Zhiyuan Wei, Jing Sun, Yuqiang Sun, Ye Liu Ā· 13 authors
Blockchainās inherent immutability, while transformative, creates critical security risks in smart contracts, where undetected vulnerabilities can result in irreversible financial losses. Current auditing tools and approaches often address specific vulnerability types, yet there is a need for a comprehensive solution that can detect a wide range of vulnerabilities with high accuracy. We propose LLM-SmartAudit, a novel framework that leverages Large Language Models (LLMs) to automate smart contract vulnerability detection and analysis. Using a multi-agent conversational architecture with a buffer-of-thought mechanism, LLM-SmartAudit maintains a dynamic record of insights generated throughout the audit process. This enables a collaborative system of specialized agents to iteratively refine their assessments, enhancing the accuracy and depth of vulnerability detection. To evaluate its effectiveness, LLM-SmartAudit was tested on three datasets: a benchmark for common vulnerabilities, a real-world project corpus, and a CVE dataset. It outperformed existing tools with 98% accuracy on common vulnerabilities and demonstrates higher accuracy in real-world scenarios. Additionally, it successfully identifies 12 out of 13 CVEs, surpassing other LLM-based methods. These results demonstrate the effectiveness of multi-agent collaboration in automated smart contract auditing, offering a scalable, adaptive, and highly efficient solution for blockchain security analysis.
Sharding is a technique to speed up transaction processing in blockchains, where the $n$ processing nodes in the blockchain are divided into $s$ disjoint groups (shards) that can process transactions in parallel. We study dynamic scheduling problems on a shard graph $G_s$ where transactions arrive online over time and are not known in advance. Each transaction may access at most $k$ shards, and we denote by $d$ the worst distance between a transaction and its accessing (destination) shards (the parameter $d$ is unknown to the shards). To handle different values of $d$, we assume a locality sensitive decomposition of $G_s$ into clusters of shards, where every cluster has a leader shard that schedules transactions for the cluster. We first examine the simpler case of the stateless model, where leaders are not aware of the current state of the transaction accounts, and we prove a $O(d \log^2 s \cdot \min\{k, \sqrt{s}\})$ competitive ratio for latency. We then consider the stateful model, where leader shards gather the current state of accounts, and we prove a $O(\log s\cdot \min\{k, \sqrt{s}\}+\log^2 s)$ competitive ratio for latency. Each leader calculates the schedule in polynomial time for each transaction that it processes. We show that for any $Īµ> 0$, approximating the optimal schedule within a $(\min\{k, \sqrt{s}\})^{1 -Īµ}$ factor is NP-hard. Hence, our bound for the stateful model is within a poly-log factor from the best possibly achievable. To the best of our knowledge, this is the first work to establish provably efficient dynamic scheduling algorithms for blockchain sharding systems.
Democratic institutions increasingly rely on verifiable digital trust to enable fair participation and evidence-based decisions. Truvry is a decentralized protocol that converts behaviour-based evidence (usage patterns, transaction integrity, peer attestations) into portable cryptographic proofs that remain independent of any single platform or identifier, allowing individuals to transfer trust capital across domains while preserving privacy. The current prototype is zero-knowledgeācompatible; in this version we use hashed proof anchoring and field-level redaction (no zk-SNARK module is deployed), with configurable smart-contract verifiers. By decoupling trust from identity, Truvry widens citizen inclusion, mitigates gatekeeping bias, and supplies auditable inputs for AI-mediated governance. In prototype tests (n=112), end-to-end proof issuance averaged 3.7 s (fastest local 1.4 s), verifier parse+check averaged 1.8 s, and the current minimum anonymization entropy is 8.9 bits; gas costs for optional on-chain anchoring remained below US$0.02. All results are based on simulated user streams; a production pilot is planned.
