Mandeep Kaur, Usharani J Vandana Rastogi, Alim Al Ayub Ahmed Divya N
Blockchain is gradually finding its way into the financial industry and seems to be a potential solution to traditional banking problems. Specifically, through real-time transactions, increasing the level of openness and reducing the costs of work, blockchain can revolutionize the financial market worldwide. This empirical study aims at exploring the disruptive nature of blockchain with reference to cross border payments, smart contracts and fraud. This research employs secondary research techniques together with critical models like Distributed Ledger Analysis and Cost-Benefit Analysis to establish the efficiency and possibilities of the blockchain than the conventional systems. The results reveal that even though blockchain has certain benefits in terms of efficiency and decentralisation, such issues as the system’s capacity, its power consumption, and legal ambiguity exist. For future research, the current study’s limitations should be considered while future studies should also look at the effects of the technology beyond the current advanced economies and emerging economies.
This study aims to demonstrate the role of explicit and implicit will in determining the law applicable to smart contracts. Traditional attribution criteria have become incapable of determining the law of digital contracts. This requires a more effective legal system that is compatible with the nature of this type of digital dispute, ensuring legal security and protecting the legal positions of the parties to the contract. This study was conducted using an analytical approach, analyzing relevant legal texts in national and international laws, in addition to a comparative legal approach to study comparative laws in the Anglo-American and Latin American systems, to demonstrate the role of these systems in establishing rules for smart contract operations through digital platforms. The study revealed that the explicit will is the best traditional solution available in legal systems for determining the law applicable to smart contracts. While implicit intention has diminished the importance of the unified elements of a smart contract across all contracts, rendering it incapable of establishing a method for determining contract law. The virtual and decentralized nature of these contracts has led many legislators to refrain from addressing them, given the difficulty of creating a legal system in light of the infrastructure that requires development to accommodate contractual processes in this type of contract. Legal development in the field of smart contracts and artificial intelligence is necessary through the study of technical aspects by specialists to develop a substantive law that addresses the legal issues that arise when implementing smart contracts similar to electronic contracts. This law also addresses the issue of determining the law applicable to the international nature of this type of contract, or through developing attribution criteria that align with the nature of virtual disputes.
Ylva Baeckström, Akanksha Jalan, Roman Matkovskyy, Julia Roloff
Abstract Individual investors dominate the rapidly growing US$2.73 trillion cryptocurrency market. Cryptocurrencies are highly controversial because of their real and expected ethical and environmental impacts. Surveying 1500 individual investors in Denmark, Finland, and Sweden, we reveal that beliefs about the ethical, sustainability, and environmental implications of cryptocurrencies influence current and intended ownership. While future participation intentions are predicated on currently owning cryptocurrencies, this relationship is moderated by investors’ ethical and sustainability perceptions. Cryptocurrency knowledge and education significantly moderate the relationship between belief and intended ownership. Furthermore, we identify notable gender differences: ethical beliefs more strongly mediate future holding intentions among men, while sustainability perceptions have a greater mediating effect among women. In line with dual-process theory concepts, previous cryptocurrency trading experience and knowledge further reinforce this relationship. Our research has broad relevance to stakeholders, including policy makers, particularly in light of the current debate about Fintech’s role in fostering financial inclusion and the dubious ethical, sustainable, and environmental position of cryptocurrency mining and trading.
The rule of law is a critical support for the national governance system and governance capacity. The legalization of public finance construction is the foundation for achieving fiscal fairness, efficiency, and justice, promoting the rational allocation of social resources, maintaining social stability, and promoting sustainable economic growth. It is of great significance for building a rule of law government and improving modern budget systems. The legalization of public finance construction should follow the principles of fairness and justice, rule of law governance, and transparency, providing strong institutional guarantees for the sustainable development of public finance. This study takes public finance construction as the research object and focuses on the rule of law principles and practical paths of public finance construction. It analyzes the rule of law principles from a theoretical perspective and proposes practical paths from a practical perspective. At present, the decentralization of budget preparation entities, chaotic internal relationships within administrative institutions, and slow progress in the rule of law are all practical problems that plague the construction of public finance in accordance with the rule of law. In response to these issues, research explores and proposes a series of specific strategies to promote the legalization of public finance from the dimensions of establishing the principle of rule of law, improving the legal system framework, optimizing law enforcement operation mechanisms, and strengthening the supervision system, to provide theoretical support and practical basis for the reform of China's public finance system.
