs, allowing users to prove that their credentials satisfy specific conditions (e.g., "age is over 18") without revealing any underlying sensitive data. Second, it designs a scalable, privacy-preserving credential revocation mechanism based on cryptographic accumulators, effectively solving credential management challenges in large-scale scenarios. Finally, it integrates a practical social key recovery scheme, significantly enhancing system usability and security. Through a prototype implementation and performance evaluation, this paper quantitatively analyzes the framework's performance in terms of proof generation time, verification overhead, and on-chain costs. Compared to existing state-of-the-art systems based on zk-SNARKs, our framework, at the cost of a larger proof size, significantly improves prover efficiency for complex computations and provides stronger security guarantees, including no trusted setup and post-quantum security. Finally, a case study in the decentralized finance (DeFi) credit scoring scenario demonstrates the framework's immense potential for unlocking capital efficiency and fostering a trusted data economy.
Electronic waste (e-waste) is a rapidly growing global problem caused by shorter device lifecycles and rising consumption. India ranks third globally in e-waste generation, producing over 1.7 million tonnes in 2023-24, of which less than half is formally processed. To address this, we propose Green Grid, an integrated AI-powered e-waste management platform combining IoT-enabled smart collection, AI-based device classification, blockchain-based traceability, and gamified citizen engagement. The system features smart recycling bins with sensors for real-time monitoring, deep learning models for device identification and sorting, a blockchain ledger for tamper-proof tracking, and a reward-based mobile or web app to encourage user participation. Additionally, Green Grid offers analytics dashboards and an eco-marketplace to support policymakers and recyclers. By bridging technology, sustainability, and community participation, the platform enhances transparency, increases formal recycling rates, and advances India's transition toward a circular economy.
As blockchain systems grow in complexity, secure and efficient smart contract development remains a crucial challenge. Large Language Models (LLMs) like DeepSeek promise significant enhancements in developer productivity through automated code generation, debugging, and testing. This study focuses on Solidity, the dominant language for Ethereum smart contracts, where correctness, gas efficiency, and security are critical to real-world adoption. This study evaluates the capabilities of DeepSeek’s V3 and R1 models, a non-reasoning Mixture-of-Experts architecture and a reasoning-based model trained via reinforcement learning, respectively, in automating Solidity contract generation and testing, as well as identifying and fixing common vulnerabilities. We designed a controlled experimental framework to evaluate both models by generating and analysing a diverse set of smart contracts, including standardised tokens (ERC20, ERC721, ERC1155) and real-world application scenarios (Supply Chain, Token Exchange, Auction). The evaluation is grounded on a multidimensional metric suite covering quality, technical robustness and process characteristics. Vulnerability detection and patching capabilities are tested using predefined vulnerable contracts and guided patch prompts. The analysis spans six levels of prompt complexity and compares the impact of reasoning-based and non-reasoning-based generation strategies. Findings reveal that R1 delivers more accurate and optimised outputs under high complexity, while V3 performs more consistently in simpler tasks with simpler code structures. However, both models exhibit persistent hallucinations, limitations in vulnerability coverage, and inconsistencies due to prompt formulation. The correlation between re-evaluation patterns and output quality suggests that reasoning helps in complex scenarios, although excessive revisions may lead to over-engineered or unstable solutions. Neither model is robust enough to autonomously generate issue-free smart contracts in complex or security-critical scenarios, underscoring the need for human oversight. These findings highlight best practices for integrating LLMs into blockchain development workflows and emphasise the importance of aligning model selection with task complexity and security requirements.