ABSTRACT In this article, we discuss how far Bitcoin has come since its formulation 16 years ago and explore promising areas for future research in finance. The future research topics fall into the following three broad areas: institutional and country adoption, criminality, and Bitcoin's relationship to stablecoins. After a brief discussion of the existing literature, we provide a list of open research questions for future research to explore.
The Agentic Service Ecosystem consists of heterogeneous autonomous agents (e.g., intelligent machines, humans, and human-machine hybrid systems) that interact through resource exchange and service co-creation. These agents, with distinct behaviors and motivations, exhibit autonomous perception, reasoning, and action capabilities, which increase system complexity and make traditional linear analysis methods inadequate. Swarm intelligence, characterized by decentralization, self-organization, emergence, and dynamic adaptability, offers a novel theoretical lens and methodology for understanding and optimizing such ecosystems. However, current research, owing to fragmented perspectives and cross-ecosystem differences, fails to comprehensively capture the complexity of swarm-intelligence emergence in agentic contexts. The lack of a unified methodology further limits the depth and systematic treatment of the research. This paper proposes a framework for analyzing the emergence of swarm intelligence in Agentic Service Ecosystems, with three steps: measurement, analysis, and optimization, to reveal the cyclical mechanisms and quantitative criteria that foster emergence. By reviewing existing technologies, the paper analyzes their strengths and limitations, identifies unresolved challenges, and shows how this framework provides both theoretical support and actionable methods for real-world applications.
We argue that the technical foundations of non-fungible tokens (NFTs) remain inadequately understood. Prior research has focused on market dynamics, user behavior, and isolated security incidents, yet systematic analysis of the standards underpinning NFT functionality is largely absent. We present the first study of NFTs through the lens of Ethereum Improvement Proposals (EIPs). We conduct a large-scale empirical analysis of 191 NFT-related EIPs and 10K+ Ethereum Magicians discussions (as of July, 2025). We integrate multi-dimensional analyses including the automated parsing of Solidity interfaces, graph-based modeling of inheritance structures, contributor profiling, and mining of community discussion data. We distinguish foundational from emerging standards, expose poor cross-version interoperability, and show that growing functional complexity heightens security risks.
This study utilizes version 6 of the regression analysis of time series (RATS) software package to implement the estimation of the bivariate diagonal generalized autoregressive conditional heteroscedasticity (GARCH) model combined with a composite asset selection approach including two hybrid performance measures to solve āthe trade-off problem between return and riskā and āthe inconsistent results from different performance measuresā in the problem of asset allocation within a group of minimum variance portfolios during the pre-COVID-19 and COVID-19 periods. Empirical results show that the optimal portfolios obtained from this approach and the assets added to a portfolio to achieve better performance differ between the pre-COVID-19 and COVID-19 periods. For instance, the optimal portfolios are the Chinese yuan-Ethereum and Bitcoin-Ethereum for the pre-COVID-19 period, but the WTI-Ethereum for the COVID-19 period. To achieve better performance, we added Ethereum to our portfolio during the pre-COVID-19 period, while WTI and Bitcoin were added during the COVID-19 period. Thus, the COVID-19 pandemic had a significant impact on the performance of asset allocation in the three markets. The proposed approaches in this study can be embedded in a computer as an asset allocation algorithm of Robo-advisers.
ā¢ Novel to literature we identify the macroeconomic determinants of cryptocurrency asset holdings.. ā¢ the more emigrants of developing countries living abroad, they intend to hold more cryptocurrencies to ease the transaction costs. ā¢ As countries governance body improves, individuals tend to hold less cryptocurrencies. Employing cross-sectional data of 142 countries worldwide, this paper examines the macroeconomic factors in shaping cryptocurrency adoption. We find that the aggregate impact of inflation volatility on crypto adoption is dependent on the level of corruption control in higher-income countries. The control of corruption appears to discourage cryptocurrency adoption, emphasizing the role of institutional trust in financial choices. We also find that higher emigrant ratios in non-high-income and lower-income countries are associated with increased cryptocurrency usage, which suggests that migrants tend to use cryptocurrencies for faster, cheaper remittances compared to traditional services. Last, we find that internet penetration plays a key role in crypto adoption, particularly in higher-income countries with advanced digital infrastructure.
