Predictive analysis has become an essential component in modern financial research and practice, driven by the rapid advancement of data analytics, machine learning, and artificial intelligence. This study aims to systematically map the intellectual structure, research trends, and key contributions in the field of predictive analysis in finance through a bibliometric approach. Data were collected from the Scopus database covering publications from 2000 to 2026 and analyzed using VOSviewer to examine co-authorship networks, citation patterns, and keyword co-occurrence. The results reveal a significant growth in research output, particularly in recent years, reflecting the increasing importance of data-driven decision-making in finance. Co-authorship analysis indicates the presence of collaborative research clusters, although the field remains partially fragmented. Citation analysis highlights that the most influential studies are those integrating advanced computational methods with practical financial applications, such as credit scoring, bankruptcy prediction, and stock market forecasting. Furthermore, keyword analysis demonstrates a clear shift from traditional statistical techniques toward machine learning, artificial intelligence, and emerging technologies such as blockchain and decentralized finance. This study contributes by providing a comprehensive overview of the evolution and current state of predictive analysis in finance, identifying key research themes and gaps. The findings suggest that future research should focus on enhancing model interpretability, integrating sustainability considerations, and expanding applications in real-time financial decision-making. Overall, this study serves as a valuable reference for researchers and practitioners seeking to understand the trajectory and future direction of predictive analytics in the financial domain.
José Manuel Torres, Luis P. Mota, Rui S. Moreira, Christophe Soares · 5 authors
Ambient Assisted Living (AAL) systems have become increasingly relevant as aging populations intensify the demand for technologies that promote autonomy, safety, and quality of life. However, the widespread adoption of audiovisual sensing in smart homes raises critical concerns regarding data protection, privacy, and user trust. Ensuring secure processing while maintaining accurate activity recognition remains a key challenge. This work introduces DistSense, a distributed Peer-to-Peer (P2P) system designed to enhance activity detection in domestic environments through collaborative inference among intelligent audiovisual sensors. DistSense prioritizes privacy by performing local processing, sharing only high-level events, and leveraging distributed ledger mechanisms to ensure data integrity and auditability and support cross-device validation. This collaborative strategy reduces false positives caused by occlusions, illumination variability, and acoustic noise. To assess the system, functional tests were conducted for each module, followed by two use cases evaluated in both simulated and real edge hardware environments. The trained models achieved 88% accuracy for audio and 80% for video, and the system demonstrated effective performance in detecting daily activities and domestic hazards under varying noise conditions. Results indicate that DistSense successfully balances security, user acceptance, and inference robustness, positioning it as a viable solution for privacy-preserving activity monitoring in smart home contexts.
Auctions play a vital role in modern commerce by offering a transparent, structured, and competitive method for trading goods, services, and data. However, traditional in-person auctions are limited in terms of accessibility, convenience, security, and efficiency. However existing online auction platforms, while addressing some of these limitations, still face challenges such as limited transparency, centralized control, and insufficient security and privacy protections. Moreover these issues become extremely critical in case of sensitive applications like healthcare, defense and finance. To address these challenges, this article proposes a three-phase trading framework. In the first phase, data generation, anonymization, and storage are performed. In the second phase, an ensemble learning-based price forecasting approach is employed to estimate the asking and bidding prices, which depend on the volume and type of data. Finally, in the third phase, a Monte Carlo-inspired auction-based Non-Fungible Token (NFT) trading mechanism (MCiANT) is incorporated to enable efficient trading between buyers and sellers. The efficacy of the proposed MCiANT framework is compared with three distinct auction algorithms: the Vickrey auction, the Markov-Inspired Stationary Distribution Auction (MISD), and the Two-Phase English–Dutch Hybrid Auction (TPEDHA). The results demonstrate that the MCiANT framework significantly outperforms the others, achieving success-rate improvements of 5%, 5%, and 1% over the Vickrey, MISD, and TPEDHA auctions, respectively. Furthermore, the proposed framework is evaluated using health data by measuring anonymization time, encryption time, InterPlanetary File System (IPFS) upload time, and I/O performance.