The Buru Regency Government, as the party tasked with administering government, development, and public services, is required to report on regional financial accountability as the basis for assessing its financial performance. The purpose of this study is to assess regional financial performance using ratios from 2020 to 2024, consisting of: Regional Fiscal Independence; Effectiveness of PAD Management; Effectiveness of Regional Taxes; Degree of Fiscal Decentralization; Fiscal Dependency; and Growth of Regional Government Finance in Buru Regency. Using secondary data sourced from the Ministry of Finance website, this study concludes that the financial performance of the Buru Regency Government consists of: 1) the regional fiscal autonomy ratio is still very low with an instructive relationship pattern, indicating that the local government is not yet capable of financing its own government activities, development, and services to the community, and the local government still needs intervention from the central government; 2) the fiscal decentralization ratio indicates that the local government's ability to increase its own revenue (PAD) to finance its own development is still very limited; 3) the local tax effectiveness ratio and local revenue (PAD) indicate that the local government is less effective in realizing tax revenue and local revenue (PAD) from the set targets and real potential; 4) The fiscal dependency ratio shows that the Buru Regency local government is still highly dependent on assistance from the central and provincial governments compared to its own regional revenue; 5) The PAD growth ratio shows that the local government is poor/negative in maintaining and increasing PAD.
J. Balamurugan, Devineni Poojitha, R Bindu, Archana Pallakonda · 8 authors
Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs.
General Background: Blockchain-based smart contracts have revolutionized global transactions by enabling automatic, transparent, and decentralized execution of agreements. Specific Background: Despite their efficiency, these digital instruments challenge traditional private international law, particularly regarding jurisdiction, applicable law, and enforceability in cross-border contexts. Knowledge Gap: Existing legal systems, especially in the Middle East, lack comprehensive frameworks to address decentralized contracting and blockchain-based evidence. Aims: This study critically examines the intersection between smart contracts and conflict of laws in digital environments, focusing on Iraq’s legal framework and regional comparison with the EU and the US. Results: The analysis reveals that while the EU has developed coherent regulatory models such as MiCA and the Data Act, and several US states have recognized smart contracts’ validity, Iraq’s Civil Code of 1951 remains inadequate to regulate automated digital agreements. Novelty: The paper proposes a unified legal model integrating UNCITRAL’s 2024 Model Law on Automated Contracting, regional cooperation through the Arab League and GCC, and legislative reforms in Iraq to recognize blockchain evidence. Implications: Implementing such a framework would harmonize technological progress with legal certainty, enhance cross-border trust, and position Iraq and the Middle East within the global digital economy.Highlight : Analyzes the intersection of smart contracts and conflict of laws in digital space. Examines Iraq’s outdated legal framework amid rapid technological change. Suggests adopting international models and regional cooperation for legal reform. Keywords : Smart Contracts, Blockchain, Conflict of Laws, Private International Law, Jurisdiction, Iraq.
Abdul Malik, Gayatri Putri, Hesti Putri, Ahmad Badruddin
The proliferation of crypto-assets has raised critical questions about their impact on global financial stability. This study rigorously investigates the structural evolution of the cryptocurrency market's role within the global financial system, testing the hypothesis that it has transitioned from a peripheral, shock-absorbing entity into a systemically significant transmitter of financial risk. We employ a Time-Varying Parameter Vector Autoregression (TVP-VAR) model on daily data from January 1, 2017, to December 31, 2024, examining the dynamic connectedness between a bespoke, rebalanced cryptocurrency index (CRIX20) and key global financial indicators (S&P 500, MSCI World, VIX, DXY). The econometric framework utilizes a Bayesian estimation approach with standard priors, a 200-day rolling window, and a 10-day forecast horizon for Generalized Forecast Error Variance Decompositions (GFEVD). Methodological robustness is confirmed through structural break tests and sensitivity analysis of the forecast horizon. Our findings reveal a profound structural transformation. Prior to mid-2020, the cryptocurrency market was a consistent net receiver of financial spillovers. A structural break, formally identified in the third quarter of 2020, marks a definitive regime shift. Post-break, the crypto market has become a significant and persistent net transmitter of risk to the traditional financial system. The total connectedness index for the entire system shows a marked secular increase, with the crypto market's contribution to systemic risk growing substantially. Gross spillover analysis confirms this shift is driven by a dramatic increase in risk transmission from the crypto market to other assets. In conclusion, the cryptocurrency market can no longer be considered an isolated ecosystem; it is now an integral and potentially destabilizing component of the global financial architecture. The era of crypto-assets as reliable diversifiers has waned, replaced by a new reality where shocks originating within this market pose a credible threat to broader financial stability. These findings present urgent challenges for regulatory oversight, systemic risk monitoring, and portfolio management.
