O presente artigo formaliza o <i>Economic Centrifugal Dispersion Model</i> (ECDM) como uma estrutura analítica de alta fidelidade para a compreensão da propagação de capital e incentivos em ecossistemas de Web3 e finanças descentralizadas (DeFi). Fundamentado em uma convergência interdisciplinar entre a praxeologia da escola austríaca, a física estatística e a dinâmica de sistemas complexos, o modelo propõe que a injeção monetária em sistemas baseados em blockchain gera forças dispersivas análogas às forças centrífugas. A pesquisa detalha a aplicação do operador de Lyapunov para avaliar a estabilidade e a resiliência desses fluxos sob condições de volatilidade estocástica.<br>
This paper introduces Autonomous Mechanism Economics (AME), a theoretical framework for analyzing economic systems where human discretion is removed from mechanism execution. While classical mechanism design theory (Hurwicz, 1960; Maskin, 1999; Myerson, 1981) focuses on designing incentive-compatible rules, it implicitly assumes human agents execute these rules. We formalize a new class of economic mechanisms-Autonomous Mechanisms (AM)-where execution is performed by deterministic, immutable code rather than discretionary human agents. We establish four core theoretical results. First, Non-Discretionary Buyback (NDB) mechanisms minimize execution-layer agency costs (Theorem 1). Second, assets satisfying specific structural conditions-revenue increasing in market volatility combined with NDB execution-may exhibit antifragility, generating positive expected returns during market stress (Theorem 2). Third, when algorithmic buying capacity exceeds maximum individual selling capacity, markets may undergo threshold transitions to qualitatively different dynamics (Theorem 3). Fourth, USDdenominated staking requirements create self-reinforcing supply dynamics with bounded equilibrium returns (Theorem 4). We connect this framework to Kydland and Prescott (1977)’s “rules versus discretion” literature, arguing that AM protocols may represent a strong rules-based solution by eliminating not merely the incentive but potentially the ability to deviate from prescribed rules. Using data from Hyperliquid—a decentralized exchange implementing NDB at scale—we provide preliminary empirical support, documenting a volume-volatility correlation of 0.627 (p
We consider the interaction between centralized trading and decentralized Proof of Stake (PoS) blockchain ecosystems. Motivated by the increasing dominance of centralized exchanges and the institutionalization of crypto markets, we study how trading activities on centralized exchanges affect staking behavior, token allocation, and decentralization within a PoS blockchain. We formulate a continuous-time mean field model, where the miners simultaneously act as validators in the PoS protocol and traders in a centralized market with price impact. Under suitable assumptions, we establish the local well-posedness of the mean field system, and derive a semi-explicit characterization of the equilibrium trading strategy. Numerical results suggest that centralized trading activities may enhance staking participation, and promote decentralization of the staking distribution through market incentives. We also study the effects of transaction costs and token supply mechanisms on the equilibrium staking ratio and concentration profile. These results illustrate how market microstructure and centralized liquidity provision can exert significant influence on decentralized blockchain protocols.
Este artigo representa uma expansão analítica e quantitativa do Economic Centrifugal Dispersion Model (ECDM), consolidando-o como um framework de "Termodinâmica Criptoeconômica". Enquanto o estudo anterior estabeleceu as bases espaciais e monetárias da força centrífuga econômica, esta continuação aprofunda a modelagem através de equações diferenciais não lineares e introduz o DAO Chaos Index (DCI) para mensurar a instabilidade em governanças descentralizadas.