The convergence of artificial intelligence, blockchain, and non-fungible tokens (NFTs) has triggered a doctrinal crisis in copyright, contract, and evidence law across several major jurisdictions, including the United States, the European Union, and selected Asian legal systems. By 2025, over 70% of top NFT sales feature hybrid human–AI creations, yet most remain in legal and economic “gray zones” across jurisdictions. This article examines the challenge of partial AI authorship through a comparative analysis of U.S., EU, and Asian legal frameworks, revealing enduring gaps in originality doctrine, inconsistencies in the treatment of blockchain-based evidence, and contested approaches to smart contract enforceability and royalty mechanisms. Particular attention is given to the technical processes through which AI systems source, transform, and recombine data from public and private domains, raising unresolved questions of infringement, attribution, and authorship when copyrighted works are used without authorization. Drawing on originality doctrine, transformative use standards, and fair use principles, the analysis argues that legal protection should be confined to AI-assisted outputs that reflect meaningful human creative judgment and demonstrable transformation, rather than automated reproduction. Situating these doctrinal tensions within broader patterns of market volatility, regulatory arbitrage, and unequal access to justice. It concludes that adaptive, pluralist governance is essential to achieving legally coherent and socially sustainable outcomes in the digital creative economy.
Abstract India’s handloom sector, the country’s second-largest rural employer after agriculture, faces existential threats from counterfeiting, power-loom imitations, fragmented supply chains, and declining artisan incomes. This study explores how blockchain-enabled traceability and Non-Fungible Token (NFT) integration can authenticate handloom products, protect intellectual property, ensure fair remuneration to artisans, and open premium global markets. Drawing on secondary data from government reports (2024–2026), academic literature, and emerging case studies, the paper finds that blockchain-based Digital Product Passports (DPPs) combined with NFTs can create tamper-proof provenance records while enabling royalty mechanisms for creators. Despite infrastructural and digital-literacy barriers, pilot initiatives demonstrate potential for 20–40% income uplift and reduced counterfeit penetration. The research highlights policy and technological pathways to integrate these tools with existing schemes such as the Handloom Mark and Geographical Indication (GI) tags. Ultimately, blockchain and NFTs offer a viable digital bridge between traditional craftsmanship and modern consumer demand for authenticity and sustainability.
Запропоновано середовище імітаційного моделювання явища максимально екстрактованої вигоди MEV (англ. Maximal Extractable Value), реалізоване мовою програмування Python із використанням бібліотеки Gymnasium, яке відтворює взаємодію сховища-мемпулу, конструювальника блоків, агента MEV-екстрактора та AMM-пулу децентралізованої біржі. Формально середовище описано як розширений та частково спостережуваний процес прийняття рішень, у межах якого агент взаємодіє з дискретно-часовою моделлю епізодів, що відображає послідовність надходження транзакцій, побудови блоків і виконання swap-операцій обміну на децентралізованій крипто-біржі. Для моделювання адаптивної поведінки агента використано методи навчання з підкріпленням, а для кількісного аналізу втрат користувачів застосовано контрфактичний підхід до оцінювання, що дає змогу порівнювати результати виконання транзакцій у різних режимах впорядкування за однакових вхідних умов. У дослідженні використано раніше описаний авторами метод зменшення негативних ефектів MEV-екстракції на основі логічних часових міток Лампорта, який реалізує локальне причинно-наслідкове впорядкування транзакцій у межах окремого смарт-контракту без модифікації глобального механізму консенсусу мережі блокчейн Ethereum. Для оцінювання практичної ефективності цього підходу сформовано три сценарії моделювання: базовий сценарій без систематичної MEV-атаки для визначення накладних витрат застосування механізму захисту, сценарій систематичної sandwich-атаки для аналізу та здатності методу зменшувати втрати користувачів та обмежувати можливості MEV-екстрактора, а також сценарій параметричного аналізу, спрямований на дослідження компромісу між рівнем захисту та "вартістю" його застосування. Отримані результати показали, що запропонований метод MEV-захищеного впорядкування може зменшувати цінові втрати користувачів від sandwich-атак і, водночас, впливати на частоту відхилення транзакцій та пов'язані комісійні витрати, що вказує на наявність керованого компромісу між ефективністю захисту та накладними витратами його використання. Практична цінність роботи полягає у створенні відтворюваного середовища імітаційного моделювання для дослідження стратегічної поведінки MEV-агентів і перевірки механізмів зменшення негативних наслідків MEV у контрольованих умовах, що може бути використано для подальшого аналізу безпеки протоколів децентралізованих фінансів та проєктування нових методів впорядкування транзакцій.