Ramazan Bektaş, Kerim Eser AFŞAR, Ahmet Aydın Arı
Cryptocurrencies initially gained prominence by eliminating intermediaries in payment systems and later found applications in various business sectors. The crypto network, pioneered by Bitcoin, has spurred new business forms and organizational structures with diverse motivations. Bitcoin's emergence is technically dated to 2008. However, its ideological and technical roots trace back to the cyberpunk literature of the late 1970s and the cypherpunk movement that began in California in 1992. The cypherpunk manifestos significantly influenced cryptographic work, shaping Bitcoin's technical foundation. This study aims to explore Bitcoin's ideological origins through a qualitative content analysis of cypherpunk manifestos, Nakamoto's posts on the "Bitcointalk" forum, and "Cryptography Mailing List" correspondence. By examining these sources, the study identifies the historical dimensions of Bitcoin's technical structure and highlights the impact of ideological debates on its development. Findings reveal that while cryptographic research influenced Bitcoin's technical evolution, ideological discussions were relatively less significant. Nonetheless, Bitcoin's developers, particularly Nakamoto, incorporated a strong ideological emphasis on "privacy" despite the primary technical focus.
Abstract The advent of blockchain technology has achieved notable progress regarding security, particularly within the realm of e-commerce. The existing Web 2.0 framework, which employs inadequate security measures, exhibits vulnerabilities when compared to the robust security features of blockchain technology. The utilization of monitors, computers, and data storage exemplifies the functionality of blockchain technology, which upholds encrypted and distributed transaction records across multiple computers, consequently improving the reliability of the digital ledger. In a nation such as Bangladesh, where transaction data is susceptible to cyber threats and online fraud is prevalent within the e-commerce sector, this type of decentralized system has the potential to alter the landscape significantly. This requires the implementation of a more comprehensive security protocol. This research advocates for the adoption of smart contracts to enhance supply chain transparency and offers digital identification solutions aimed at preventing fraud, including issues related to non-delivery and counterfeit goods. This research utilizes Next.js for front-end development and facilitates backend integration through Solidity and Hardhat.js, specifically for the Solana Blockchain, deployed on an Amazon EC2 instance. This research commenced with an examination of the current e-commerce ecosystem, physical identification infrastructure, and consumer attitudes, ultimately presenting a strategic implementation plan for the adoption of blockchain technology to enhance trust and assurance within the e-commerce landscape of Bangladesh. It further delineates particular obstacles to adoption: technological limitations, regulatory challenges, socio-economic factors, and the expanding digital payments landscape, particularly concerning mobile financial services. This research enhances the current understanding of blockchain as a transformative force in emerging e-commerce markets and provides valuable insights into technology policies relevant to the developing economy of Bangladesh for policymakers, businesses, and technologists. Graphical abstract
This study constructs a machine learning-driven multi-factor model for Ethereum quantitative trading, combining traditional technical indicators (RSI, MACD), on-chain metrics (gas usage, active addresses), and X platform social sentiment to predict short-term returns. Backtesting from Q4 2021 to Q3 2024, using online learning and genetic algorithms for dynamic factor updates, yields a 97% annualized return, a Sharpe ratio of 2.5, and an information ratio of 1.2, outperforming Ethereum's raw returns. Simulated trading in Q4 2024 (bull market) achieves a 33% quarterly return with an 18% maximum drawdown, while Q1 2025 (bear market) records a -10% quarterly return with a 12% drawdown, confirming robustness. Technical and sentiment factors drive performance, though a 22% maximum drawdown in backtesting highlights volatility risks. An optimal Z-score threshold (±1.0) and 4-hour trading frequency balance profitability and costs. Future enhancements include high-frequency mainnet data integration and advanced risk management to strengthen model resilience in Ethereum's volatile market.