ABSTRACT This study investigates how overconfident cryptocurrency traders influence the connection between returns and risk premia, proxied by optionāadjusted credit spreads. Using daily data from January 2021 to February 2025, we uncover asymmetry and state dependence: returns decline when spreads widen, particularly during crashes, yet they do not recover when spreads narrow. Equity indices exhibit more balanced coāmovements. The asymmetry strengthens in highāvolatility periods and persists after we control for broad market returns and after we substitute a composite crypto index for individual cryptocurrencies. These findings indicate a distinctive pricing mechanism in cryptocurrency markets shaped by overconfident behaviour and creditāspread dynamics.
Dan Ivanov, Tristan Freiberg, Shahabi, Shirin, Jonathan Gold Ā· 5 authors
DSperse is a modular framework for distributed machine learning inference with strategic cryptographic verification. Operating within the emerging paradigm of distributed zero-knowledge machine learning, DSperse avoids the high cost and rigidity of full-model circuitization by enabling targeted verification of strategically chosen subcomputations. These verifiable segments, or "slices", may cover part or all of the inference pipeline, with global consistency enforced through audit, replication, or economic incentives. This architecture supports a pragmatic form of trust minimization, localizing zero-knowledge proofs to the components where they provide the greatest value. We evaluate DSperse using multiple proving systems and report empirical results on memory usage, runtime, and circuit behavior under sliced and unsliced configurations. By allowing proof boundaries to align flexibly with the model's logical structure, DSperse supports scalable, targeted verification strategies suited to diverse deployment needs.
Open access
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Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
This paper presents an optimal peer-to-peer (P2P) energy transaction mechanism leveraging decentralized blockchain technology to enable a secure and scalable retail electricity market for the increasing penetration of distributed energy resources (DERs). A decentralized bidding strategy is proposed to maximize individual profits while collectively enhancing social welfare. The market design and transaction processes are simulated using the Ethereum testnet, demonstrating the blockchain network's capability to ensure secure, transparent, and sustainable P2P energy trading among DER participants.
This paper examines fiscal policy spillovers through informal international financial channels, using the US stimulus checks as a positive, sudden, and direct fiscal shock. I utilize granular, transaction-level cryptocurrency data combined with an algorithm to probabilistically identify cross-border "crypto vehicle" transactions to construct bilateral cryptocurrency flows between countries. Using a difference-in-differences strategy, I compare cryptocurrency outflows between the US and other high-income countries and find a sharp but temporary increase in cryptocurrency outflows as a result of the direct stimulus. I quantify the fiscal spillover relative to expenditure and place an upper bound of 2.52% through this channel. This implies that fiscal spillovers through remittance channels are likely modest in size.
Blockchain-enabled federated learning (BCFL) addresses fundamental challenges of trust, privacy, and coordination in collaborative AI systems. This chapter provides comprehensive architectural analysis of BCFL systems through a systematic four-dimensional taxonomy examining coordination structures, consensus mechanisms, storage architectures, and trust models. We analyze design patterns from blockchain-verified centralized coordination to fully decentralized peer-to-peer networks, evaluating trade-offs in scalability, security, and performance. Through detailed examination of consensus mechanisms designed for federated learning contexts, including Proof of Quality and Proof of Federated Learning, we demonstrate how computational work can be repurposed from arbitrary cryptographic puzzles to productive machine learning tasks. The chapter addresses critical storage challenges by examining multi-tier architectures that balance blockchain's transaction constraints with neural networks' large parameter requirements while maintaining cryptographic integrity. A technical case study of the TrustMesh framework illustrates practical implementation considerations in BCFL systems through distributed image classification training, demonstrating effective collaborative learning across IoT devices with highly non-IID data distributions while maintaining complete transparency and fault tolerance. Analysis of real-world deployments across healthcare consortiums, financial services, and IoT security applications validates the practical viability of BCFL systems, achieving performance comparable to centralized approaches while providing enhanced security guarantees and enabling new models of trustless collaborative intelligence.