A decentralized system for academic credential verification using Ethereum blockchain and hybrid off-chain storage. The system replaces traditional manual verification by allowing institutions to issue digitally signed certificates whose cryptographic hashes are stored on-chain, ensuring immutability and tamper resistance. A dual-hashing approach (SHA-256 followed by Keccak-256) is used to enhance security and maintain compatibility with the Ethereum ecosystem. Credential files are stored off-chain (e.g., Supabase/IPFS) to reduce cost, while verification is performed by comparing hashes, achieving fast (under a few seconds) and reliable authentication. Overall, the paper demonstrates a scalable, secure, and efficient solution for real-world use cases such as academic admissions and recruitment.
This study examines the transformation of property rights amid rapid digital innovation, focusing on how legal systems are adapting to address the inheritance of virtual assets alongside traditional physical property. The rise of digital assets, including cryptocurrencies, non-fungible tokens (NFTs), digital accounts, and online intellectual property, has created significant gaps in existing inheritance laws. Using doctrinal and comparative legal analysis, the study reviews national and international frameworks to identify inconsistencies, accountability deficits, and equity concerns. The findings reveal that most jurisdictions lack specific legislation governing digital inheritance, creating systemic disadvantages for heirs. The study concludes by recommending harmonized legal standards, mandatory digital estate-planning mechanisms, and proactive regulatory reforms to ensure equal inheritance rights regardless of asset type.
The rapid advancement of Large Language Models (LLMs) has established autonomous agents as the core vehicles for artificial intelligence applications. However, existing Internet infrastructures, primarily relying on TCP/IP and DNS, are designed for human-centric, host-to-host data transmission, inherently lacking the semantic awareness, dynamic capability discovery, and decentralized trust mechanisms required for autonomous agent interactions. To address these limitations and break the closed ecosystems of single vendors, this paper proposes AONA (Agentic Overlay Network Architecture), a novel overlay network architecture for the Internet of Agents (IoA). We first provide a multi-disciplinary scientific defense for multi-agent collaboration, demonstrating its theoretical necessity over single super-intelligence through the lenses of organizational economics, scaling principles, and the Price of Anarchy. AONA is then structured as a four-layer logical blueprint comprising the Base, Interconnection, Collaboration, and Application layers, which facilitates cross-protocol and cross-platform interoperability without disrupting the underlying physical network. To physically instantiate this blueprint, we design a distributed node infrastructure anchored by Management Root Nodes, Registry Service Nodes, Discovery Service Nodes, and Enterprise Intelligent Service Hubs for private domain integration. Finally, we detail the dynamic operational workflows-including zero-trust identity issuance, globally coordinated semantic taxonomy synchronization, intent-driven semantic discovery, and trusted metering for commercial settlement-that drive the network. This comprehensive architecture provides a robust, scalable, and secure foundation for the future of global agentic collaboration.
Rongji Huang, Yifeng Ye, Gerui Wang, Mingchao Wan · 8 authors
Due to regulatory compliance and governance management, modern (permissioned) blockchains require flexible endorsement, which allows the endorsement policy for each contract or state object to be individually defined. To enable flexible endorsement, Hyperledger Fabric employs an execute-order-validate (EOV) paradigm, in which transactions first undergo speculative execution and endorsement, and are only then ordered and validated. Meanwhile, most blockchain systems, including the platform targeted in this work (i.e., ChainMaker), still follow a conflict-free order-execute framework. We argue that the EOV paradigm still faces several limitations, notably high abort rates in high-contention workloads such as those in Decentralized Finance (DeFi). To avoid refactoring our system and better suit DeFi applications, we try to integrate flexible endorsement into the classical order-execute architecture and accordingly propose a new framework. The key challenge is to deterministically remove problematic transactions from an ordered list, while preserving censorship resistance and decentralization for the remaining ones. We instantiate this framework on top of Tendermint, a seminal Byzantine fault-tolerant (BFT) protocol adopted in our system, and thereby propose FlexTender. By elegantly embedding endorsements into consensus, FlexTender incurs no additional messaging overhead in the normal case. Empirical evaluation using an Ethereum USDT workload demonstrates that FlexTender achieves up to $10.6\times$ speedup in throughput over an EOV simulation on the same platform.