This paper explores the evolution of financial technology (fintech) from early digital banking to today’s AI-driven, blockchain-enabled financial ecosystems. It examines how fintech has disrupted traditional banking models by enhancing efficiency, inclusion, and transparency. Through global case studies and emerging market insights, the research highlights innovations in mobile payments, robo-advisors, decentralized finance (DeFi), and regulatory responses like sandboxes and open banking. It also discusses cybersecurity, ethical risks, and the role of AI and quantum computing in shaping fintech’s future. The study argues for a balanced approach combining innovation, regulation, and ethics to ensure sustainable financial transformation.
Smart contract upgrades are increasingly common due to their flexibility in modifying deployed contracts, such as fixing bugs or adding new functionalities. Meanwhile, upgrades compromise the immutability of contracts, introducing significant security concerns. While existing research has explored the security impacts of contract upgrades, these studies are limited in collection of upgrade behaviors and identification of insecurities. To address these limitations, we conduct a comprehensive study on the insecurities of upgrade behaviors. First, we build a dataset containing 83,085 upgraded contracts and 20,902 upgrade chains. To our knowledge, this is the first large-scale dataset about upgrade behaviors, revealing their diversity and exposing gaps in public disclosure. Next, we develop a taxonomy of insecurities based on 37 real-world security incidents, categorizing eight types of upgrade risks and providing the first complete view of upgrade-related insecurities. Finally, we survey public awareness of these risks and existing mitigations. Our findings show that four types of security risks are overlooked by the public and lack mitigation measures. We detect these upgrade risks through a preliminary study, identifying 31,407 related issues - a finding that raises significant concerns.
Stablecoins have become a foundational component of the digital asset ecosystem, with their market capitalization exceeding 230 billion USD as of May 2025. As fiat-referenced and programmable assets, stablecoins provide low-latency, globally interoperable infrastructure for payments, decentralized finance, DeFi, and tokenized commerce. Their accelerated adoption has prompted extensive regulatory engagement, exemplified by the European Union's Markets in Crypto-assets Regulation, MiCA, the US Guiding and Establishing National Innovation for US Stablecoins Act, GENIUS Act, and Hong Kong's Stablecoins Bill. Despite this momentum, academic research remains fragmented across economics, law, and computer science, lacking a unified framework for design, evaluation, and application. This study addresses that gap through a multi-method research design. First, it synthesizes cross-disciplinary literature to construct a taxonomy of stablecoin systems based on custodial structure, stabilization mechanism, and governance. Second, it develops a performance evaluation framework tailored to diverse stakeholder needs, supported by an open-source benchmarking pipeline to ensure transparency and reproducibility. Third, a case study on Real World Asset tokenization illustrates how stablecoins operate as programmable monetary infrastructure in cross-border digital systems. By integrating conceptual theory with empirical tools, the paper contributes: a unified taxonomy for stablecoin design; a stakeholder-oriented performance evaluation framework; an empirical case linking stablecoins to sectoral transformation; and reproducible methods and datasets to inform future research. These contributions support the development of trusted, inclusive, and transparent digital monetary infrastructure.
The increasing reliance on cloud services demands advanced security mechanisms to protect sensitive data and ensure robust access control. This study addresses critical challenges in cloud security by proposing a novel framework that integrates blockchain-based smart contracts to enhance authorization and authentication processes. Smart contracts, as self-executing agreements embedded with predefined rules, enable decentralized, transparent, and tamper-proof mechanisms for managing access control in cloud environments. The proposed system mitigates prevalent threats such as unauthorized access, data breaches, and identity theft through an immutable and auditable security framework. A prototype system, developed using Ethereum blockchain and Solidity programming, demonstrates the feasibility and effectiveness of the approach. Rigorous evaluations reveal significant improvements in key metrics: security, with a 0% success rate for unauthorized access attempts; scalability, maintaining low response times for up to 100 concurrent users; and usability, with an average user satisfaction rating of 4.4 out of 5. These findings establish the efficacy of smart contract-based solutions in addressing critical vulnerabilities in cloud services while maintaining operational efficiency. The study underscores the transformative potential of blockchain and smart contracts in revolutionizing cloud security practices. Future research will focus on optimizing the system’s scalability for higher user loads and integrating advanced features such as adaptive authentication and anomaly detection for enhanced resilience across diverse cloud platforms.
