Mauro Cambarieri, Claudia Alejandra Viadana, Nicolás García Martínez, Luis Vivas · 6 authors
This paper explores the potential of blockchain and Web3 technologies in the digital transformation of public entities, focusing on digital identity management and the issuance of verifiable credentials. Key concepts of these technologies, as well as W3C standards, are analyzed, highlighting their ability to ensure integrity, security, portability, and transparency in administrative processes. In the public sector, blockchain offers benefits such as the elimination of intermediaries, automation through smart contracts, asset tokenization, and improved interoperability. In education, the accelerated evolution of the labor market—driven by technological advancements and demands for dynamic specialization—has positioned micro-credentials as critical components for professional reinvention. However, their effective implementation requires overcoming challenges related to interoperability, security, and portability. This is where digital identity and verifiable credentials (VCs) emerge as key enablers of transformation. VCs streamline the issuance and verification of academic certifications, promoting employability and the portability of skills. The case study presented leverages the Digital Credentials Consortium (DCC) to implement VCs based on standards such as JSON-LD and decentralized identifiers (DIDs). This work outlines conceptual frameworks, technical implementation details, and contextual considerations for the adoption of Web3 technologies.
This study addresses critical challenges in managing the transportation of spent nuclear fuel, including inadequate data transparency, stringent confidentiality requirements, and a lack of trust among collaborating parties, issues prevalent in traditional centralized management systems. Given the high risks involved, balancing data confidentiality with regulatory transparency is imperative. To overcome these limitations, a prototype system integrating blockchain technology and the Internet of Things (IoT) is proposed, featuring a multi-tiered consortium chain architecture. This system utilizes IoT sensors for real-time data collection, which is immutably recorded on the blockchain, while a hierarchical data structure (operational, supervisory, and public layers) manages access for diverse stakeholders. The results demonstrate that this approach significantly enhances data immutability, enables real-time multi-sensor data integration, improves decentralized transparency, and increases resilience compared to traditional systems. Ultimately, this blockchain-IoT framework improves the safety, transparency, and efficiency of spent fuel transportation, effectively resolving the conflict between confidentiality and transparency in nuclear data management and offering significant practical implications.
Krzysztof Gogol, Szczepan Gurgul, Faizan Nehal Siddiqui, David Branes · 5 authors
Ethereum's scalability limitations pose significant challenges for the adoption of decentralized applications (dApps). Zero-Knowledge Rollups (ZK Rollups) present a promising solution, bundling transactions off-chain and submitting validity proofs on-chain to enhance throughput and efficiency. In this work, we examine the technical underpinnings of ZK Rollups and stress test their performance in real-world applications in decentralized finance (DeFi). We set up a proof-of-concept (PoC) consisting of ZK rollup and decentralized exchange, and implement load balancer generating token swaps. Our results show that the rollup can process up to 71 swap transactions per second, compared to 12 general transaction by Ethereum. We further analyze transaction finality trade-offs with related security concerns, and discuss the future directions for integrating ZK Rollups into Ethereum's broader ecosystem.
Edge Intelligence (EI) serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by ensuring transactional transparency, auditability, and system-wide reliability through a decentralized network model. However, the operational architecture of such systems introduces inherent vulnerabilities, particularly due to the extensive data interactions between edge gateways (EGs) and the distributed nature of information storage during service provisioning. To address these challenges, we propose an autonomous computing model along with its interaction topologies tailored for privacy-critical and time-sensitive health applications. The system supports continuous monitoring, real-time alert notifications, disease detection, and robust data processing and aggregation. It also includes a data transaction handler and mechanisms for ensuring privacy at the EGs. Moreover, a resource-efficient one-dimensional convolutional neural network (1D-CNN) is proposed for the multiclass classification of arrhythmia, enabling accurate and real-time analysis of constrained EGs. Furthermore, a secure access scheme is defined to manage both off-chain and on-chain data sharing and storage. To validate the proposed model, comprehensive security, performance, and cost analyses are conducted, demonstrating the efficiency and reliability of the fine-grained access control scheme.
Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual's unique physiology and usage patterns. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalised model training. This study proposes and develops a verifiable and auditable optimised second-order FL framework BFEL (blockchain enhanced federated edge learning) based on optimised FedCurv for personalised healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through fisher information matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each client device while effectively managing personalised training on non-iid and heterogeneous data. The incorporation of ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework's high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.
This study investigates the price dynamics of Bitcoin, a highly volatile and speculative digital asset. Using daily closing price data from January 2023 to January 2024, we apply the Bates model, which combines stochastic volatility with jump-diffusion processes, to better capture both continuous fluctuations and sudden, large price changes in the market. The model parameters are calibrated using historical data and evaluated through Monte Carlo simulation with 10,000 generated price paths over a 31-day forecast horizon. The results demonstrate a strong short-term predictive performance, with a Mean Absolute Percentage Error (MAPE) of 4.32%. This indicates that the Bates model can capture both volatility clustering and abrupt shifts, which are characteristic of Bitcoin. The findings suggest that this approach provides a valuable tool for risk management and investment decision-making in highly uncertain and dynamic markets.
Small and medium size enterprises are becoming critical in driving innovations and economic growth in digital economy. However SME growing reliance on digital technologies exposes to cybersecurity attacks such as data breaches and phishing attacks and ransoms ware attack leads to greater financial loss, reputational challenges and business closure. In order to protect the SME business operation and their process data against cyber security attacks, many researchers applies emerging technologies such as Artificial intelligence and blockchain. Despite of many advantages of the implementing blockchain towards decentralization and transparency while artificial intelligence approaches towards predicting and classifying attacks, it is mandatory to establish an integrated solution to enhance security of the distributed servers of the SME. In this paper, blockchain integrated convolution neural network is designed to predict and classify the user with user level to secure access of data in blockchain enabled distributed servers. Initially Blockchain is established to business process data of the SME with immutable ledger for fostering trust and transparency. Convolution Neural Network establishes access control mechanism to blockchain distributed server to authenticate user against unauthorized access and predict the user level of access to data. In Blockchain, trusted nodes can validate the transaction and request for data access through generation of new transaction by user. User request is logged in blockchain which leads to data transparency and support detect the malicious user to retrieve data in the blockchain. Convolution Neural Network processes the log data of blockchain which contain user request. The user requests were processed in the convolution layer to extract the spatial temporal features. Extracted feature were embedded as spatial embedding and temporal embedding and applied to Max pooling layer. Max pooling layer reduces spatial dimension of the feature map. Spatially reduced feature map is applied to fully connected layer which contains activation function and softmax function to authenticate user and categorize the user with level of access to the data. Experimental analysis of the model is performed in the blockchain platform named as hyperledger which enables convolution neural network for authenticate user and categorize level of user towards data access. Performance analysis of the model proves that model is more secure and accurate against detecting authorized user and classifying user on their level access to data.
ABSTRACT This paper examines the potential and inherent difficulties of initial coin offerings (ICOs), a different approach to startup funding. It investigates investor attitudes, startup motives, and the legal environment around initial coin offerings (ICOs) using a mixed-methods methodology that includes survey responses and literature research. The findings indicate that initial coin offerings (ICOs) provide quick, decentralized funding options and a broader global reach, particularly for firms focused on technology. Nonetheless, worries about investor safety, fraud, and a lack of regulation continue. The study comes to the conclusion that although initial coin offerings (ICOs) have the potential to be revolutionary, their full potential can only be achieved with organized governance, transparency, and investor education. Initial Coin Offerings (ICOs) have become a cutting-edge way for entrepreneurs to raise money, especially in the technology and blockchain industries. This paper looks at ICOs' dual nature, emphasizing both the hazards related to investor protection and regulatory ambiguity as well as its promise as a decentralized fundraising strategy. This study highlights the main opportunities and difficulties faced by investors and entrepreneurs using a mixed-methods approach that includes surveys and a review of the literature. The results show that although initial coin offerings (ICOs) provide quick and worldwide access to cash, trust and openness are still essential to their long-term survival.