This paper introduces a systems-theoretic framework for understanding how high-coherence social structures emerge following the collapse of centralized ideological movements. Using the fragmentation of the Black Panther Party and the subsequent rise of Chicago-based organizations such as the Gangster Disciples and Black P. Stone Nation as a primary case study, the research analyzes how identity systems function as distributed governance mechanisms in high-threat environments. The paper argues that after a loss of centralized coordination capacity—accelerated in this case by external intervention such as COINTELPRO and internal organizational fragmentation—localized groups reconstruct coherence through symbolic and procedural frameworks. These identity systems, composed of geometric symbols, ritualized protocols, and codified behavioral rules (“The Literature”), act as low-cost identity verification mechanisms and enable rule-based behavioral alignment across decentralized networks. By applying a comparative systems lens, the study draws a functional analogy between these 20th-century urban dynamics and the historical nationalization of Israelite tribal identity in the ancient Near East (cf. William G. Dever). In both contexts, fragmented groups achieve large-scale coordination through the implementation of standardized identity codes, compliance signaling mechanisms (e.g., taxation/tithing), and boundary-maintaining symbolic systems. These mechanisms allow for structural persistence even in the absence of continuous centralized enforcement. The paper further develops the concept of distributed command, in which authority is embedded within the identity system itself rather than dependent on the physical presence of leadership figures such as Larry Hoover and Jeff Fort. This shift enables what is defined as Projected Threat Recursion, where adherence to system rules is maintained through internalized identity and anticipated enforcement rather than immediate coercion. To support operationalization, the study proposes a preliminary Coherence Coefficient (Cₛ) model, conceptualizing system stability as a function of alignment, response consistency, and identity strength relative to fragmentation and external distortion pressures. This model provides a foundation for analyzing resilience and failure dynamics in distributed human systems. The findings suggest that identity-mediated coordination is a persistent and scalable solution to systemic fragmentation, extending beyond historical or urban contexts. Comparable architectures are observable in decentralized autonomous organizations (DAOs), digital identity networks, insurgent systems, and platform-based communities. As modern systems experience increasing fragmentation, identity-based governance structures may represent a primary adaptive pathway for achieving long-term coordination and structural persistence. This work contributes to interdisciplinary discussions in systems theory, sociology, political science, and complexity studies by reframing identity not as a cultural byproduct, but as a functional infrastructure for distributed governance under constraint.
Type of the article: Research ArticleAbstractThe freelance economy opens new ways for direct interaction between freelancers and customers without intermediaries. This study aims to systematize the forms of the freelance economy in the context of Industry 5.0. A structured review methodology focusing on technological progress and human-centric solutions of the freelance economy is used. Freelancing and Industry 5.0 are closely intertwined and complement each other, forming new economic models and work processes. Their relationship lies in the combination of technological development and human creativity, which allows for the formation of efficient and flexible economic structures. Personalization and customization of consumption within Industry 5.0 promote the freelancing (individualization) of the production sphere, building a win-win strategy both for consumers and producers. Freelancing economy focuses on information processing of work, enables remote communications, promotes creativity of work, provides opportunities for the synergistic combination of human cognitive abilities with AI, ensures the development of personalization and customization of consumption, and contributes to the social development of workers. The structure of the forms of the freelance economy is characterized by the integration of decentralized financial systems, the use of artificial intelligence and blockchain, and the transition to new forms of labor organization based on global digital platforms and self-regulated organizations. One of the key barriers to the freelance economy is the lack of legal regulation of cryptocurrencies and decentralized autonomous organizations (DAOs), as well as the associated cybersecurity risks. To summarize, the significance lies in creating a more adaptive, flexible, and decentralized labor market that meets the challenges of today’s digital world.AcknowledgmentsThis research was funded by a grant “Fundamental grounds for Ukraine’s transition to a digital economy based on the implementation of Industries 3.0; 4.0; 5.0” (No. 0124U000576) and “Digital transformations to ensure civil protection and post-war economic recovery in the face of environmental and social challenges” (No. 0124U000549). 