Abstract Federated Learning (FL) has emerged as a promising distributed machine learning approach that addresses confidentiality and integrity concerns in various sectors, including Internet of Things (IoT), healthcare, finance, and cybersecurity. In order to improve privacy protection and detection accuracy in decentralized systems, this study investigates the incorporation of FL into Intrusion Detection Systems (IDS). FL is especially useful in situations where data security and privacy are crucial because it allows for the cooperative training of models without centralizing sensitive data. We examine many FL-based IDS solutions across several domains, emphasizing how well they mitigate data breaches, maintain confidentiality, and enhance intrusion detection capabilities. The use of Generative Adversarial Networks (GANs), artificial immune systems, and hybrid deep learning techniques to maximize IDS performance are among the current developments in FL methodology that are covered in the paper. We also look at issues like the requirement for effective aggregation procedures and non-independent and identically distributed (non-IID) data. Finally, we outline future directions and open research topics to improve the scalability, resilience, and effectiveness of FL-based IDS solutions in practical applications.
Siamak Abdi, Giuseppe Di Fatta, Atta Badii, Giancarlo Fortino
Blockchain is a distributed ledger technology that has applications in many domains such as cryptocurrency, smart contracts, supply chain management, and many others. Distributed consensus is a fundamental component of blockchain systems that enables secure, precise, and tamper-proof verification of data without relying on central authorities. Existing consensus protocols, nevertheless, suffer from drawbacks, some of which are related to scalability, resource consumption, and fault tolerance. We introduce Blockchain Epidemic Consensus Protocol (BECP), a novel fully decentralised consensus protocol for blockchain networks at a large scale. BECP follows epidemic communication principles, without fixed roles like validators or leaders, and achieves probabilistic convergence, efficient message dissemination, and tolerance to message delays. We provide an extensive experimental comparison of BECP against classic protocols like PAXOS, RAFT, and PBFT, and newer epidemic-based protocols like Avalanche and Snowman. The findings indicate that BECP provides desirable gains in throughput, consensus latency, and substantial message-passing efficiency compared to existing epidemic-based approaches, validating its usability as an effective and scalable approach for next-generation blockchain systems.
The increasing penetration of renewable energy sources in day-ahead energy markets introduces challenges in balancing supply and demand, ensuring grid resilience, and maintaining trust in decentralized trading systems. This paper proposes a novel framework that integrates the Proximal Policy Optimization (PPO) algorithm, a state-of-the-art reinforcement learning method, with blockchain technology to optimize automated trading strategies for prosumers in day-ahead energy markets. We introduce a comprehensive framework that employs RL agent for multi-objective energy optimization and blockchain for tamper-proof data and transaction management. Simulations using real-world data from the Electricity Reliability Council of Texas (ERCOT) demonstrate the effectiveness of our approach. The RL agent achieves demand-supply balancing within 2\% and maintains near-optimal supply costs for the majority of the operating hours. Moreover, it generates robust battery storage policies capable of handling variability in solar and wind generation. All decisions are recorded on an Algorand-based blockchain, ensuring transparency, auditability, and security - key enablers for trustworthy multi-agent energy trading. Our contributions include a novel system architecture, curriculum learning for robust agent development, and actionable policy insights for practical deployment.