In recent years, the cryptocurrency market in Indonesia has grown rapidly. Along with this trend, investor behavior is frequently influenced by cognitive shortcuts known as heuristics. The purpose of this study is to look at how heuristic biases such as representativeness, overconfidence, anchoring, availability bias, and the gambler's fallacy affect retail investment decisions in the Bandung Raya area. A quantitative approach with a survey method was used to collect data from 139 respondents, which were then analyzed using statistical techniques such as correlation tests and simple linear regression. The findings show that heuristic biases have a significant impact on investment decisions, emphasizing the significance of raising investor awareness of psychological factors that may interfere with rational judgment when making cryptocurrency investments.
As blockchain technology drives the global expansion of the digital currency market, the widespread adoption of high-frequency trading and cross-market arbitrage strategies poses dual challenges to traditional regulatory measures in terms of timeliness and accuracy. This study constructs a hybrid neural network model that integrates supervised and unsupervised learning to explore multi-dimensional feature fusion paths between on-chain data from blockchain and secondary market price data. Based on dynamic game theory, an intelligent regulatory sandbox system is designed, incorporating on-chain address reputation scoring mechanisms and liquidity smart contract circuit breakers to achieve real-time warnings and responses to market manipulation behaviors. Furthermore,a distributed regulatory framework built on zero-knowledge proof technology is proposed, providing a feasible solution for establishing a penetrating regulatory system while ensuring transaction privacy.
Rico Aji Pratama, Suripto Suripto, M. Iqbal Harori
This study aims to analyze the influence of Bitcoin Halving on the dynamics of Bitcoin price trends during the period 2012 to 2025. Using a descriptive quantitative approach, data is obtained through documentation of Bitcoin's historical price from various reliable sources. The sample was determined by purposive sample in the period before and after the halving in 2012, 2016, 2020, and 2024. The analysis was conducted using the Moving Average Convergence Divergence (MACD) indicator to observe trend change patterns. The results show that any halving event is generally followed by a significant upward trend in prices, reflecting an imbalance between demand and supply. Additionally, the MACD has proven to be effective in identifying trend change signals related to halvings. External factors such as regulation and market sentiment also strengthen its impact. This research makes a theoretical contribution to the digital financial literature and practical recommendations for crypto investors.
David Hardiago, Rani Fadhila Syafrinaldi, Syafrinaldi, M. Musa · 5 authors
The advancement of science and technology continues to accelerate. Internet and its supporting technology became the backbone for many activities. This kind of advancement goes beyond territorial boundaries and human’s intelligence. This phenomenon triggers a new crime model. One of them is a crime related to the existence of Cryptocurrency such as money laundering, terrorism financing, and other forms of crime. This is understood given that Cryptocurrency give rise to large number of crimes as well as the emergence of a new modus operandi in several criminal acts. This is further supported by the lack of current Indonesian regulations that do not specifically accommodate the Cryptocurrency mechanism. The main issues raised in this study are related to how crimes and new modus operandi are caused by the Cryptocurrency mechanism against Indonesian criminal law, as well as how the mechanism of crime prevention and handling caused by the Cryptocurrency mechanism through the means of penal policy, covering targeted legislative amendments, specialised investigative units, prosecutorial guidelines, and asset-seizure procedures—to prevent and combat such crimes. This article uses normative method to answer the problem through comparative approach, case approach, literary approach and statutory approach. Initial hypotheses proposed to address these problems are: First, Cryptocurrency as a financial transaction mechanism that relies on the computational system with anonymous transactions opens a great opportunity for crime in the mechanism of the transaction. Second, the establishment of new rules in order prevent and combat crime and the modus operandi caused by the Cryptocurrency mechanism through the mechanism of penal policy are essential.
Abstract: Ensuring free and fair elections is the foundation of democratic nations, but conventional voting systems are still susceptible to manipulation, fraud, and inefficiencies. With the advancement of digital infrastructure, electronic voting (evoting) has become a reality, but usually at the expense of transparency and security because of centralized control. Blockchain technology, and specifically Ethereum with its smart contract feature, provides a chance to transform voting systems through decentralization, immutability, and end-to-end verifiability. This suggests a next-generation e-voting system on the Ethereum blockchain with secure voter authentication, transparent vote casting, and smart contract-based automated result counting. Experimental results confirm the system's fraud resistance, scalability for medium-sized elections, and capability to present realtime, tamper-proof election results.
