This study investigates the dynamic relationship between order flow toxicity, measured by the volume-synchronized probability of informed trading (VPIN), and price jumps in the Bitcoin market using high-frequency data and vector autoregressive model (VAR) modelling. By integrating behavioral finance theory to market microstructure framework, we explore how informed trading activity influences jumps in price, and how traders respond to such volatility. Our findings reveal that VPIN significantly predicts future price jumps, with positive serial correlation observed in both VPIN and jump size, suggesting persistent asymmetric information and momentum effects. On the contrary, price jumps occasionally affect VPIN. This study also identifies time-zone and day-of-the-week effects in VPIN, highlighting the role of global trading patterns. The results are robust among the choices of jump tests including Jiang and Oomen (2008) test which is empirically robust against market microstructure noise. These results contribute to a deeper understanding of intraday volatility in cryptocurrency markets and offer practical implications for risk management, trading strategy design, and regulatory oversight.
Fuzzing is an effective technique to detect vulnerabilities in smart contracts. The challenge of smart contract fuzzing lies in the statefulness of contracts, which indicates that certain vulnerabilities can only be manifested in specific contract states. State-of-the-art fuzzers may generate and execute a plethora of meaningless or redundant transaction sequences during fuzzing, incurring a penalty in efficiency. To this end, we present DepFuzz , a hybrid fuzzer for efficient smart contract fuzzing, which introduces a symbolic execution module into the feedback-based fuzzer. Guided by the distance-based function dependencies between functions, DepFuzz can efficiently yield meaningful transaction sequences that contribute to vulnerability exposure or code coverage. The experiments on 286 benchmark smart contracts and 500 large real-world smart contracts corroborate that, compared to state-of-the-art approaches, DepFuzz achieves higher instruction coverage rate and uncovers many more vulnerabilities with less time.
Open access
Blockchain Technology Applications and Security
Auction Theory and Applications
Advanced Steganography and Watermarking Techniques
Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending classical results in mechanism design, and introduce a decentralized price adjustment algorithm with provable convergence guarantees that can be implemented in real time. To evaluate performance, we combine extensive synthetic benchmarks with a proof-of-concept real-world dataset (MovieLens). The synthetic tests probe robustness under fee volatility, participation shocks, and dynamic demand, while the MovieLens case study illustrates how the mechanism can balance efficiency and fairness in realistic allocation environments. Results demonstrate that the proposed mechanism achieves substantial improvements in both efficiency and equity while remaining resilient to abrupt perturbations, confirming its stability beyond steady state analysis. The findings highlight broad managerial and policy relevance for supply chains, logistics, energy markets, healthcare resource allocation, and public infrastructure, where transparent and auditable coordination is increasingly critical. By combining theoretical rigor with empirical validation, the study shows how digital contracts can serve not only as technical artifacts but also as institutional instruments for transparency, accountability, and resilience in high-stakes resource allocation.
Effective supply chain management under high-variance demand requires models that jointly address demand uncertainty and digital contracting adoption. Existing research often simplifies demand variability or treats adoption as an exogenous decision, limiting relevance in e-commerce and humanitarian logistics. This study develops an optimization framework combining dynamic Negative Binomial (NB) demand modeling with endogenous smart contract adoption. The NB process incorporates autoregressive dynamics in success probability to capture overdispersion and temporal correlation. Simulation experiments using four real-world datasets, including Delhivery Logistics and the SCMS Global Health Delivery system, apply maximum likelihood estimation and grid search to optimize adoption intensity and order quantity. Across all datasets, the NB specification outperforms Poisson and Gaussian benchmarks, with overdispersion indices exceeding 1.5. Forecasting comparisons show that while ARIMA and Exponential Smoothing achieve similar point accuracy, the NB model provides superior stability under high variance. Scenario analysis reveals that when dispersion exceeds a critical threshold (r > 6), increasing smart contract adoption above 70% significantly enhances profitability and service levels. This framework offers actionable guidance for balancing inventory costs, service levels, and implementation expenses, highlighting the importance of aligning digital adoption strategies with empirically observed demand volatility.