This paper introduces a structural game-theoretic model to value decentralized digital assets like Bitcoin. Instead of relying on speculative beliefs, it frames the asset's price within a Rational-Expectations Security-Utility Nash Equilibrium (RESUNE). This equilibrium is a fixed point where the market-clearing price dictates the hash rate through a free-entry mining model, which in turn endogenously sets the network's security. The security, defined as one minus the probability of a 51% attack, is determined via a global games model of attacker coordination, providing a unique and continuous security function. We prove the existence of a RESUNE and offer conditions for its uniqueness and stability. The model predicts that the stabilizing direct effect of price on demand must outweigh the potentially destabilizing feedback from price to security. The framework generates testable predictions, such as a protocol halving causing a contraction in both hash rate and price. A structural Vector Autoregression (VAR) model is proposed to test this mechanism. The model decomposes Bitcoin's value into transactional utility, security, and speculative components and explains the observed unidirectional causality from price to hash rate.
Mobile edge computing (MEC) is a promising technology that enhances the efficiency of mobile blockchain networks, by enabling miners, often acted by mobile users (MUs) with limited computing resources, to offload resource-intensive mining tasks to nearby edge computing servers. Collaborative block mining can further boost mining efficiency by allowing multiple miners to form coalitions, pooling their computing resources and transaction data together to mine new blocks collaboratively. Therefore, an MEC-assisted collaborative blockchain network can leverage the strengths of both technologies, offering improved efficiency, security, and scalability for blockchain systems. While existing research in this area has mainly focused on the single-coalition collaboration mode, where each miner can only join one coalition, this work explores a more comprehensive multi-coalition collaboration mode, which allows each miner to join multiple coalitions. To analyze the behavior of miners and the edge computing service provider (ECP) in this scenario, we propose a novel two-stage Stackelberg game. In Stage I, the ECP, as the leader, determines the prices of computing resources for all MUs. In Stage II, each MU decides the coalitions to join, resulting in an overlapping coalition formation (OCF) game; Subsequently, each coalition decides how many edge computing resources to purchase from the ECP, leading to an edge resource competition (ERC) game. We derive the closed-form Nash equilibrium for the ERC game, based on which we further propose an OCF-based alternating algorithm to achieve a stable coalition structure for the OCF game and develop a near-optimal pricing strategy for the ECP's resource pricing problem.
This work presents a cryptographic protocol for secure multi-party verification that achieves com putational privacy while maintaining exceptional computational efficiency. The proposed Position Based Commitment Protocol (PBCP) introduces a position-dependent nonce mechanism combined with cyclic verification architecture, enabling se cure computation over private inputs without re vealing individual parameters. Unlike existing commitment schemes that require complex cryp tographic assumptions, computationally expensive zero-knowledge proofs, or extensive public key in frastructure, Fundamental innovation lies in adapt ing physical laws of fluid dynamics to create nat ural mathematical relationships where each verifi cation equation contains multiple unknowns, mak ing parameter extraction computationally infeasible while preserving verification integrity. The proto col preliminary analysis suggests O(n) communica tion complexity with O(n2) verification complexity, providing substantial improvements over traditional Byzantine Agreement protocols that require O(n3) message exchanges. Comprehensive security analysis reveals robust resistance against statistical attacks with complexity O(R3) where R represents the pa rameter range, complete immunity to timing attacks through blind submission mechanisms, and resilience against collusion attacks involving up to n/2 ā 1 ad versarial parties. The protocolās unique cyclic neigh bor verification creates an interdependent validation network that prevents individual parameter extrac tion while maintaining system-wide integrity through mathematical interdependence rather than crypto graphic assumptions.