Accurate volatility forecasts are vital in modern finance for risk management, portfolio allocation, and strategic decision-making. However, existing methods face key limitations. Fully multivariate models, while comprehensive, are computationally infeasible for realistic portfolios. Factor models, though efficient, primarily use static factor loadings, failing to capture evolving volatility co-movements when they are most critical. To address these limitations, we propose a novel, model-agnostic Factor-Augmented Volatility Forecast framework. Our approach employs a time-varying factor model to extract a compact set of dynamic, cross-sectional factors from realized volatilities with minimal computational cost. These factors are then integrated into both statistical and AI-based forecasting models, enabling a unified system that jointly models asset-specific dynamics and evolving market-wide co-movements. Our framework demonstrates strong performance across two prominent asset classes-large-cap U.S. technology equities and major cryptocurrencies-over both short-term (1-day) and medium-term (7-day) horizons. Using a suite of linear and non-linear AI-driven models, we consistently observe substantial improvements in predictive accuracy and economic value. Notably, a practical pairs-trading strategy built on our forecasts delivers superior risk-adjusted returns and profitability, particularly under adverse market conditions.
Emergency pharmaceutical logistics during rapid-onset disasters must balance timeliness, legal compliance, and environmental uncertainty. We present a hybrid framework that co-designs quantum-inspired decision dynamics, embedded legal constraints, and blockchain-verified environmental feedback. Candidate routes are modeled as a superposed state whose collapse is governed by entropy modulation-delaying commitment under ambiguity and accelerating resolution when coherent signals emerge. Legal statutes act as real-time projection operators shaping feasible choices, while environmental decoherence cues adjust confidence and path viability. The core engine is situated within a multilevel governance and mechanism design architecture, establishing clear roles, accountability channels, and audit trails. Large-scale simulations in wildfire scenarios demonstrate substantial gains over conventional baselines in latency, compliance, and robustness, while preserving interpretability and fairness adaptation. The resulting system offers a deployable, governance-aware infrastructure where law and physical risk jointly inform emergency routing decisions.
Synthetic time series are essential tools for data augmentation, stress testing, and algorithmic prototyping in quantitative finance. However, in cryptocurrency markets, characterized by 24/7 trading, extreme volatility, and rapid regime shifts, existing Time Series Generation (TSG) methods and benchmarks often fall short, jeopardizing practical utility. Most prior work (1) targets non-financial or traditional financial domains, (2) focuses narrowly on classification and forecasting while neglecting crypto-specific complexities, and (3) lacks critical financial evaluations, particularly for trading applications. To address these gaps, we introduce \textsf{CTBench}, the first comprehensive TSG benchmark tailored for the cryptocurrency domain. \textsf{CTBench} curates an open-source dataset from 452 tokens and evaluates TSG models across 13 metrics spanning 5 key dimensions: forecasting accuracy, rank fidelity, trading performance, risk assessment, and computational efficiency. A key innovation is a dual-task evaluation framework: (1) the \emph{Predictive Utility} task measures how well synthetic data preserves temporal and cross-sectional patterns for forecasting, while (2) the \emph{Statistical Arbitrage} task assesses whether reconstructed series support mean-reverting signals for trading. We benchmark eight representative models from five methodological families over four distinct market regimes, uncovering trade-offs between statistical fidelity and real-world profitability. Notably, \textsf{CTBench} offers model ranking analysis and actionable guidance for selecting and deploying TSG models in crypto analytics and strategy development.
Annes Maria Pangidoan, Putu Wira Buana, Fajar Purnama
Data, including digital and physical documents, is a valuable asset often vulnerable to forgery, theft, and reliance on centralized servers, which are costly and prone to failure. This study develops a prototype of a decentralized document storage application by combining blockchain and the InterPlanetary File System (IPFS). The system is designed as a web-based decentralized application (DApp), integrating Ethereum smart contracts to immutably record document metadata and access history, while the actual files are stored in IPFS and identified using unique Content Identifiers (CIDs). User interactions are facilitated through MetaMask for authentication and transaction approval. The system is developed using the Waterfall methodology. Functional testing is conducted through unit tests using Ganache as a local Ethereum blockchain, and the smart contract is also deployed to the Sepolia Ethereum testnet. The results show that the system successfully stores documents via IPFS and records metadata and access activities transparently on the blockchain. Access and download tracking features enhance document accountability. This solution provides a secure, efficient, and transparent alternative to centralized document storage and contributes to the advancement of distributed digital archiving systems.
