Bence Soóki-Tóth, István András Seres, Kamilla Kara, Ábel Nagy · 6 authors
The long-term success of cryptocurrencies largely depends on the incentive compatibility provided to the validators. Bribery attacks, facilitated trustlessly via smart contracts, threaten this foundation. This work introduces, implements, and evaluates three novel and efficient bribery contracts targeting Ethereum validators. The first bribery contract enables a briber to fork the blockchain by buying votes on their proposed blocks. The second contract incentivizes validators to voluntarily exit the consensus protocol, thus increasing the adversary's relative staking power. The third contract builds a trustless bribery market that enables the briber to auction off their manipulative power over the RANDAO, Ethereum's distributed randomness beacon. Finally, we provide an initial game-theoretical analysis of one of the described bribery markets.
This paper introduces Large Execution Models (LEMs), a novel deep learning framework that extends transformer-based architectures to address complex execution problems with flexible time boundaries and multiple execution constraints. Building upon recent advances in neural VWAP execution strategies, LEMs generalize the approach from fixed-duration orders to scenarios where execution duration is bounded between minimum and maximum time horizons, similar to share buyback contract structures. The proposed architecture decouples market information processing from execution allocation decisions: a common feature extraction pipeline using Temporal Kolmogorov-Arnold Networks (TKANs), Variable Selection Networks (VSNs), and multi-head attention mechanisms processes market data to create informational context, while independent allocation networks handle the specific execution logic for different scenarios (fixed quantity vs. fixed notional, buy vs. sell orders). This architectural separation enables a unified model to handle diverse execution objectives while leveraging shared market understanding across scenarios. Through comprehensive empirical evaluation on intraday cryptocurrency markets and multi-day equity trading using DOW Jones constituents, we demonstrate that LEMs achieve superior execution performance compared to traditional benchmarks by dynamically optimizing execution paths within flexible time constraints. The unified model architecture enables deployment across different execution scenarios (buy/sell orders, varying duration boundaries, volume/notional targets) through a single framework, providing significant operational advantages over asset-specific approaches.
Stefanos Chaliasos, Conner Swann, Sina Pilehchiha, Nicolas Mohnblatt · 6 authors
Rollups have become the de facto scalability solution for Ethereum, securing more than $55B in assets. They achieve scale by executing transactions on a Layer 2 ledger, while periodically posting data and finalizing state on the Layer 1, either optimistically or via validity proofs. Their fees must simultaneously reflect the pricing of three resources: L2 costs (e.g., execution), L1 DA, and underlying L1 gas costs for batch settlement and proof verification. In this work, we identify critical mis-pricings in existing rollup transaction fee mechanisms (TFMs) that allow for two powerful attacks. Firstly, an adversary can saturate the L2's DA batch capacity with compute-light data-heavy transactions, forcing low-gas transaction batches that enable both L2 DoS attacks, and finality-delay attacks. Secondly, by crafting prover killer transactions that maximize proving cycles relative to the gas charges, an adversary can effectively stall proof generation, delaying finality by hours and inflicting prover-side economic losses to the rollup at a minimal cost. We analyze the above attack vectors across the major Ethereum rollups, quantifying adversarial costs and protocol losses. We find that the first attack enables periodic DoS on rollups, lasting up to 30 minutes, at a cost below 2 ETH for most rollups. Moreover, we identify three rollups that are exposed to indefinite DoS at a cost of approximately 0.8 to 2.7 ETH per hour. The attack can be further modified to increase finalization delays by a factor of about 1.45x to 2.73x, compared to direct L1 blob-stuffing, depending on the rollup's parameters. Furthermore, we find that the prover killer attack induces a finalization latency increase of about 94x. Finally, we propose comprehensive mitigations to prevent these attacks and suggest how some practical uses of multi-dimensional rollup TFMs can rectify the identified mis-pricing attacks.
We formulate automated market maker (AMM) \emph{rebalancing} as a binary detection problem and study a hybrid quantum--classical self-attention block, \textbf{Quantum Adaptive Self-Attention (QASA)}. QASA constructs quantum queries/keys/values via variational quantum circuits (VQCs) and applies standard softmax attention over Pauli-$Z$ expectation vectors, yielding a drop-in attention module for financial time-series decision making. Using daily data for \textbf{BTCUSDC} over \textbf{Jan-2024--Jan-2025} with a 70/15/15 time-series split, we compare QASA against classical ensembles, a transformer, and pure quantum baselines under Return, Sharpe, and Max Drawdown. The \textbf{QASA-Sequence} variant attains the \emph{best single-model risk-adjusted performance} (\textbf{13.99\%} return; \textbf{Sharpe 1.76}), while hybrid models average \textbf{11.2\%} return (vs.\ 9.8\% classical; 4.4\% pure quantum), indicating a favorable performance--stability--cost trade-off.