The purpose of this research is to analyze the long-term and short-term impacts of the use of cryptocurrency and electronic money on the money supply (M2) in Indonesia, as well as to analyze the long-term and short-term impacts of the use of cryptocurrency and electronic money on monetary stability (exchange rates) in Indonesia. The research method used is quantitative descriptive analysis with the Vector Error Correction Model (VECM) using the Eviews application and secondary data in the form of monthly data from 2011 to 2023 obtained from the official websites of Bank Indonesia and Finance. This study utilizes data on cryptocurrency transaction values and electronic money transaction values in Indonesia by analyzing the VECM model, which can observe the long-term and short-term impacts of the use of digital money on the money supply and monetary stability, in this case, viewed through the Indonesian exchange rate. The research results indicate that there is a one-way causality between electronic money and cryptocurrency, but not the other way around, and there is a one-way causality between the money supply and cryptocurrency, with the money supply as the dependent variable influencing cryptocurrency. The results of the VECM estimation indicate that in the long term, electronic money has a negative and sifnificant impact on the money supply. Meanwhile, the short-term estimation shows that both cryptocurrency and electronic money significantly influence the money supply, but their effects are dynamic and vary based on different lags. The long-term estimation with the exchange rate as the dependent variable shows that cryptocurrency does not have a significant impact on the exchange rate, while in the short term, both cryptocurrency and electronic money have a negative and significant effect on the exchange rate.
Proof-of-Work cryptocurrencies employ miners to sustain the system through algorithmic reward adjustments. We develop a stochastic model of the multicurrency mining market and identify conditions for stable transaction speeds. Bitcoin's algorithm requires hash supply elasticity $\le$ 1 for stability, while ASERT remains stable for any elasticity and can be interpreted as a form of stochastic gradient descent algorithm under a certain loss function. Interactions with other currencies can relax Bitcoin's stability requirements. Using a ``halving'' event, we estimate miners' hash supply elasticity and conduct counterfactual simulations. Our findings reveal Bitcoin's heavy reliance on low hash-supply elasticity and interactions with smaller cryptocurrencies, suggesting an algorithm upgrade is crucial for stability.
Meihua Xiao, Y. P. Xu, Yongtuo Zhang, Ke Yang Ā· 6 authors
Smart contracts, as a critical application of blockchain technology, significantly enhance its programmability and scalability, offering broad application prospects. However, frequent security incidents have resulted in substantial economic losses and diminished user trust, making security issues a key challenge for further development. Since smart contracts cannot be modified after deployment, flaws in their design or implementation may lead to severe consequences. Therefore, rigorous pre-deployment verification of their correctness is particularly crucial. This paper explores the symmetry in control flows and state transitions of Solidity smart contracts and leverages this inherent structural symmetry to develop a normalized state transition model based on a finite state machine. The FSM model is subsequently formalized into a Promela model with the Spin model checker. By integrating manually defined Linear Temporal Logic formulas with those generated by Smart Pulse, the Promela model is formally verified in Spin to ensure the correctness and security of smart contracts. This approach establishes a systematic verification framework, providing effective support to enhance the reliability and security of smart contracts.
ErdinƧ Akyıldırım, Ahmet Faruk Aysan, OÄuzhan Ćepni, Shaen Corbet
This study investigates the influence of news-based sentiment on the returns of Decentralized Finance (DeFi) coins using a sample of 27 coins from January 2017 to March 2022. Our results indicate that news sentiment significantly impacts DeFi returns, with negative sentiment exerting a stronger influence than positive sentiment. Transaction volume and network security also emerge as critical drivers of DeFi coin returns. Smaller coins are more sensitive to news sentiment, showing greater return volatility. The impact of news-based sentiment is more pronounced during weekdays, likely due to reduced participation by institutional investors and trading algorithms. These findings have important implications for investors and policymakers, suggesting multiple pathways for market manipulation under specific conditions. ā¢ We investigate the relationship between DeFi coins and news-based sentiment. ā¢ Negative sentiment has a greater impact on returns. ā¢ Transaction volume and network security drive returns. ā¢ Smaller DeFi coins are more susceptible to news sentiment and greater return volatility. ā¢ DeFi returnsā sensitivity to news-media sentiment is significantly elevated during weekdays.