Hong Qu, Krzysztof Gogol, Florian Grötschla, Claudio J. Tessone
Decentralized Finance (DeFi) lending enables permissionless borrowing via smart contracts. However, it faces challenges in optimizing interest rates, mitigating bad debt, and improving capital efficiency. Rule-based interest-rate models struggle to adapt to dynamic market conditions, leading to inefficiencies. This work applies Offline Reinforcement Learning (RL) to optimize interest rate adjustments in DeFi lending protocols. Using historical data from Aave protocol, we evaluate three RL approaches: Conservative Q-Learning (CQL), Behavior Cloning (BC), and TD3 with Behavior Cloning (TD3-BC). TD3-BC demonstrates superior performance in balancing utilization, capital stability, and risk, outperforming existing models. It adapts effectively to historical stress events like the May 2021 crash and the March 2023 USDC depeg, showcasing potential for automated, real-time governance.
The article explores the transitive model of digital statehood as a conceptual response to systemic challenges arising from the dynamic digital transformation of public governance. The author argues that traditional models of state administration no longer provide adequate responses to the rapid evolution of digital tools, infrastructures, and agents that operate within new decision-making spaces — from distributed ledgers to autonomous cognitive systems. Therefore, the concept of transitivity is proposed as a theoretical framework to describe the transition from classical hierarchical forms of statehood to complex, open, and dynamic digital architectures.The methodological foundation of the research combines three complementary approaches: the system-synergetic, institutional-cognitive, and network-platform paradigms. The system-synergetic approach makes it possible to view public governance as a complex open system capable of self-organization and the emergence of new structures in response to external digital impulses. The institutional-cognitive paradigm captures the changing logic of institutional functioning in the context of decentralized knowledge and functions, while the network-platform approach focuses on understanding the state as a digital platform where decisions are formed through the interaction of multiple agents — both human and algorithmic.Within the proposed transitive model, a three-level architecture of digital statehood is introduced: (1) the institutional core, which undergoes cognitive reconfiguration under the influence of digital agents; (2) the infrastructural level, based on the integration of AI and blockchain technologies into functional management mechanisms; (3) the level of external agents — platforms, networks, and users — that actively participate in shaping public decisions. Particular attention is paid to the analysis of smart contracts as a new institutional unit that enables automated, conditional, and irreversible execution of public governance decisions.It is demonstrated that the integration of blockchain and artificial intelligence alters the principles of trust formation, legitimacy, and accountability in digital interaction. At the same time, digital logic becomes the driver of institutional evolution — from fixed models to flexible, adaptive mechanisms functioning in a mode of permanent recalibration. The article also reveals the phenomenon of «post-institutional reflexivity,» in which the role of the state is reimagined — not as a monopolist of authority but as a cognitive integrator of data, platforms, and meanings.The practical value of the study lies in the proposed theoretical construct, which may be applied to the design of digital policy frameworks, the transformation of governance processes in the public sector, and the assessment of risks associated with technological dynamics. The article provides a foundation for further research into the formalization of trust mechanisms, automated decision legitimation, and the synthesis of governance strategies in complex digital environments.
