Blockchain Papers

Muhammad Ali Hassan Ahmad, Muhammad Hashim Ali, Muhammad Ali Amer, Muhammad Naiman Jalil · 6 authors

A blockchain is a decentralized, secure ledger system that enables transparent and immutable record-keeping, essential for trust and security in digital transactions. Smart contracts are self-executing agreements encoded on a blockchain, enabling different parties to fulfill the terms of the agreement automatically. These contracts trigger corresponding actions when conditions are met, ensuring decentralized and transparent transactions. Writing reliable smart contracts is challenging due to the lack of standardization. To find security vulnerabilities, tools based on various approaches, including symbolic execution, are used. However, these tools often report a large number of false positives, raising concerns about their reliability. The time and effort spent investigating false positives diverts resources from addressing actual vulnerabilities. Therefore, such tools must also be evaluated according to the rate of false positives they exhibit. More importantly, the algorithms and heuristics used by the tools must be enhanced to distinguish between true vulnerabilities and false alarms. In this paper, we first demonstrate the prevalence of false positives in vulnerability reports generated by Mythril, a symbolic execution-based analysis tool for Ethereum smart contracts. We analyze the root causes of these inaccuracies and devise a rule-based approach based on the gained insight to reduce false positives. We implement our rules for the most impactful vulnerabilities in Mythril and assess the effectiveness of our approach. Our results show a significant reduction in false positives without compromising the detection of true vulnerabilities, thus enhancing the tool's reliability.

Jakub Kacper Szeląg, Aydin Abadi, Mohammad Naseri

Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, distributed computing, artificial intelligence, cloud infrastructures, and Internet of Things (IoT) architectures, there remains no universally accepted definition of decentralization applicable across computer communication systems. This has become increasingly problematic with the emergence of decentralized AI and machine learning paradigms, including collaborative training, distributed inference, blockchain-based, and agentic AI, where decentralization is often treated as a core design objective. Meanwhile, existing approaches frequently conflate decentralization with related notions such as distribution of trust or specific implementation paradigms. Such ambiguity creates inconsistencies in system analysis, limits comparability between works, and weakens the rigor of formal reasoning surrounding communication architectures and protocol design. In this work, we define this research gap as the Decentralization Problem. We analyze the formal-semantic, epistemological, and pragmatic foundations of decentralization and introduce a graph-based ontology defining it as both relational and subject-specific property of computer communication systems. The framework formally distinguishes decentralization from distribution and supports evaluation through two novel metrics: Void Tolerance and Imperviousness. We also provide a browser-based implementation that enables automated classification and metric computation of arbitrary systems. Instantiations to federated learning and blockchain architectures show consistent, comparable assessments where existing definitions produce incomplete or contradictory conclusions, providing a domain-independent foundation for analysing decentralization across heterogeneous systems.

Julia Kończal, Rafał Połoczański

Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies. We study four Bitcoin and Ethereum ETPs traded on Xetra and Nasdaq Stockholm over the period January 2024 - December 2025 using one-minute bars. As a benchmark, we adopt an extreme value theory approach in which anomalous bars are defined as returns falling below a threshold estimated by fitting a generalised Pareto distribution to left-tail exceedances. We then propose three new binary anomaly indicators. The first, a cross-venue divergence anomaly, identifies venue-specific price divergence between the two exchanges. The second is a no-recovery anomaly that identifies extreme price drops followed by little or no recovery over the next ten active bars. The third is a momentum-reversal anomaly that identifies extreme price drops following positive short-term momentum. Although each anomaly type represents fewer than 1% of one-minute bars, statistical analysis using Mann-Whitney U tests shows that anomaly observations exhibit significantly higher effective spreads, higher values of liquidity-related ratios, and more pronounced order-flow imbalances than non-anomalous bars. Furthermore, employing an out-of-sample prediction methodology with four classifiers - random forest, logistic regression, extreme gradient boosting, and light gradient boosting machine - shows that all four anomaly types are predictable one bar ahead, with AUC-ROC values of up to 0.82. Permutation importance indicates that short-term volatility and drawdown measures are generally more useful for prediction than microstructure variables.

Kundan Mukhia, Sabat Rai, Vivek Shrivastav, Imran Ansari · 5 authors

Stablecoins have rapidly emerged as an important class of digital assets and a component of the digital financial ecosystem. Despite their growing importance, the statistical properties of stablecoin transaction activity remain largely unexplored. To the best of our knowledge, this is the first study to investigate scaling behavior in stablecoin transaction data, focusing on USDT and USDC. We analyze approximately 370 million USDT and USDC transactions recorded on the Ethereum blockchain across six periods spanning June 2024 to February 2026. Based on interactions between Externally Owned Accounts (EOAs) and Smart Contracts (SCs), we classify transactions into four categories: EOA-EOA, EOA-SC, SC-EOA, and SC-SC. Using maximum-likelihood estimation of power-law exponents, we find that transaction value distributions exhibit heavy-tailed scaling for both stablecoins across all periods and interaction categories. We identify two distinct scaling regimes: EOA-involved categories cluster around 1.45-1.60, whereas SC-SC transactions exhibit higher exponents of approximately 1.72-1.73. Sensitivity analysis confirms that this separation is robust across periods, stablecoins, and fitting sample sizes. Counterfactual analysis shows that changes in category weights alone cannot explain the observed variation in the overall exponent. Across different sample sizes, the counterfactual path accounts for only about 10%-35% of the total temporal range observed in the actual data. Overall, our results indicate two broadly differentiated scaling regimes in the tail of stablecoin transaction values. Power-law tail behavior is observed throughout stablecoin transaction activity, but the exponent depends on whether transactions are driven by EOAs or SCs. These findings provide a basis for further research on scaling behavior and transaction heterogeneity in blockchain-based financial systems.