Bu çalışma, 2020–2025 yılları arasında DAO (Decentralized Autonomous Organizations) yapılarıyla ilgili literatürü incelemek amacıyla SCOPUS veri tabanından elde edilen 3.113 akademik çalışma üzerinde bibliyometrik analiz gerçekleştirmiştir. “decentralized autonomous organization”, “DAO”, “smart contract”, “on-chain governance” gibi anahtar kelimelerle yapılan tarama sonucunda, literatürün blockchain ve akıllı sözleşmeler temelli teknik altyapı etrafında yoğunlaştığı; buna karşılık yönetişim modelleri, token ekonomisi, oylama süreçleri, güvenlik, veri gizliliği ve hukuki statü gibi konuların araştırmalarda öne çıktığı belirlenmiştir. Bulgular, DAO çalışmalarının çok disiplinli bir yapıya sahip olduğunu, ülke ve kurum bazlı yayın yoğunluklarının küresel olarak hızla arttığını ve kavramsal çeşitliliğin yüksek seviyede olduğunu göstermektedir. Analizler, DAO’ların yönetişim ve denetim açısından standartlaşmamış, teknik olarak karmaşık ve hukuken belirsiz bir yapı sergilediğini; bu nedenle geleneksel finansal denetim modelleriyle uyum sorunlarının bulunduğunu ortaya koymaktadır. Sonuç olarak, DAO ekosisteminin sürdürülebilir ve denetlenebilir bir yapıya kavuşması için yönetişim protokollerinin netleşmesi, teknik güvenlik standartlarının geliştirilmesi ve hukuki çerçevelerin güçlendirilmesi gerekmektedir.
Ігор Романович Соломка, Богдан Богданович Любінський
This study investigates the process of validator committee selection in permissionless blockchain networks operating on the Proof-of-Stake algorithm. The task addressed relates to the vulnerability of conventional static selection schemes to identity-forging (Sybil) attacks. A fixed baseline weight facilitates stake splitting among numerous fictitious entities, allowing attackers to gain control over the network. In response to these challenges, a method for the dynamic stabilization of consensus based on an adaptive control law has been devised. This method automatically regulates the weight mixing intensity using the smoothed Gini coefficient. The concept of Proof-of-Persistence has been proposed, which replaces the uniform baseline distribution with a time-weighted reputation of the participants. The analytical and experimental analyses of data from 10 real-world networks were conducted, demonstrating that the proposed mechanism reliably reduces the aggregate weight of a potential attacker. The result is attributed to the fact that when new entities are created, their prior participation experience is not considered, and the loss of reputational weight outweighs the benefits of acquiring new baseline shares. This makes the stake-splitting strategy economically unviable. An important distinct feature is that the system's adaptation is carried out exclusively on the basis of deterministic on-chain data, without the need for external identification. The proposed system functions autonomously: under a normal mode, intervention is minimized, while under the risk of an oligopoly, protection is strengthened. The results could be practically applied to the architecture of permissionless blockchain networks as the method might be integrated both at the network protocol core level and in the form of smart contracts to enhance the security of distributed ledgers without additional manual adjustments.
This study investigates the process of validator committee selection in permissionless blockchain networks operating on the Proof-of-Stake algorithm. The task addressed relates to the vulnerability of conventional static selection schemes to identity-forging (Sybil) attacks. A fixed baseline weight facilitates stake splitting among numerous fictitious entities, allowing attackers to gain control over the network. In response to these challenges, a method for the dynamic stabilization of consensus based on an adaptive control law has been devised. This method automatically regulates the weight mixing intensity using the smoothed Gini coefficient. The concept of Proof-of-Persistence has been proposed, which replaces the uniform baseline distribution with a time-weighted reputation of the participants. The analytical and experimental analyses of data from 10 real-world networks were conducted, demonstrating that the proposed mechanism reliably reduces the aggregate weight of a potential attacker. The result is attributed to the fact that when new entities are created, their prior participation experience is not considered, and the loss of reputational weight outweighs the benefits of acquiring new baseline shares. This makes the stake-splitting strategy economically unviable. An important distinct feature is that the system's adaptation is carried out exclusively on the basis of deterministic on-chain data, without the need for external identification. The proposed system functions autonomously: under a normal mode, intervention is minimized, while under the risk of an oligopoly, protection is strengthened. The results could be practically applied to the architecture of permissionless blockchain networks as the method might be integrated both at the network protocol core level and in the form of smart contracts to enhance the security of distributed ledgers without additional manual adjustments.