Актуальность исследования обусловлена стремительным развитием криптовалютного рынка и растущим интересом инвесторов к альтернативным способам получения дохода. Одним из таких инструментов становится стейкинг криптовалют, позволяющий получать вознаграждение за участие в поддержании блокчейн-сетей. Проблематика развития данного инструмента заключается в недостаточной осведомленности инвесторов о механизмах стейкинга, его разновидностях, рисках и потенциальной доходности, особенно на фоне высокой волатильности крипторынка. Цель статьи – проанализировать стейкинг как форму альтернативного инвестирования, определить его преимущества и уязвимости, а также оценить перспективность для различных категорий инвесторов. В рамках исследования авторами применены методы сравнительного анализа, теоретического обзора источников, а также произведена оценка рисков и доходности на примерах конкретных криптовалют и платформ. В статье рассматриваются основные виды стейкинга: фиксированный, гибкий, ликвидный и делегированный. Приведены примеры популярных криптовалют, таких как Tezos, Cardano, Algorand, Polkadot и Ethereum, использующих алгоритм консенсуса Proof-of-Stake (PoS - доказательство доли владения), и сопоставлены их потенциальные доходности. Результаты исследования показывают, что при грамотном подходе стейкинг может быть эффективным инструментом создания пассивного дохода. Выявлены ключевые риски: волатильность, ограниченная ликвидность, угроза потери активов и технические сложности. Научным результатом являются предложенные стратегии минимизации рисков, включающие диверсификацию, выбор ликвидных активов и использование проверенных платформ. Таким образом, стейкинг представляет собой перспективный и гибкий инвестиционный механизм в рамках цифровой экономики. The relevance of the study is due to the rapid development of the cryptocurrency market and the growing interest of investors in alternative ways of generating income. One of these tools is cryptocurrency staking, which allows you to receive rewards for participating in the maintenance of blockchain networks. The problem with the development of this tool lies in the lack of investor awareness about staking mechanisms, its varieties, risks and potential returns, especially against the background of high volatility of the crypto market. The purpose of the article is to analyze staking as a form of alternative investment, identify its advantages and vulnerabilities, and assess its prospects for various categories of investors. As part of the study, the authors applied methods of comparative analysis, a theoretical review of sources, and an assessment of risks and profitability based on examples of specific cryptocurrencies and platforms. The article discusses the main types of staking: fixed, flexible, liquid and delegated. Examples of popular cryptocurrencies such as Tezos, Cardano, Algorand, Polkadot, and Ethereum using the Proof-of-Stake (PoS proof of ownership) consensus algorithm are given, and their potential returns are compared. The results of the study show that, with the right approach, staking can be an effective tool for creating passive income. Key risks have been identified: volatility, limited liquidity, the threat of asset loss and technical difficulties. The scientific result is the proposed risk minimization strategies, including diversification, the choice of liquid assets and the use of proven platforms. Thus, staking is a promising and flexible investment mechanism within the digital economy.
Abstract: The real estate sector faces persistent challenges,includingintermediary dependence, information asymmetry, fraud vulnerability, and limited liquidity,despite technological advancements in other industries. This paper presents the design, implementation, and evaluation of a blockchain-based solution addressing these challenges. Your platform, developed using Ethereum smart contracts, comprises a distributed property ledger, automated transaction processing, and property tokenization capabilities. Performance evaluation demonstrates substantial improvements over traditional methods: transaction times decreased by 90%, costs reduced by 80-90%, and security enhanced through immutable record-keeping. Property transfers that traditionally require weeks were executed in minutes, with smart contracts automating escrow management and document verification. The empirical results provide concrete evidence of blockchain's efficacy in real estate transactions and establish a framework adaptable across different property markets and regulatory environments
Ruixue Jing, Ryota Kobayashi, Luis Enrique Correa Rocha
The rapidly evolving cryptocurrency market presents unique challenges for investment due to its inherent volatility and evolving regulatory environment. Collective price movements can be exploited to construct diversified portfolios with improved risk-return profiles. This paper introduces an integrated framework that combines network analysis, price forecasting, and portfolio theory to identify stable groups of highly correlated cryptocurrencies for profitable portfolio construction. We employ the Louvain community detection algorithm together with consensus clustering to extract temporally persistent correlation clusters, and incorporate ARIMA-based price forecasts to enhance forward-looking cluster formation. Using 5 years of daily closing prices, we evaluate portfolio performance across multiple strategies and holding horizons, assessing both profitability and downside risk with return-based and tail-risk metrics. Our empirical results show that predictive consensus-clustering portfolios maintain consistently positive and stable performance up to a 14-day horizon, exhibit favourable gain-loss asymmetry, and achieve tighter tail-risk control. These findings demonstrate that stable interdependencies in cryptocurrency markets can be leveraged to construct profitable and risk-aware portfolios across short-term holding horizons.
Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj, Ian Foster · 5 authors
Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, multi-institutional settings. This paper introduces a modular, domain-agnostic architecture for provenance tracking in federated environments, leveraging permissioned blockchain infrastructure to guarantee integrity, immutability, and auditability. The system supports decentralized interaction, persistent identifiers for artifact traceability, and a provenance versioning model that preserves the history of updates. Designed to interoperate with diverse scientific domains, the architecture promotes transparency, accountability, and reproducibility across organizational boundaries. Ongoing work focuses on validating the system through a distributed prototype and exploring its performance in collaborative settings.