Since its inception in 2009, cryptocurrencies have been a subject of debate in literature. In the general literature, the debate is mainly about the legality and application of these currencies while in Islamic literature, the debate is about its compliance with Sharia rules and directions. The primary objective of this study was to analyze current cryptocurrencies using a novel methodology and propose a new Islamic cryptocurrency, called “Halal Coin”. To achieve the objectives of this study, a qualitative research method was followed by analyzing how the included cryptocurrencies work, analyzing some of its data for the period from January 1, 2023 to May 31,2025, and determining the characteristics of the proposed coin. Data used in this study were analyzed using descriptive statistics and the measure of “value at risk”. The results revealed that none of the current cryptocurrencies are Sharia-compliant, and the proposed Halal coin is characterized by 15 attributes, including being accessible to all people, serving as a unit of account, and being free from high volatility.
In the current economic background, traditional supply chain finance mainly relies on the credit of core enterprises, but the credit is difficult to be effectively transmitted to small and medium-sized enterprises (SMEs) at the end of the supply chain. This paper examines the application of blockchain technology in SMEs supply chain finance through a case study of AntChain platform. The distributed ledger and smart contract technologies of blockchain can effectively solve the problems of information asymmetry, high financing costs and high financing risks in SMEs supply chain financing. After AntChain platform integrates the data of the upstream and downstream of the supply chain, it can provide more flexible financing for SMEs through the credit transfer of core enterprises. However, there are also certain deficiencies in actual operation. It is suggested to strengthen data privacy security, lower the entry barriers for SMEs and actively expand strategic partnerships to improve the development of AntChain platform.
This paper analyzes the influence of Bitcoin whales on price formation through the lens of market microstructure. Whales affect volatility and liquidity not only via large trades but also by sending strong informational signals. Despite a gradual diffusion of ownership and the rise of derivatives, whales remain structurally capable of destabilizing or stabilizing Bitcoin's market dynamics.
This article explores the transformative potential of Blockchain and distributed ledger technologies (DLT) in Afghanistan’s financial sector, amid a backdrop of systemic instability, infrastructural gaps, and geopolitical constraints. Drawing on an extensive review of digital banking development, expert interviews, and comparative global experiences, the study critically assesses Afghanistan's readiness to adopt Blockchain as a tool for financial inclusion, transparency, and institutional resilience. Although the formal banking system has largely regressed post-2021, grassroots crypto adoption reflects a latent readiness for decentralized solutions. The paper argues for a strategic, phased approach to Blockchain integration through regulatory reform, stakeholder engagement, and pilot implementations, particularly in land registration and remittance processing.
As Web3 matures, decentralized naming and storage systems, such as ENS, Unstoppable Domains, and IPFS, offer new paradigms for publishing and accessing web content without relying on centralized infrastructure.However, the process of retrieving content in such an environment remains fragmented, often dependent on vulnerable public gateways or centralized APIs.This paper investigates the resilience of content retrieval in decentralized systems, using Web3Compass as a case study.The system integrates real-time registry monitoring, onchain name resolution, and direct access to decentralized storage via self-hosted IPFS nodes.By avoiding reliance on third-party resolution services and fallback gateways except when necessary, Web3Compass provides a robust method for discovering and rendering Web3 websites.We detail the system's architecture, including resolver logic, node infrastructure, and content validation policies, and evaluate its robustness against gateway failure, incomplete pinning, and resolution inconsistencies.Our findings indicate that proactive pinning, resolver-specific logic, and local node infrastructure significantly improve access reliability, even under constrained network conditions.
The rapid growth of decentralized web technologies, such as IPFS, ENS, and Arweave, has enabled the creation and hosting of censorship-resistant, open-access websites.However, these systems suffer from a fundamental usability problem: decentralized websites are effectively invisible to the average user due to the absence of an indexing and discovery infrastructure.This paper introduces Web3 Compass, a search engine purpose-built for the decentralized internet.Unlike traditional search engines that rely on centralized servers and behavioral tracking, Web3 Compass discovers and indexes content from decentralized domains through real-time blockchain monitoring, resolver contract interactions, and a custom IPFS infrastructure.It outlines the visibility problem, examines failed or insufficient past solutions, and presents the architectural design of a hybrid, privacy-preserving search tool optimized for the decentralized web.The contribution aims to address the core bottleneck in Web3 usability by making decentralized content discoverable and accessible.