Ifran Khan, Huangbao Gui, Chin Man Chui, Mrs Faryal · 6 authors
This study investigates the dynamic volatility transmission between leading cryptocurrencies (Bitcoin, Ethereum, and Binance Coin) and major Chinese firms in the technology (Tencent and Alibaba), green energy (CATL, BYD, and LONGi), and traditional energy (PetroChina) sectors, including the CSI 300 index. Employing the frameworks of Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) on daily data from July 2018 to May 2025, we demonstrate significant cross-market risk transmission. The total connectedness index averages 34.77%, soaring to over 50% during the COVID-19 crisis, underscoring heightened systemic vulnerability. Our key finding identifies the CSI 300 index and cryptocurrencies (BTC, ETH) as the primary net transmitters of volatility shocks, whereas Chinese tech and energy firms (Tencent, CATL, and PetroChina) act as the main net receivers. A critical insight from the frequency decomposition is the absolute dominance of short-term spillovers (1–4 days), which constitute 34.85% of total connectedness, vastly outweighing the minimal effects in the medium- (4–10 days: 0.78%) and long-term (beyond 10 days: 0.52%). Investor sentiment, speculation, and news shocks drive short-term volatility spillovers from cryptocurrencies to stocks, particularly evident in their strong correlation with Chinese tech and energy equities. We attribute these spillovers to shared investor bases, sectoral links like crypto mining's energy demand, and regulatory interdependencies. Our evidence confirms that cryptocurrency markets are now integral to global financial stress, transmitting significant volatility to real-economy sectors. This study offers critical insights for investors and policymakers managing risk in an increasingly interconnected financial landscape.
This study examines the design and deployment of scalable blockchain protocols that can serve as the backbone for smart city applications. The manuscript reviews existing blockchain consensus mechanisms—including Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Authority (PoA)—and evaluates their suitability for heterogeneous smart city ecosystems. It further explores emerging scalability approaches such as sharding, sidechains, directed acyclic graphs (DAGs), and layer-2 protocols, alongside hybrid models that incorporate AI-driven optimization. A comparative simulation-based methodology is employed, assessing throughput, latency, and energy consumption across multiple blockchain prototypes. Results demonstrate that modular hybrid architectures leveraging sharding and DAG structures can increase throughput by up to 400% compared to traditional blockchains, with latency reductions of over 90% and significant energy savings. Beyond technical findings, the study contextualizes blockchain scalability within broader smart city governance frameworks, addressing interoperability between diverse urban domains such as energy microgrids, healthcare data platforms, autonomous mobility systems, and decentralized citizen services. The implications for data privacy, regulatory compliance, and citizen trust are also highlighted, emphasizing the necessity of balancing decentralization with governance oversight. By synthesizing technical, social, and policy considerations, this work contributes a comprehensive roadmap for scalable blockchain adoption in smart cities. Ultimately, the research demonstrates that with careful architectural design and integration of scalability-enhancing techniques, blockchain can evolve from a niche financial tool into a universal urban infrastructure enabler. The findings not only advance blockchain scalability research but also provide actionable insights for policymakers, urban planners, and technologists seeking to design sustainable, citizen-focused smart cities.