Smart contracts enable contract terms to be automatically executed and verified on the blockchain, and recent years have witnessed numerous applications of them in areas such as financial institutions and supply chains. The execution logic of a smart contract is closely related to the contract state, and thus the correct and safe execution of the contract depends heavily on the precise control and update of the contract state. However, the contract state update process can have issues. In particular, inconsistent state update issues can arise for reasons such as unsynchronized modifications. Inconsistent state update bugs have been exploited by attackers many times, but existing detection tools still have difficulty in effectively identifying them. This paper conducts the first large-scale empirical study about inconsistent state update vulnerabilities (that is, inconsistent state update bugs that are exploitable) in smart contracts, aiming to shed light for developers, researchers, tool builders, and language or library designers in order to avoid inconsistent state update vulnerabilities. We systematically investigate 116 inconsistent state update vulnerabilities in 352 real-world smart contract projects, summarizing their root causes, fix strategies, and exploitation methods. Our study provides 11 original and important findings, and we also give the implications of our findings. To illustrate the potential benefits of our research, we also develop a proof-of-concept checker based on one of our findings. The checker effectively detects issues in 64 popular GitHub projects, and 19 project owners have confirmed the detected issues at the time of writing. The result demonstrates the usefulness and importance of our findings for avoiding inconsistent state update vulnerabilities in smart contracts.
In recent years, cloud marketplaces have emerged as dominant platforms for cloud service procurement. These marketplaces, led by major providers such as Azure, Amazon Web Services, and Google Cloud, feature services exclusively available on their own platforms, introducing limitations for end users. This paper explores the convergence of cloud computing, blockchain, and semantics, envisioning a marketplace where cloud providers can offer their cloud services and cloud consumers can efficiently discover, procure, and potentially resell these services. Semantic representations streamline service discovery and composition, enhancing interoperability. Blockchain technology and non-fungible tokens play pivotal roles in establishing a fully decentralized marketplace characterized by data verification and immutability. This enables autonomous contract validation and execution between cloud consumers and cloud providers, reducing reliance on intermediaries. This innovative fusion promises to reshape the landscape of cloud service procurement, making it more transparent and independent.
Alessia Galdeman, Lucio La Cava, Matteo Zignani, Andrea Tagarelli Ā· 5 authors
The rapid growth of Non-Fungible Tokens (NFTs) and the extensive trading activities associated with such an intriguing domain led to the emergence of large-scale and interconnected transaction networks involving the most prominent NFT markets. Despite such interdependencies representing an inestimable source of information for the proper understanding of the NFT landscape, previous studies treated each market separately, overlooking relevant phenomena. In this study, we explore a multilayer network modeling approach to analyze transactions in multiple NFT markets. We reveal previously unnoticed macroscopic and mesoscopic traits by investigating indicators that discern whether markets are independent or linked: users trading NFTs are organized in cross-market communities where multi-market users act as bridges across marketplaces, adapting to the diverse nature of the markets they operate in. We also conduct an in-depth examination of such multi-market users, studying their specific activity patterns that leave a distinctive mark on the system: the majority of multi-market users well differentiate their earnings and expenses among the markets, while a fraction of them is directed toward a more polarized money allocation based on the typology of the markets. By offering a fresh perspective on this intricate financial system and emphasizing the importance of perceiving the NFT markets as a unique and interconnected world, our study paves the way for further contributions aimed at unraveling the complexity of cryptosystems and understanding the latent phenomena across NFT markets.