Anak Agung Lingga Pratyaksa Nugraha, Ni Wayan Emmy Rosiana Dewi, Fajar Purnama
The development of the entertainment industry, especially music concerts, has driven the transformation of ticket sales systems from conventional to digital methods. Although online concert ticket sales offer greater convenience and reach, they still face the risks of fraud, counterfeit tickets, and unfair distribution. This study, Blockchain Smart Contract Implementation for NFT-Based Online Music Concert Ticket Transactions, aims to develop a ticket sales system using blockchain technology by integrating smart contracts and Non-Fungible Tokens (NFTs). The main objectives are to design and implement smart contracts on the Ethereum network, implement ERC-721-based digital tickets, ensure transparency in transaction history, and verify ticket authenticity through unique identifiers. This study adopts the Agile method, with implementation on the Ethereum Sepolia Testnet and testing using the meta mask digital wallet. The results show that the developed system can automatically hold funds through an escrow mechanism until the ticket is downloaded, generate unique and tamper-proof NFT tickets, display transaction details transparently, and facilitate ticket verification effectively. In conclusion, the use of smart contracts and NFTs significantly improves the security, transparency, and trustworthiness of online music concert ticket transactions.
Academic publishing, integral to knowledge dissemination and scientific advancement, increasingly faces threats from unethical practices such as unconsented authorship, gift authorship, author ambiguity, and undisclosed conflicts of interest. While existing infrastructures like ORCID effectively disambiguate researcher identities, they fall short in enforcing explicit authorship consent, accurately verifying contributor roles, and robustly detecting conflicts of interest during peer review. To address these shortcomings, this paper introduces a decentralized framework leveraging Self-Sovereign Identity (SSI) and blockchain technology. The proposed model uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to securely verify author identities and contributions, reducing ambiguity and ensuring accurate attribution. A blockchain-based trust registry records authorship consent and peer-review activity immutably. Privacy-preserving cryptographic techniques, especially Zero-Knowledge Proofs (ZKPs), support conflict-of-interest detection without revealing sensitive data. Verified authorship metadata and consent records are embedded in publications, increasing transparency. A stakeholder survey of researchers, editors, and reviewers suggests the framework improves ethical compliance and confidence in scholarly communication. This work represents a step toward a more transparent, accountable, and trustworthy academic publishing ecosystem.
In an increasingly globalized financial ecosystem, cross-border payment systems continue to face persistent challenges, including high transaction costs, settlement delays, regulatory fragmentation, and exposure to counterparty risk. Traditional banking infrastructures, reliant on correspondent banking networks, are often opaque, inefficient, and vulnerable to compliance breaches and fraud. This study investigates the application of blockchain-based smart contracts as a transformative solution to these longstanding inefficiencies in international finance. From a macro perspective, blockchain’s distributed ledger architecture offers enhanced transparency, immutability, and consensus-driven validation, presenting a robust framework for automating and securing cross-border settlements. The research evaluates the operational mechanisms of smart contracts self-executing code embedded within blockchain protocols that facilitate real-time, trustless transaction execution and regulatory rule enforcement across jurisdictions. A key focus is the integration of Know Your Customer (KYC), Anti-Money Laundering (AML), and Central Bank Digital Currency (CBDC) compliance checks within programmable contracts to ensure legal adherence while reducing operational bottlenecks. The study also explores case applications by global fintech firms and intergovernmental consortia experimenting with blockchain for real-time gross settlement (RTGS), payment-versus-payment (PvP), and delivery-versus-payment (DvP) models. Findings indicate that blockchain-based smart contracts significantly lower cross-border transaction costs, reduce settlement times from days to minutes, and enhance auditability for regulators. However, interoperability, legal recognition, and jurisdictional variance in digital asset treatment remain unresolved obstacles. The paper concludes by proposing a hybrid governance framework combining decentralized architecture with regulatory oversight, enabling secure, compliant, and frictionless global payment infrastructure.