This paper addresses one of the most noteworthy issues in the recent virtual asset market, the privacy concerns related to token transactions of Real-World Assets tokens, known as RWA tokens. Following the advent of Bitcoin, the virtual asset market has experienced explosive growth, spawning movements to link real-world assets with virtual assets. However, due to the transparency principle of blockchain technology, the anonymity of traders cannot be guaranteed. In the existing blockchain environment, there have been instances of protecting the privacy of fungible tokens (FTs) using mixer services. Moreover, numerous studies have been conducted to secure the privacy of non-fungible tokens (NFTs). However, due to the unique characteristics of RWA tokens and the limitations of each study, it has been challenging to achieve the goal of anonymity protection effectively. This paper proposes a new token trading platform, the ARTeX, designed to resolve these issues. This platform not only addresses the shortcomings of existing methods but also ensures the anonymity of traders while enhancing safeguards against illegal activities.
Marta Massi, Andrea Vocino, Chiara Piancatelli, Paola Cillo · 5 authors
Non-fungible tokens (NFTs) are revolutionizing luxury fashion by offering digital experiences that promise innovation, exclusivity, and sustainability. While luxury brands increasingly experiment with these technologies, little is known about how they influence consumer perceptions of sustainability, brand legitimacy, and purchase likelihood. Drawing on dematerialization theory, institutional and legitimacy theory, and the sufficiency model, this research investigates NFTs’ role in promoting sustainable consumption and brand legitimacy. Building on insights from a preliminary qualitative study, three experiments test how product type (non-NFT, NFT, digital twin) affects purchase likelihood and how perceived product sustainability and brand legitimacy moderate and mediate these effects. Study 1 shows that digital twin products combining physical and NFT components yield the highest likelihood of purchase. Study 2 finds the positive effect of NFTs strengthens when perceived product sustainability is high. Study 3 reveals perceived product sustainability acts as a boundary condition, shaping how product type influences brand legitimacy and purchase likelihood. Findings offer theoretical insights and actionable guidance for managers.
Open access
Consumer Behavior in Brand Consumption and Identification
Tradicionalne metode preverjanja prisotnosti, kot so ročno beleženje ali QR kode, so podvržene manipulaciji in ne zagotavljajo zadostne varnosti ter zasebnosti uporabnikov. Magistrsko delo naslavlja te izzive z razvojem decentraliziranega sistema za preverjanje fizične prisotnosti, ki temelji na tehnologiji verige blokov in ničelno spoznavnih dokazih (zk-SNARK). Sistem integrira ZoKrates ogrodje za generiranje zasebnih dokazov, geolokacijsko verifikacijo z GPS koordinatami, Ethereum pametne pogodbe ter hibridni pristop k shranjevanju podatkov. Implementirani so bili večplatformski uporabniški vmesniki (spletna in mobilna aplikacija) z različnimi načini potrjevanja prisotnosti. Razvita rešitev predstavlja funkcionalen in robusten sistem, ki omogoča varno ter transparentno preverjanje prisotnosti brez razkrivanja osebnih podatkov uporabnikov.
This paper investigates the dual impact of Decentralized Finance (DeFi) and smart contracts on European Union (EU) market stability, with a focus on the role of regulation. The research problem centers on understanding how the rapid growth of DeFi interacts with emerging regulatory frameworks to shape financial stability. The purpose is to provide an integrated analysis that combines quantitative data with qualitative legal insights to inform policy. The methodology employs a fixed-effects panel data model to analyze the effect of DeFi market capitalization, smart contract deployments, and transaction volumes on a market stability index across EU member states, while also incorporating a qualitative review of the EU’s regulatory landscape, including the Markets in Crypto-Assets (MiCA) Regulation [1]. Key findings indicate that while DeFi’s growth correlates with increased market volatility, regulatory interventions like MiCA appear to have a stabilizing effect. The paper concludes that a clear and harmonized regulatory framework is crucial for mitigating the risks associated with DeFi while fostering responsible innovation. The relevance of this study lies in its timely contribution to the ongoing policy debate on DeFi regulation and its implications for financial stability in the EU [2].