Maksym W. Sitnicki, Олена Шатілова, Nikita Smohorzhevskyi
The growth of the knowledge economy requires new models enabling consulting firms to convert expertise into venture capital capabilities within Web 3.0 ecosystems. Existing research rarely explains how knowledge-based consultancies transform into institutional investors with scalable investment strategies and measurable performance. This study aims to develop an original theoretical and applied framework explaining the transition of a Web 3.0 consulting company into a venture capital institution through quantitative forecasting, governance mechanisms, and diversified investment design. The proposed concept integrates organizational maturity assessment, financial modeling, investment governance, and scenario analysis into a unified venture transition framework for knowledge-economy firms. The core research question addresses how a knowledge-economy consulting company can operationalize its transition into venture capital management within the Web 3.0 ecosystem. Using PEMM analysis, gap analysis, Gantt charts, RACI matrices, market sizing (TAM/SAM/SOM), financial forecasting, and scenario modeling, this paper proposes a phased framework for venture fund structuring, investment strategy formulation, and 5-year performance projections—directly applied to Solus Agency’s context to demonstrate practical pathways for capturing value in this high-growth, high-risk domain. The empirical basis combines venture datasets, company-level indicators, and proprietary Solus Agency statistics, including 180+ venture funds, 160+ private investors, 46 fundraising projects, and USD 13.8 million attracted for clients. Quantitative modeling shows that a diversified USD 50 million fund may generate projected profits of USD 120 million under a negative scenario, USD 200 million in the baseline scenario, and USD 290 million in an optimistic scenario, corresponding to expected multipliers between 2.4× and 5.8×. Portfolio valuation is forecast to increase from USD 20.6 billion to USD 54.6 billion, demonstrating substantial sensitivity to allocation strategy and market conditions. The proposed Solus Agency subfund achieves an expected total return of USD 36.38 million, a gross multiplier of 3.64, a net multiplier of 3.11, a gross IRR of 52.05%, and a LP net IRR of 43.60%, indicating high projected efficiency despite elevated early-stage risks. Probability modeling identifies seed-stage allocations as the strongest contributor (USD 13.06 million projected profit) and demonstrates that diversification across AI, Web3, DeFi, and RWA segments reduces volatility while preserving growth potential. The scientific novelty lies in constructing an original framework quantitatively linking organizational maturity, consulting expertise, and venture performance indicators. The findings provide a transferable model for knowledge-economy firms seeking institutionalization as venture capital actors and support further research on quantitative venture strategies and Web 3.0 investment ecosystems.
Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) allow for elegant, privacy-preserving validation of computations. PLONK, a subclass of the zk-SNARKs, is certainly useful, but its complex interactions with permutation arguments, lookup tables, and blinding, among other considerations, make the protocol difficult to follow, let alone understand. This paper describes a framework centered around the core components of zk-SNARKs. In particular, we detail the construction of arithmetic gate constraints, representation of witness polynomials, and the Kate-Zaverucha-Goldberg (KZG) commitment scheme. By removing permutation proofs, lookup, and blinding, we aim to simplify the pedagogy of zk-SNARKs and preserve their essential properties of soundness and completeness. We describe a Python module from the ground up that demonstrates the generation and validation of proofs in a PLONK-modified zk-SNARK. We validate the framework and its foundations with a benchmark of a module generating and validating proofs in a PLONK-modified zk-SNARK. We validate the module against a circuit of 1,000 gates and demonstrate that the system correctly rejects all invalid witnesses. We illustrate the expected asymptotic behavior, with a pro tor of tight the module is quasi-linear, and verification, tight. We justify the foundations of the module and describe tight with zero private inputs. We have also bridged the gap between abstract zk-SNARK theoretical arguments and their practical implementation and research. We have provided a simple, empirically grounded mechanism that describes the key components of PLONK. We have done this in such a way that researchers, developers, and teachers can build on this base module and create production-ready systems without the abstraction.
Ignat Melnikov, Roman Vlasov, Vladimir Gorgadze, Andrey Seoev · 5 authors
Decentralized Finance (DeFi) is a rapidly evolving segment of blockchain technology that enables a transformative approach to financial services through Web3 applications. By leveraging smart contracts, DeFi allows developers to build flexible and innovative financial instruments. Among the most prominent DeFi primitives by liquidity are decentralized exchange~(DEX) swap protocols~(such as Uniswap, Curve, and Balancer) that facilitate fast token-to-token exchanges. However, new exchange mechanisms also introduce new market inefficiencies that can be systematically exploited by arbitrageurs. This paper focuses on swap protocols based on the Automated Market Maker~(AMM), where the product of reserves is preserved as an invariant. We analyze the interaction between arbitrageurs and AMM liquidity pools and develop a mathematical model grounded in empirical pool configurations. Using this model, we derive bounds on the joint revenue of liquidity providers~(LPs) and arbitrageurs, propose a method to estimate the expected number of blocks until the occurrence of Impermanent Loss~(IL), and obtain a lower bound on the pool fee required to achieve a fixed target probability of staying in the Impermanent Gain (IG) zone within a block. The proposed framework extends existing LP risk-assessment methodologies by quantifying symbiotic profitability zones, providing a principled basis for fee selection that aligns LP-arbitrageur incentives and enhances market stability.