Este artigo explora a transposição analógica e matemática da Equação de Rayleigh, originalmente concebida no domínio da física acústica e teoria de sinais, para a modelagem de fenômenos complexos em ambientes Web3 e infraestruturas de blockchain. A pesquisa fundamenta-se na premissa de que a distribuição de Rayleigh, ao descrever a magnitude de vetores compostos por componentes gaussianas independentes, oferece um arcabouço robusto para analisar a volatilidade de criptoativos, a latência de propagação de rede, e a resiliência de sistemas de consenso. Através de uma abordagem interdisciplinar que integra econofísica, teoria de sinais e auditoria algorítmica, o estudo discute como a variabilidade estocástica impacta a segurança e a eficiência de protocolos descentralizados. São analisadas as conexões entre a distribuição de Rayleigh e a Lei de Benford na detecção de fraudes em tokenomics, além de contrastar o Efeito Cantillon com o Efeito Nakamoto na distribuição de riqueza digital. Por fim, propõe-se o Modelo de Resiliência Estocástica ECDM (Environment, Consensus, Distribution, Magnitude) como uma ferramenta preditiva para a governança e auditoria de ecossistemas blockchain.<br>
O presente diagnóstico econômico investiga as falhas estruturais e as vulnerabilidades sistêmicas inerentes aos modelos de engenharia econômica da Web3, fundamentando-se no princípio da não neutralidade da moeda conhecido como Efeito Cantillon. A pesquisa articula como a distribuição assimétrica inicial de tokens, frequentemente favorecendo fundadores e investidores institucionais, estabelece uma assinatura econômica de fragilidade que compromete a descentralização e a sustentabilidade dos protocolos digitais. Através de uma abordagem interdisciplinar, o relatório integra o conceito de Doppler Econômico para explicar a defasagem informacional entre agentes privilegiados e o público geral, além de utilizar a metáfora do Mammoth Money para descrever dinâmicas de predação de capital. Para a verificação empírica, aplica-se a Lei de Benford como ferramenta de auditoria estatística e a Curva de Laffer para determinar os limites de incentivos de emissão. O estudo conclui que a resiliência dos ecossistemas Web3 depende de um redesenho fundamental dos mecanismos de alocação inicial e de uma transparência radical que mitigue as distorções perceptivas e econômicas que levam a colapsos catastróficos e eventos de Cisne Negro.<br>
This thesis investigates the design of automated market makers (AMMs) for trading tokenized derivatives in decentralized finance. Motivated by the limitations of existing AMMs, which only permit strictly positive prices, we introduce an invariant that also allows for negative prices. As a primary use case, the thesis develops an AMM for trading an offset token against tokenized euros. The thesis employs, on the one hand, a Monte Carlo–based simulation to evaluate the risk-adjusted returns of an AMM. The simulation includes two types of traders: arbitrage traders, who exploit price deviations between the AMM and the fair value, and noise traders, who represent demand for liquidity. On the other hand, we introduce KPIs such as impermanent loss and market depth. The goal of the thesis is to analyse whether these KPIs can be used to predict the risk-adjusted returns of an AMM.
<p>Resumo <br>Este artigo investiga a viabilidade econômica e comportamental da criação de criptomoedas <br>personalizadas (Fan Coins) atreladas à performance de jogadores de futebol, utilizando métodos <br>quantitativos em Econometria, com foco em arrecadação por bilheteria, patrocínios e apostas esportivas. A <br>análise inclui os casos de Diego Ribas (Flamengo), Neymar (Santos), e artilheiros do São Paulo e Palmeiras. <br>A proposta é analisar como a reputação e a performance esportiva podem ser transformadas em ativos <br>digitais de valor mensurável. Ainda, propõe-se a utilização de sistemas de inteligência artificial para gestão <br>de carteiras de patrocinadores e um aplicativo de fan clube com sistema de assinaturas para fomentar um <br>novo modelo de negócios esportivos baseado em dados e personalização. <br>Palavras-chave: Fan Coin; Criptomoeda; Econometria; Economia do Esporte; Teoria dos <br>Jogos; Finanças Comportamentais; Apostas Esportivas; Inteligência Artificial; Modelagem <br>Financeira; Patrocínio Digital.</p>
Autonomous financial agents, powered by the convergence of artificial intelligence and blockchain technology, represent a paradigm shift in decentralized finance. These self-operating entities now possess capabilities to hold cryptocurrency wallets, execute complex transactions, and even launch tokens without human oversight. The architectural framework supporting these agents integrates specialized language models, secure wallet management systems, and persistent on-chain identities. From market-making to yield optimization, these agents demonstrate remarkable efficacy across various financial operations, creating novel market dynamics when interacting with both human participants and other autonomous systems. Essential to mainstream adoption are sophisticated reputation frameworks combining algorithmic assessment with social consensus mechanisms. However, significant challenges exist, including market manipulation vulnerabilities, spam production, and regulatory complexity. As these autonomous agents continue evolving, appropriate governance models tailored to agent characteristics become critical for balancing innovation with market integrity in this emerging financial landscape.
Consider a social-choice function (SCF) is chosen to decide votes in a formal system, including votes to replace the voting method itself. Agents vote according to their ex-ante belief over what decisions are considered, and whether they prefer them to be decided by the incumbent SCF or the suggested replacement. The existing SCF then aggregates the agents' votes and arrives at a decision of whether it should itself be replaced. An SCF is self-maintaining if it can not be replaced in such fashion by any other SCF. Our focus is on the implications of self-maintenance for centralization. For this purpose, unlike [Barbera and Jackson, 2004], we do not generally restrict attention to anonymous SCFs. We also do not restrict attention to neutral SCFs, unlike [Koray, 2000]. We present results considering optimistic, pessimistic and i.i.d. approaches with respect to agent beliefs, different tie-breaking rules, and different SCF domains. To highlight two of the results, (i) for the i.i.d. unbiased case with arbitrary tie-breaking and general Boolean functions, we prove an Arrow-Style Theorem for Dynamics: We show that only a dictatorship is self-maintaining, and any other SCF has a path of changes that arrives at a dictatorship. (ii) With a pessimistic approach, tie-breaking that prefers the status quo, and WMGs, we provide a tight characterization of the self-maintaining rules, which are exactly all games with minimal winning coalitions of size at most 2. We then consider two extensions, (i) forward-looking voters, (ii) Where the voter utility depends on wisdom of the crowd effects. In both cases, less centralized SCFs become self-maintaining. All in all we provide a basic framework and body of results for centralization dynamics and stability, applicable for institution design, especially in formal De-Jure systems, such as Blockchain Decentralized Autonomous Organizations (DAOs).