This paper examines the market reaction to the approval of spot Bitcoin and Ethereum exchange-traded funds (ETFs), focusing on the return dynamics of a functionally diverse types of leading cryptocurrencies, including coins (BTC, BCH, LTC, XRP), smart contract platforms (ETH, ADA, AVAX), and utility tokens (LINK, MATIC). Using high-frequency intraday data, we perform an event study to assess the abnormal returns around the ETF approval dates. This study makes a significant contribution to the literature on event studies by being the first to examine investors’ reactions to information arrival in a “primary market.” Both the market model and the capital asset pricing model (CAPM) are applied to evaluate the effects of ETF approval on individual asset returns. Our results reveal that spot Bitcoin ETF approval by the US Securities and Exchange Commission leads to significant positive abnormal returns, along with heightened market volatility. In contrast, spot Ethereum ETF approval has had more modest effects. Moreover, we observe considerable shifts in the volatility spillovers among Bitcoin, Ethereum, and other major cryptocurrencies after the ETF approval, reflecting a change in market sentiment and interconnectedness. This analysis enhances understanding of how institutional products, such as ETFs, shape cryptocurrency market behavior, offering valuable insights for regulatory frameworks and investor strategies.
Ensuring ballot secrecy is critical for fair and trustworthy electronic voting systems, yet achieving strong secrecy guarantees in decentralized, large-scale elections remains challenging. This paper proposes the concept of collectively secure voting, in which voters themselves can opt in as secret holders to protect ballot secrecy. A practical blockchain-based collectively secure voting system is designed and implemented. Our design strikes a balance between strong confidentiality guarantees and real-world applicability. The proposed system combines threshold cryptography and smart contracts to ensure ballots remain confidential during voting, while all protocol steps remain transparent and verifiable. Voters can use the system without prior blockchain knowledge through an intuitive user interface that hides underlying complexity. To evaluate this approach, a user testing is conducted. Results show a high willingness to act as secret holders, reliable participation in share release, and high security confidence in the proposed system. The findings demonstrate that voters can collectively maintain secrecy and that such a practical deployment is feasible.
Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24/7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market detection critical for risk management. We introduce a theoretically-grounded multi-agent cryptocurrency trend prediction framework that advances the state-of-the-art through three key innovations: (1) an information-preserving news analysis system with formal theoretical guarantees that systematically quantifies market impact, regulatory implications, volume dynamics, risk assessment, technical correlation, and temporal effects using large language models; (2) an adaptive volatility-conditional fusion mechanism with proven optimal properties that dynamically combines news sentiment and technical indicators based on market regime detection; (3) a distributed multi-agent coordination architecture with low communication complexity enabling real-time processing of heterogeneous data streams. Comprehensive experimental evaluation on Bitcoin across three prediction horizons demonstrates statistically significant improvements over state-of-the-art natural language processing baseline, establishing a new paradigm for financial machine learning with broad implications for quantitative trading and risk management systems.
The scalability of blockchain systems is constrained by inefficient P2P broadcasting, as most existing optimizations focus only on the logical layer without considering physical network conditions. To address this, we propose BlockSDN, the first SDN-based integrated architecture for blockchain. BlockSDN employs a distributed control plane for a global network view, a graph engine for hierarchical clustering, and a hybrid macro-micro neighbor selection with hierarchical broadcasting. A dedicated simulation platform shows that BlockSDN reduces global block synchronization time by 65% and 55% compared to Gossip and Mercury, respectively.These results highlight the potential of SDN-enabled cross-layer coordination to significantly enhance blockchain scalability and performance.
Qiushi Tian, Churong Liang, Kairan Hong, Runnan Li
Cryptocurrency markets present formidable challenges for trading strategy optimization due to extreme volatility, non-stationary dynamics, and complex microstructure patterns that render conventional parameter optimization methods fundamentally inadequate. We introduce Cypto Genetic Algorithm Agent (CGA-Agent), a pioneering hybrid framework that synergistically integrates genetic algorithms with intelligent multi-agent coordination mechanisms for adaptive trading strategy parameter optimization in dynamic financial environments. The framework uniquely incorporates real-time market microstructure intelligence and adaptive strategy performance feedback through intelligent mechanisms that dynamically guide evolutionary processes, transcending the limitations of static optimization approaches. Comprehensive empirical evaluation across three cryptocurrencies demonstrates systematic and statistically significant performance improvements on both total returns and risk-adjusted metrics.