Decentralized Autonomous Organizations (DAOs) aim to enable participatory governance, but in practice face challenges of voter apathy, concentration of voting power, and misaligned delegation. Existing delegation mechanisms often reinforce visibility biases, where a small set of highly ranked delegates accumulate disproportionate influence regardless of their alignment with the broader community. In this paper, we conduct an empirical study of delegation in DAO governance off-chain discussions from 14 DAO forums. We develop a methodology to link forum participants to on-chain addresses, extract governance interests using large language models, and compare these interests against delegates' historical behavior. Our analysis reveals that delegations are frequently misaligned with token holders' expressed priorities and that current ranking-based interfaces exacerbate power concentration. We argue that incorporating interest alignment into delegation processes could mitigate these imbalances and improve the representativeness of DAO decision-making. To support future research, we will release our dataset and code in a public repository.
Introduction This study explores the feasibility of embedding futarchy, specifically policy-binding conditional prediction markets anchored to democratically chosen key performance indicators (KPIs) in Decentralized Science (DeSci) governance. By externalizing belief formation to speculative markets while anchoring values democratically, futarchy offers a structurally distinct alternative to existing Decentralized Autonomous Organization (DAO) governance models. Methods Through an empirical analysis of governance data from 13 DeSci DAOs, this study examines governance, participation, and cadence patterns that condition futarchic adoption. A retrospective simulation using proposals from VitaDAO assessed the degree to which historical decisions align with futarchy-preferred outcomes. Results The results indicate full directional alignment under deterministic modeling, suggesting latent compatibility between futarchy and existing DeSci governance. Discussion The analysis further outlines the design principles for implementation, emphasizing measurable KPIs and epistemic diversity. Futarchy, if carefully instantiated, may serve as a governance alternative for funding truth-tracking science through probabilistic decision making and market-based information aggregation.
The intersection of non-fungible tokens (NFTs) and decentralised autonomous organisations (DAOs) highlights two transformative, yet divergent, applications of blockchain technology. NFTs focus on establishing unique digital ownership, emphasising individuality and exclusivity, while DAOs represent a collective governance model based on community-driven decision-making. This dynamic mirrors the contrasting personalities in The Odd Couple , symbolising the challenge of balancing uniqueness with collective action. Despite their differences, NFTs and DAOs are increasingly integrated in innovative ways, enabling new forms of collaboration and legal challenges. This chapter explores how NFTs and DAOs coexist, examining their legal structures, governance mechanisms and practical applications. Ultimately, it asks whether these two concepts are truly an ‘odd couple’ or a symbiotic pairing that is redefining ownership, governance and digital interaction.
Smart contracts underpin contemporary decentralized systems, yet their immutability and perpetual execution amplify the consequences of latent defects. Despite progress in manual audits, static analysis, fuzzing, and formal verification, auditors face a widening gap between the scalability and desired assurance due to limited automation capability. Recent large language models (LLMs) with tool-use capabilities promise greater automation, but monolithic single-agent auditors struggle with coverage, robustness, and reproducibility. Motivated by addressing these issues, we propose Multi-Agent Auditing (MAA), a framework that coordinates a team of tool-grounded agents through a constrained protocol that privileges verifiable artifacts. Besides, we mechanize an LLM-assisted orchestration mechanism and a shared knowledge base to coordinate a set of agents specialized in sophisticated testing approaches to produce budget-aware and evidence-centric audit results. Furthermore, we present the experiment results showing that MAA outperforms singleLLM auditors and provide empirical insights into LLM-backend selection.
Securities trading systems have settlement efficiency, audit transparency, and fraud prevention concerns due to centralized intermediaries and aging infrastructure. Existing research models risk counterparty trading due to delayed settlements, opaque record keeping, and human compliance checks. The study aims to design and evaluate a blockchain-based equities trading platform for transaction security and traceability. Provable Atomic Consensus for Trading (PACT), a blockchain-based architecture for regulated financial institutions' trading environments, combines hybrid consensus with a privacy-preserving cryptographic approach. A hybridized consensus process for efficient transaction finality, zero-knowledge proof enabled atomic settlements for instant delivery vs. payment while protecting commercial secrecy, and regulator-accessible smart contracts for real-time compliance checks are used in the PACT algorithm PACT found a 20% reduction in consensus finality time, 53% reduction in proof verification time, 56% improvement in smart contract vulnerability, and 42% improvement in auditability index on a permissioned blockchain with hardware-accelerated smart contracts. The study indicated 35.6% lower throughput and 41.7% lower Tx volume over 10 validators. Latency over 10 validators is 24% lower and Tx volume is 23.2% lower than existing research models. Blockchain improves securities infrastructure speed, reliability, and transparency without affecting compliance, according to studies.