Cryptocurrencies market capitalization has surpassed $4 trillion in 2025, attracting individual and institutional traders seeking investment and speculation. However, volatility of cryptocurrencies prices makes profitable strategies a huge challenge, especially with respect to the variance of returns. In this context, this paper presents an innovative strategy based on the counterintuitive concept from Game Theory called Parrondo’s Paradox. The presented strategy results in improved capital gains (returns) when compared to traditional buy & hold. Also, the strategy is proven to work in daily, weekly and minute-by-minute timeframes. With the empirical results shown in this paper, the Parrondo’s Paradox framework can be used as a trading strategy by either individual or institutional investors.
Historical Context and Problem Statement The digital revolution has created two parallel challenges that have resisted comprehensive solutions: Internet Data Transfer Limitations: Despite decades of progress, internet download speeds remain constrained by inefficient protocols that don't adapt to network topology dynamics. Traditional download managers like IDM operate with static segmentation strategies that ignore the quantum-inspired probabilistic nature of network paths. Web3 Liquidity Fragmentation: Decentralized finance (DeFi) suffers from fragmented liquidity across multiple venues, resulting in significant MEV exploitation. As documented by Qin et al. (2021), MEV extraction has cost users over $680 million in 2021 alone, with no comprehensive solution addressing the root cause. These seemingly disconnected problems share a common underlying structure: both involve the transfer of "value" (data or financial assets) across complex networks where efficiency is hampered by non-resonant transmission strategies.
Stanisław Drożdż, Paweł Jarosz, Jarosław Kwapień, Maria Skupień · 5 authors
Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale- and fluctuation-dependent correlation matrices using the multifractal detrended cross-correlation coefficient ρr that selectively emphasizes fluctuations of different amplitudes. We examine the spectral properties of these detrended correlation matrices and compare them to the spectral properties of the matrices calculated in the same way from synthetic Gaussian and q-Gaussian signals. Our results show that detrending, heavy tails, and the fluctuation-order parameter r jointly produce spectra, which substantially depart from the random case even under the absence of cross-correlations in time series. Applying this framework to one-minute returns of 140 major cryptocurrencies from 2021 to 2024 reveals robust collective modes, including a dominant market factor and several sectoral components whose strength depends on the analyzed scale and fluctuation order. After filtering out the market mode, the empirical eigenvalue bulk aligns closely with the limit of random detrended cross-correlations, enabling clear identification of structurally significant outliers. Overall, the study provides a refined spectral baseline for detrended cross-correlations and offers a promising tool for distinguishing genuine interdependencies from noise in complex, nonstationary, heavy-tailed systems.
This paper introduces the Universal Turing Market Machine (UTMM): a unified, neuromorphic market infrastructure designed to compute, adapt, and coordinate economic activity autonomously. Building on Hayek’s theory of spontaneous order and Ashby’s Law of Requisite Variety, the paper argues that while markets themselves emerge naturally, the computational substrate that supports them can be intentionally designed. The UTMM integrates sensory inputs (e.g., IoT data), distributed ledger signaling, evolutionary compute layers, and real-world actuators to form an adaptive, nervous-system-like architecture for market coordination. This framework enables transparent, auditable, self-organizing market processes capable of discovering their own requisite dimensionality. The paper formalizes these systems under the term Adaptive Resource-Coordinated Organisms (ARCOs), digital-economic organisms that merge machine learning, blockchain, and adaptive market solvers into a cohesive evolutionary market machine.