The necessity of blockchain systems to remain decentralised limits current solutions to blockchain governance and dynamic management, forcing a trade-off between control and decentralisation. In light of the above, this work proposes a dynamic and decentralised blockchain management mechanism based on digital twins. To ensure decentralisation, the proposed mechanism utilises multiple digital twins that the system's stakeholders control. To facilitate decentralised decision-making, the twins are organised in a secondary blockchain system that orchestrates agreement on, and propagation of decisions to the managed blockchain. This enables the management of blockchain systems without centralised control. A preliminary evaluation of the performance and impact of the overheads introduced by the proposed mechanism is conducted through simulation. The results demonstrate the proposed mechanism's ability to reach consensus on decisions quickly and reconfigure the primary blockchain with minimal overhead.
Quantum blockchains provide inherent resilience against quantum adversaries and represent a promising alternative to classical blockchain systems in the quantum era. However, existing quantum blockchain architectures largely depend on entanglement to maintain inter-block connections, facing challenges in stability, consensus efficiency, and system verification. To address these issues, this work proposes a novel quantum blockchain framework based on quantum walks, which reduces reliance on entanglement while improving stability and connection efficiency. We further propose a quantum consensus mechanism based on a weighted quantum voting protocol, which enables a fairer voting process while reflecting the weights of different nodes. To validate the proposed framework, we conduct circuit simulations to evaluate the correctness and effectiveness of both the quantum walk-based block construction and the quantum voting consensus mechanism. Compared with existing entanglement-dependent approaches, our framework achieves stronger stability and enables simpler verification of block integrity, making it a practical candidate for quantum-era blockchain applications.
Introduction to the Problem: This article examines the U.S. strategy for countering corruption and the increasing challenges of money laundering involving cryptocurrencies in a globalized financial ecosystem. As digital assets gain legitimacy, they have simultaneously become tools for illicit finance, prompting the need for coordinated global regulatory efforts. The United States, home to the world’s largest crypto exchanges and a leading jurisdiction for asset seizures, has developed a comprehensive Five-Pillar Strategy emphasizing global coordination and institutional strengthening. Purpose/Objective Study: This study analyzes how U.S. policy frameworks, including those under the Commodity Futures Trading Commission (CFTC), Financial Crimes Enforcement Network (FinCEN), and Dodd-Frank Act, respond to transnational threats of corruption, crypto-related crime, and illicit finance. It assesses how these measures promote transparency and shape international cooperation mechanisms. Design/Methodology/Approach: Using a mixed-method legal approach grounded in methodological pluralism, this research integrates normative legal analysis, legal sociology, and neoliberal institutionalism to evaluate the adaptive capacity of global coordination in addressing crypto-related financial crimes. Findings: The study finds that effective responses to crypto-based corruption require not only domestic policy coherence but also institutionalized multilateral coordination anchored in international regimes such as the Financial Action Task Force (FATF), the UN Convention against Corruption (UNCAC), and the OECD’s Crypto-Asset Reporting Framework. The U.S. Five-Pillar Strategy strengthens transparency through beneficial ownership reporting, enhances the detection of illicit transactions via FinCEN and CFTC oversight, and reinforces cross-border collaboration through FATF and UNCAC partnerships. These frameworks collectively represent a pragmatic application of neoliberal institutionalism (where institutions mitigate the risks of an anarchic financial order) and sociological jurisprudence, which treats law as a dynamic tool of social engineering. However, gaps persist in enforcement harmonization and data-sharing, underscoring the continued need for adaptive and inclusive global coordination mechanisms. Paper Type: Research Article