Inbamalar T M, Abarna S, Abinaya G, D. R. · 5 authors
A decentralized Non-Fungible Token (NFT) marketplace website powered by block chain technology enables secure and transparent trading of digital assets has been proposed. Unlike traditional platforms, this system eliminates the central authority and provides full control over their NFTs to the users. It incorporates smart contracts to automate transactions, ensuring efficiency and security. As there is no limit to crypto currency, the marketplace works exclusively through crypto currencies, enhancing global access and minimizing transaction fees. The platform also features a bargaining system, allowing buyers and sellers to negotiate prices directly. By utilizing decentralized storage solutions like IPFS, the project ensures secure, immutable storage of NFT metadata and assets. This website enables users to buy or sell their NFTs. The process involves selecting the desired NFT from the list of NFTs. The next step is the "make offer" system, where the buyer and seller negotiate the price. After that, the price is fixed, and the transaction is processed with the crypto currency in their wallet. Finally, the ownership is updated by the smart contract on the block chain and the database. This website sets a new standard for efficient, secure, and innovative NFT trading. Thus, it motivates people to buy assets from the internet as they have ownership over their assets
Blockchain technology has driven the development of Decentralized Applications (DApps) in areas such as decentralized finance. However, as application scenarios become more complex, the limitations of computational resources and costs gradually lead to insufficient performance. Large Language Models (LLMs), as a promising technology, have the potential to enhance blockchain’s capabilities in complex task governance. However, due to factors such as consensus mechanisms, it is challenging to directly integrate them with blockchain. To address this issue, this article proposes and implements a general framework for integrating LLMs with blockchain data, C-LLM, which successfully overcomes interoperability barriers between the two. By combining semantic relevance evaluation and truth discovery techniques, this article presents an innovative data aggregation method, SenteTruth, which effectively improves the correctness and credibility of data generated by LLMs. To validate the framework’s effectiveness, we construct a dataset containing three types of questions, covering Q&A records between 10 oracle nodes and 5 LLM models. Experimental results show that, in the presence of 40% malicious nodes, the proposed method improves data correctness by an average of 17.74% compared with the optimal baseline. This research not only provides an innovative solution for the intelligent application of smart contracts but also demonstrates the potential for deep integration of LLMs and blockchain, driving the development of smarter and more complex application scenarios for smart contracts.
Amulyashree Sridhar, Kalyan Nagaraj, S. Ravi, Sindhu Kurup
The current research aims to discover applications of QML approaches in realizing liabilities within smart contracts. These contracts are essential commodities of the blockchain interface and are also decisive in developing decentralized products. But liabilities in smart contracts could result in unfamiliar system failures. Presently, static detection tools are utilized to discover accountabilities. However, they could result in instances of false narratives due to their dependency on predefined rules. In addition, these policies can often be superseded, failing to generalize on new contracts. The detection of liabilities with ML approaches, correspondingly, has certain limitations with contract size due to storage and performance issues. Nevertheless, employing QML approaches could be beneficial as they do not necessitate any preconceived rules. They often learn from data attributes during the training process and are employed as alternatives to ML approaches in terms of storage and performance. The present study employs four QML approaches, namely, QNN, QSVM, VQC, and QRF, for discovering susceptibilities. Experimentation revealed that the QNN model surpasses other approaches in detecting liabilities, with a performance accuracy of 82.43%. To further validate its feasibility and performance, the model was assessed on a several-partition test dataset, i.e., SolidiFI data, and the outcomes remained consistent. Additionally, the performance of the model was statistically validated using McNemar's test.