Zhuoran Pan, Yue Li (102191), Zhi Guan, Jianbin Hu · 5 authors
The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of translating high-level user intents into functionally correct, state-dependent on-chain transactions. We present \textsc{Intent2Tx}, a high-fidelity benchmark featuring 29,921 single-step and 1,575 multi-step instances meticulously derived from 300 days of real-world Ethereum mainnet traces. Unlike prior works that rely on synthetic instructions, \textsc{Intent2Tx} grounds natural language intents in real-world protocol interactions across 11 categories, including diverse long-tail Decentralized Finance (DeFi) primitives. To enable rigorous evaluation, we propose an execution-aware framework that transcends surface-level text matching by employing differential state analysis on forked mainnet environments. Our extensive evaluation of 16 state-of-the-art LLMs reveals that while scaling and retrieval-augmentation enhance logical consistency and parameter precision, current models struggle with out-of-distribution generalization and multi-step planning. Crucially, our execution-based analysis demonstrates that syntactically valid outputs often fail to achieve intended state transitions, highlighting a significant gap in current "reasoning-to-execution" capabilities. \textsc{Intent2Tx} serves as a critical foundation for developing autonomous, reliable agents in intent-centric Web3 ecosystems. Code and data: https://anonymous.4open.science/r/Intent2Tx_Bench-97FF .
The digital money world is facing a massive security challenge. We have Decentralized Finance (DeFi), built on Smart Contracts-which are supposed to be self-executing and unbreakable-running on top of Cloud Computing, which is fast, scalable, but inherently centralized and has a big, easy-to-hit security perimeter. This awkward partnership creates a critical weak point. Hackers aren't breaking the blockchain itself; they are exploiting the connections, like manipulating data feeds (Oracles) or stealing cloud credentials, something old, siloed security checks simply miss. We've developed the Integrated Cloud-DeFi Resilience (ICDR) Framework to fix this. Think of it as a single, smart security bodyguard that protects your system from the cloud down to the code. The ICDR Framework seamlessly brings together three crucial defense layers: first, we automatically audit the Smart Contract code before it even launches; second, we use Cloud Security Posture Management (CSPM) to continuously monitor the cloud infrastructure’s health in real-time; and third, we use specialized Blockchain Technology (DLT) to create an unchangeable, honest record of every security event. The core innovation is its ability to play detective: it catches stealthy attacks by connecting a suspicious administrative action in the cloud (like a key change) with an immediate, shady transaction on the DeFi chain. In our tests on a simulated financial application, this unified approach reduced the time a system was vulnerable (Vulnerability Exposure Rate, or VER) by over 80% compared to separate monitoring tools. The ICDR Framework offers a crucial, practical model for the financial industry to build the resilient, compliant, and trustworthy digital banking systems of tomorrow. Keyword: Security Auditing; Decentralized Finance (DeFi), Smart Contracts, Cloud Computing, Cloud Security Posture Management (CSPM), Oracle Manipulation,Interoperability Risks, Cross-Stack Correlation, Immutable Audit Trail.