Large Language Models (LLMs) have enabled the emergence of autonomous agents capable of complex reasoning, planning, and interaction. However, coordinating such agents at scale remains a fundamental challenge, particularly in decentralized environments where communication lacks transparency and agent behavior cannot be shaped through centralized incentives. We propose a blockchain-based framework that enables transparent agent registration, verifiable task allocation, and dynamic reputation tracking through smart contracts. The core of our design lies in two mechanisms: a matching score-based task allocation protocol that evaluates agents by reputation, capability match, and workload; and a behavior-shaping incentive mechanism that adjusts agent behavior via feedback on performance and reward. Our implementation integrates GPT-4 agents with Solidity contracts and demonstrates, through 50-round simulations, strong task success rates, stable utility distribution, and emergent agent specialization. The results underscore the potential for trustworthy, incentive-compatible multi-agent coordination in open environments.
The global carbon credit trading market faces significant challenges including lack of real-time verification, double-spending issues, and insufficient transparency in emission measurements. This paper presents a novel blockchain-enabled framework integrating Internet of Things (IoT) sensors for automated carbon credit generation and trading. Our proposed system combines tamper-proof IoT sensor networks with smart contract automation to address current limitations in carbon credit systems. The methodology employs distributed sensor nodes equipped with CO2, temperature, and humidity sensors connected to an Ethereum-based blockchain network. Through extensive simulation and real-world testing, our system demonstrates 99.2% accuracy in emission measurement and real-time carbon credit generation. The framework reduces verification time by 87% compared to traditional manual verification processes while ensuring immutable transaction records. Key contributions include: (1) a decentralized IoT-blockchain architecture for carbon monitoring, (2) smart contract protocols for automated credit generation, (3) a novel consensus mechanism for sensor data validation, and (4) comprehensive security analysis demonstrating resistance to common blockchain attacks. Results indicate significant potential for transforming carbon credit markets through enhanced transparency, reduced fraud, and improved environmental monitoring accuracy.
Zero-knowledge rollups rely on provers to generate multi-step state transition proofs under strict finality and availability constraints. These steps require expensive hardware (e.g., GPUs), and finality is reached only once all stages complete and results are posted on-chain. As rollups scale, staying economically viable becomes increasingly difficult due to rising throughput, fast finality demands, volatile gas prices, and dynamic resource needs. We base our study on Halo2-based proving systems and identify transactions per second (TPS), average gas usage, and finality time as key cost drivers. To address this, we propose a parametric cost model that captures rollup-specific constraints and ensures provers can keep up with incoming transaction load. We formulate this model as a constraint system and solve it using the Z3 SMT solver to find cost-optimal configurations. To validate our approach, we implement a simulator that detects lag and estimates operational costs. Our method shows a potential cost reduction of up to 70\%.
This paper presents the design and implementation of a blockchain-secured system for monitoring driver sobriety and real-time geolocation. The proposed platform integrates a Modular Sensor Battery (MSB) for detecting alcohol concentration in exhaled air, a centralized Data Collection Platform (DC Platform) for real-time data visualization and storage, and a complementary physiological monitoring device—the IoT Fit-Bit Smart Band (IFSB)—which captures heart rate and blood oxygen saturation as alternative indicators when breath-based sensing may be compromised. The MSB, the DC Platform, integration with the IoT FitBit Smart Band, and the blockchain-based data management architecture represent the authors’ direct contribution to both the conceptual design and technical implementation. These elements are introduced as part of a unified, fully integrated system designed to enable non-invasive sobriety monitoring and secure data integrity in vehicular contexts. To ensure data authenticity, a custom Ethereum smart contract stores cryptographic hashes of sensor readings, enabling decentralized, tamper-evident verification without exposing sensitive medical information. The system was validated in a controlled experimental environment, confirming its operational robustness and demonstrating its potential to improve road safety through secure, real-time sobriety detection and geolocation tracking.
In the last decade, cryptocurrency has emerged as a major financial and technological phenomenon. This research explores the use of Bitcoin in Yemen. Yemen is a country currently facing a severe humanitarian and economic crisis. The study aims to analyze the opportunities offered by Bitcoin. These opportunities could help overcome traditional financial constraints. The research also examines the challenges that hinder its spread and use. An analytical descriptive methodology was used. The study looked at economic, legal, and social aspects. The results showed that Bitcoin provides real opportunities. It can facilitate financial transfers and offer alternatives to broken banking systems. However, it also faces significant challenges. These include the absence of a legal framework. Other challenges are weak infrastructure and the risks of security breaches and fraud. The research also addressed the legal stance towards these currencies, which remains unclear. The study concludes with recommendations. It provides suggestions for relevant authorities and users. The goal is to maximize benefits and reduce the risks of cryptocurrencies. It emphasizes the need for effective regulations. This will ensure safe and sustainable use.