Blockchains support a rapidly growing digital economy. Through decentralization, they enable the ownership and transfer of digital assets without centralized intermediaries while resisting attacks, faults, and collusion. Yet decentralization is not guaranteed and must be sustained against external shocks. What design choices help blockchains maintain decentralization when disrupted? We find that resource flexibility, how easily consensus resources can be moved and redeployed, is key to sustained decentralization. Using three real-world shocks—China’s 2021 crypto mining ban, Hetzner’s 2022 shutdown of Solana validators, and Ethereum’s transition from proof-of-work to proof-of-stake—we show that blockchains whose consensus resources are more flexible recover decentralization faster. These findings offer actionable guidance. Blockchain designers should consider resource flexibility as a first-order design parameter. Operators should diversify infrastructure across jurisdictions to reduce vulnerability to localized shocks. Policymakers should be aware that regulations targeting blockchains can have unintended effects on decentralization.
Blockchain protocols incentivize participation through monetary rewards, assuming rational actors behave honestly to maximize their gains. However, attackers may attempt to harm others even at personal cost. These denial of profit attacks aim to reduce the rewards of honest participants, potentially forcing them out of the system. While existing work has largely focused on the profitability of attacks, they often neglect the potential harm inflicted on the victim, which can be significant even when the attacker gains little or nothing. This paper introduces a framework to quantify denial of profit attacks by measuring both attacker cost and victim loss. We model these attacks as a game and introduce relevant metrics to quantify these attacks. We then focus on committee-based blockchains and model vote collection as a game. We show that in the vote collection game, disincentivizing one denial of profit attack will make another attack more appealing, and therefore, attacks have to be balanced. We apply our framework to analyze real-world reward mechanisms in Ethereum and Cosmos. Our framework reveals imbalances in Cosmos that can make correct behavior suboptimal in practice. While Ethereum provides stronger protections, our framework shows that it is also not complete, and we propose alternative parameter settings to improve the balance between attacks. Our findings highlight the need for better-balanced reward designs to defend against denial of profit attacks.
Optimistic Rollups (ORUs) significantly enhance blockchain scalability but inherently suffer from the verifier's dilemma, particularly concerning validator attentiveness. Current systems lack mechanisms to proactively ensure validators are diligently monitoring L2 state transitions, creating a vulnerability where fraudulent states could be finalized. This paper introduces the Randomized Attention Test (RAT), a novel L1-based protocol designed to probabilistically challenge validators in ORUs, thereby verifying their liveness and computational readiness. Our game-theoretic analysis demonstrates that an Ideal Security Equilibrium, where all validators are attentive and proposers are honest, can be achieved with RAT. Notably, this equilibrium is attainable and stable with relatively low economic penalties (under \$1000) for non-responsive validators, a low attention test frequency (under 1\% per epoch), and a minimal operation overhead (monthly under \$30) with 10 validators. RAT thus provides a pivotal, practical mechanism to enforce validator diligence, fortifying the overall security and integrity of ORU systems with minimizing additional costs.
Automated Market Makers (AMMs) are decentralized exchange protocols that provide continuous access to token liquidity without the need for order books or traditional market makers. However, this innovation has failed to scale when it comes to cross-chain swaps. Modern cross-chain swaps employ double-sided AMMs, which are not only inefficient due to liquidity fragmentation but also require an intermediate token. This introduces inherent volatility risk as well as blockchain and bridging risk, especially in the case of wrapped tokens. This paper describes the inefficiencies of existing AMM invariants, particularly their mixed polynomial nature, and derives a new class of AMMs that do not have bi-state dependency between the assets being swapped. We propose a novel method of value transfer swaps using the described invariant that mitigates the need for bi-state dependency and eliminates the need for intermediate tokens or bridging. Furthermore, we show how this mechanism enables efficient cross-chain swaps with lower gas requirements and no bridging risks. The proposed technology is designed to support cross-chain swaps across any permutation of L1, L2, and L3 blockchains.