Decentralized Finance (DeFi) promised to eliminate traditional financial intermediaries and hierarchies, replacing them with trustless, automated, and decentralized systems. However, the reality of DeFi governance reveals how disintermediation does not equate to the absence of conflicts or trust issues; instead, it shifts them into new, less-regulated domains. Cryptoenterprises—known as financial Decentralized Autonomous Organizations (DAOs)—operate without traditional corporate governance mechanisms such as boards of directors or managerial oversight, which only rely on computer code for governance. Misaligned incentives, governance opacity, and unchecked insider control cause conflicts between insiders (cryptopromoters) and investors (cryptoasset holders). This article examines the emerging role of cryptogatekeepers: a new category of cryptointermediaries that counterbalance these governance failures. It explores the structural deficiencies of cryptoenterprises, including the absence of internal monitoring mechanisms, fiduciary duties, and investor protections. The analysis highlights how cryptopromoters—those in control of DeFi protocols—retain significant decision-making power while obscuring accountability, leading to agency problems reminiscent of traditional finance sans regulatory safeguards By assessing the function of cryptointermediaries as potential de facto governance enforcers, this article argues that cryptogatekeepers can introduce a layer of oversight that compensates for the governance void in DeFi. It outlines best practices for mitigating conflicts of interest, enhancing disclosure standards, and improving the monitoring of cryptointermediaries. The study also considers transnational regulatory approaches to bolster accountability in DeFi through proposing mechanisms such as cryptointermediary registries, mutual recognition of licensed cryptointermediaries, and standardized reporting frameworks. Ultimately, this article contends that while DeFi presents an innovative model for financial services, it cannot escape fundamental governance challenges. The rise of cryptogatekeepers suggests that some level of re-intermediation is inevitable and necessary to balance decentralization with maintaining investor protection and market integrity.
Pavan Sollu, Aniruddha Mukherjee, Divya Pulivarthi, S. R. Eshwar · 9 authors
Hyperledger Fabric (HLF) is a modular, permissioned blockchain widely adopted in enterprise settings. Enhancing its throughput and latency remains challenging, as optimization decisions made in one phase of the transaction lifecycle can adversely affect other phases. In this work, we present a systematic, phase-level and end-to-end study of HLF optimizations along three fronts, combining production-grade testbed experiments with calibrated SimPy simulations. First, we introduce two novel optimization techniques that target commit-phase bottlenecks: block-level pipelining and strategic waiting. In pipelining, we overlap validation and private-data acquisition of successive blocks with state-consistency checks and ledger updates improving commit throughput by up to 1.9x. Strategic waiting coordinates commit progress by temporarily pausing fast leaders and boosting laggers to sustain endorsement parallelism, yielding up to a 1.2x higher throughput. Second, we conduct micro-benchmarking of three configuration levers: private-data dissemination, block-size selection, and endorsement peer selection. Our results reveal that: (i) Relaxed quorums for private-data dissemination significantly reduce latency in both endorsement and commit phases; (ii) Under light workloads, smaller blocks yield lower end-to-end latency, whereas, under heavy workloads, larger blocks are necessary to improve throughput and reduce latency; and (iii) Relaxed leader selection dramatically reduces dropped transactions and boosts endorsement throughput, with a modest increase in MVCC invalidations. Finally, we analyze the interplay among private-data dissemination, VSCC parallelization, and pipelined commits. Interestingly, the throughput gains over a serial commit path are maximized at a moderate level of parallelization. Together, our findings provide phase-aware and protocol-level refinements for optimizing HLF.
LLM agents are promising tools for empirical discovery, but their flexibility can also turn discovery into uncontrolled search. We study how to use agents under a reproducible protocol through cryptocurrency factor discovery. Our framework casts the task as sequential hypothesis search: an agent reads an append-only experiment trace, proposes falsifiable factor hypotheses, and maps them to executable recipes, while a deterministic engine enforces fixed data splits, selection gates, transaction costs, and portfolio tests. Candidate actions are restricted to a point-in-time factor DSL, making both successful and failed hypotheses auditable. A ridge-combined portfolio trained only on 2020--2022 data achieves a 44.55% annualized return and Sharpe ratio of 1.55 in the 2024--2026 pure out-of-sample period after a 5 basis point one-way trading cost.
FlyClient is a lightweight blockchain verification protocol that enables proof-of-work validation using minimal data, making it ideal for resource-constrained environments like mobile wallets, Internet-of-Things devices or cross-chain bridges implemented with smart contracts. Despite its strong potential for enabling lightweight blockchain verification, FlyClient protocol is still in the experimental stages, with limited real-world deployments and performance evaluations under diverse conditions. In this paper we bridge the gap between theory and deployment, by addressing several technical challenges to advance FlyClient to a production-ready solution. Namely, our contribution is three-fold: (i) we formally introduce an adversary model alternative to the original FlyClient one, that allows us to parametrize a verifier under a concrete economic interpretation, while also saving some proof space; (ii) we provide the first practical FlyClient prover implementation for a production blockchain (Zcash), and we estimate its performance under different configurations; (iii) we introduce and evaluate two optimizations that minimize the size of FlyClient proofs, the first of which does not require any consensus change.