Non-fungible tokens (NFTs) have emerged as a transformative innovation in art and technology, relying heavily on social networks for promotion and revenue generation. The value of NFTs is profoundly influenced by their scarcity, rarity, and unique breeding mechanisms, which present novel challenges for viral marketing strategies. In this paper, we introduce a new research problem of NFT Revenue Maximization (NRM), which focuses on maximizing revenue from the perspective of NFT marketplaces by optimally selecting users for viral marketing campaigns (NFT airdrops) and determining the ideal quantities of NFTs to release. We prove the hardness of NRM and propose an approximation algorithm named Quantity and Offspring-Oriented Airdrops (QOOA). Our algorithm leverages the concepts of Scarcity-Conscious Revenue and Valuation-based Quantity Inequality to prune suboptimal airdrops and quantities at an early stage. To further enhance revenue through NFT breeding, QOOA identifies and incentivizes Rare Trait Collectors to acquire multiple NFTs with rare traits, facilitating the breeding of high-value offspring. Experimental results demonstrate that QOOA significantly outperforms baselines, achieving up to 3.8 times higher revenue in large-scale social networks.
Mirza Ahad Baig, Christoph U. Günther, Krzysztof Pietrzak
The blocks in the Bitcoin blockchain record the amount of work W that went into creating them through proofs of work. When honest parties control a majority of the work, consensus is achieved by picking the chain with the highest recorded weight. Resources other than work have been considered to secure such longest-chain blockchains. In Chia, blocks record the amount of space S (via a proof of space) and sequential computational steps V (via a VDF). In this paper, we ask what weight functions Γ(S,V,W) (that assign a weight to a block as a function of the recorded space, speed, and work) are secure in the sense that whenever the weight of the resources controlled by honest parties is larger than the weight of adversarial parties, the blockchain is secure against private double-spending attacks. We completely classify such functions in an idealized "continuous" model: Γ(S,V,W) is secure against private double-spending attacks if and only if it is homogeneous of degree one in the timed resources V and W, i.e., αΓ(S,V,W)=Γ(S,αV, αW). This includes Bitcoin rule Γ(S,V,W)=W and Chia rule Γ(S,V,W) = SV. In a more realistic model where blocks are created at discrete time-points, one additionally needs some mild assumptions on the dependency on S (basically, the weight should not grow too much if S is slightly increased, say linear as in Chia). Our classification is more general and allows various instantiations of the same resource. It provides a powerful tool for designing new longest-chain blockchains. E.g., consider combining different PoWs to counter centralization, say the Bitcoin PoW W_1 and a memory-hard PoW W_2. Previous work suggested to use W_1+W_2 as weight. Our results show that using {\sqrt}(W_1){\cdot}{\sqrt}(W_2), {\min}{W_1,W_2} are also secure, and we argue that in practice these are much better choices.
Smart contracts are important for digital finance, yet they are hard to patch once deployed. Prior work has mainly explored LLMs for smart contract vulnerability detection, leaving end-to-end automated exploit generation (AEG) much less understood. We study that gap with \textsc{ReX}, an execution-grounded framework that links LLM-based exploit synthesis to the Foundry stack for end-to-end generation, compilation, execution, and validation. Five recent LLMs are evaluated across eight common vulnerability classes, supported by a curated dataset of 38{+} real incident PoCs and three automation aids: prompt refactoring, a compiler feedback loop, and templated test harnesses. Results indicate that current frontier LLMs can often produce deterministic PoCs for single-contract vulnerabilities, but remain weak on cross-contract attacks; outcomes depend mainly on the model and bug type, while code structure and prompt tuning contribute less in our setting. The study also surfaces important boundary conditions of LLM-driven AEG, including gaps between oracle-validated exploitability and real-world economic attacks, pointing to the need for stronger defenses and more realistic evaluation.