The intersection of Blockchain and Artificial Intelligence (AI) holds the potential to revolutionize the way smart contracts are deployed and managed. Blockchain provides decentralization, immutability and transparency however its deterministic and inflexible characteristics make it challenging to adapt to dynamic and rapidly changing environments. On the other hand, AI can provide the ability to do things such as pattern recognition, predictive analytics, and intelligent decision-making, but lacks the trust and verifiability that a blockchain can provide. This article presents research on the incorporation of AI in blockchain-powered smart contracts to improve trust, operational efficacy, and execution precision. We propose a new type of architecture where AI agents run in or adjacent to smart contracts to optimally set the conditions, automatically resolve disputes, detect anomalous behavior, and validate external data using oracles and machine learning models in real-time. By illustrating AIenabled smart contracts in domains such as supply chain management and decentralized finance (DeFi), we show how the use of AI can improve the performance of smart contracts by lowering latency, eliminating fraudulent triggers of contracts and the ability for contracts to adapt based on context-aware inputs without having to sacrifice the integrity and auditability native in decentralized systems. These two aspects of performance, namely, gas cost reduction, error rate minimization, and contract adaptability, are studied across the domains, or environments, of public and permissioned blockchains. The paper further addresses hurdles considering AI interpretability, on-chain computational limits, and the necessity for uniform standards for AI-blockchain interfacing. By combining the trust layer of blockchain with the cognitive layer of AI, we unlock a new realm of smart contracts - those that are not only self-executing but also self-optimizing and contextually aware.
The unprecedented rise of Bitcoin has marked a significant milestone in the evolution of decentralized finance (DeFi). Despite Bitcoin's groundbreaking contributions, it faces inherent challenges due to its reliance on the Unspent Transaction Output (UTXO) model, which limits its capabilities in executing complex transactions and embedding diverse data types. To overcome these limitations, Ordinals and Inscriptions have been introduced, allowing extensive data and information embedding within Bitcoin transactions. Building upon these advancements, the recent development of the BRC-20 standard leverages these capabilities to introduce fungible tokens on the Bitcoin network. However, these innovations have yet to be fully integrated into the broader blockchain and DeFi ecosystems, and they still face challenges related to interoperability, scalability, and user perception. In this paper, we explore the intricacies of Ordinal, Inscription, and BRC protocols to address these challenges by analyzing their functionalities, operational methodologies, and potential applications. We offer a detailed examination of the challenges and future prospects, shedding light on the unexplored potential of these technologies in transforming Bitcoin transactions and expanding its role in the DeFi space. By thoroughly analyzing these new developments, we aim to bridge the gap in current academic research and offer valuable insights for developers, investors, and enthusiasts. This paper serves as a foundation for future innovations, paving the way for more robust, scalable, and user-friendly applications in the DeFi and Web3 landscape.
Eyal Briman, Nimrod Talmon, Angela Kreitenweis, Muhammad Idrees
Abstract The Optimism Retroactive Project Funding (RetroPGF) is a key initiative within the blockchain ecosystem that retroactively rewards projects deemed valuable to the Ethereum and Optimism communities. Managed by the Optimism Collective, a decentralized autonomous organization (DAO), RetroPGF represents a large-scale experiment in decentralized governance. Funding rewards are distributed in OP tokens, the native digital currency of the ecosystem. As of this writing, four funding rounds have been completed, collectively allocating over $100M, with an additional $1.3B reserved for future rounds. However, we identify significant shortcomings in the current allocation system, underscoring the need for improved governance mechanisms given the scale of funds involved. Leveraging computational social choice techniques and insights from multiagent systems, we propose improvements to the voting process by recommending the adoption of a utilitarian moving phantoms mechanism [1]. This mechanism was originally introduced by Freeman et al. [1], is designed to enhance social welfare (using the $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm) while satisfying strategyproofness–two key properties aligned with the application’s governance requirements. Our analysis provides a formal framework for designing improved funding mechanisms for DAOs, contributing to the broader discourse on decentralized governance and public goods allocation.
Bitcoin money laundering detection faces critical challenges including labeled data scarcity, extreme class imbalance, and transactional heterogeneity. To address these issues, we propose DyHom-SSL, a semisupervised learning framework integrating dynamic pseudolabel optimization, and homophily-aware graph learning. The framework operates in two stages: in the warm-up stage, a similarity-based mechanism dynamically generates thresholds for high-quality pseudolabels, replacing confidence-based selection to enhance flexibility, while in the consistency training stage, a learnable data augmentation module is introduced and optimized through the dual objectives of consistency (semantic preservation) and diversity (feature variation). In addition, homophily distribution leverages topological differences between illicit and licit nodes to resolve class imbalance without distorting data distribution. Extensive evaluations on elliptic, elliptic++, and AMLworld datasets demonstrate state-of-the-art performance, achieving 92.29% precision, 77.30% recall, and 84.02% F1-score on the elliptic dataset. DyHom-SSL outperforms graph and nongraph baselines in both homogeneous and heterogeneous datasets, proving its effectiveness for real-world antimoney laundering (AML) applications.