Behavioral finance has emerged as a critical framework for understanding market dynamics beyond traditional rational agent models. This research presents a comprehensive multimodal approach to behavioral finance analysis, integrating market data, macroeconomic indicators, news sentiment, cryptocurrency metrics, Web3 analytics, GitHub development activity, and social sentiment to test five advanced hypotheses regarding behavioral pattern identification and market anomaly detection. The study employs an ultra-comprehensive data pipeline processing 30,400 samples across seven distinct data sources, generating 91 engineered features representing behavioral biases, investment patterns, and market psychology. Advanced machine learning techniques including Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding, Variational Autoencoders, K-Means, Hierarchical Clustering, DBSCAN, Isolation Forest, One-Class SVM, and Elliptic Envelope are applied to identify behavioral structures and detect anomalies. Statistical validation through chi-square tests, ANOVA, Granger causality analysis, and lagged correlation studies demonstrates that three of five hypotheses (60%) achieve statistical significance at p < 0.05. Key findings reveal that behavioral structures exist and correspond to canonical biases (chi-square = 3406.780, p < 0.001), cluster assignments maintain moderate stability across market regimes (Jaccard similarity = 0.300), and sentiment and macroeconomic factors exhibit 65 significant causal relationships with behavioral patterns. However, multimodal data integration does not uniformly improve clustering quality (Silhouette score decrease of 0.116), and cluster-conditioned anomaly detection fails to outperform global methods (F1-score decrease of 0.017). These findings contribute to behavioral finance theory while providing practical applications for investment management, fraud detection, and regulatory compliance.
Mohd Shahid Ali, Alam Ahmad, Mohd Atif, Monika Mittal · 5 authors
Decentralized exchanges (DEXs) are one of the keystones of decentralized finance (DeFi). Instead of booking the order under centralized system, you have straight peer-to-peer trades via Automated Market Makers (AMM). AMMs like Uniswap and Curve have actually been developed to reduce the friction of liquidity provisioning. Nevertheless, they still experience impermanent loss, deadweight loss, compartmentalization of market liquidity, in addition to suboptimal operation in volatile environments. This paper explains an RL-based algorithm that can regulate liquidity and flexible market-making in DEXs. RL agents has been trained to maximize capital allowance, liquidity rebalancing, and spread adjusting in live trading information from SushiSwap and Uniswap in addition to synthetically created cardiovascular test. DQN, PPO, and A3C are three RL algorithms that we have actually contrasted versus constant-product AMM standards. With risk-adjusted returns of as much as 1.6 vs. 0.9, an impermanent loss reduction of 15-20%, and test-set revenues of 12.5 -15.7% vs. 8, it seems that RL-poured method is considerably much better. The stability and scalability of RL models under different swimming pool dimensions and volatility regimes are further made certain by level of sensitivity analysis. Actually, PPO is the most effective in high-volatility circumstances, DQN assembles more quickly in moderate scenarios, and A3C offers a trade-off. Our results open up the design of flexible monetary AI systems and are right away appropriate to enhancing liquidity rewards, stability, and performance in DeFi. The result of the experiment shows that RL can be made use of to improve the rationality of liquidity administration in DEXs. The integration of administration systems right into multi-agent RL and the gas-efficient migration of these algorithms from off-chain to on-chain ought to be the primary tasks of future research study.
The rise of Decentralized Finance (DeFi), enabled by blockchain technology, has introduced open and transparent financial ecosystems that contrast sharply with traditional financial systems. While DeFi expands the financial landscape and democratizes participation in global markets, it also introduces new complexities. Classic valuation models used in traditional finance often fall short in this context. However, DeFis transparency---where all transactions are publicly recorded---offers a unique opportunity to model and understand market behavior using modern analytical tools. Motivated by the challenges and opportunities of DeFi, the main goal of this thesis is to propose novel methods to understand market dynamics through the lens of network science and machine learning. To this end, we focus on four specific objectives: (i) assess whether structural information from blockchain transaction networks provides predictive signals beyond traditional indicators; (ii) develop robust trust-based valuation metrics for DeFi protocols; (iii) develop a framework for forecasting financial time series through uncertainty-aware machine learning architectures; (iv) construct diversified portfolios using network-based representations of asset relationships. First, using Ethereum as a case study, we analyze the influence of the transaction network on market trends by comparing the performance of two machine learning models: one that uses technical analysis and social media indicators commonly found in the literature and another that incorporates structural properties of the transaction network. We found that by including transaction network information, we can anticipate 46% more uptrends and 19% more downtrends, highlighting the predictive power of the transaction network. Second, we introduce the TVL/MCAP bands as a tool to identify periods of overconfidence and underconfidence in the DeFi market. We show that extreme values of this indicator can signal price movements: values above the 95th percentile are associated with a 15\% higher return in the following month, while values below the 5th percentile anticipate declines, highlighting investor confidence as a key market driver. Third, we address the need for forecasts that not only anticipate market trends but also quantify the uncertainty surrounding them. To this end, we integrate Reservoir Computing (RC) with conformal prediction methods to provide statistically rigorous forecasts along with prediction intervals. We found that RC outperform traditional econometric models, particularly in anticipating the trend of financial time series. Furthermore, we show that conformal methods, especially quantile-conformal variants, significantly improve forecast reliability while adapting to market volatility. Finally, we address the challenge of portfolio optimization using network-based methods. Specifically, we model the network of relationships between cryptocurrencies to obtain a market representation that enables selecting a more diversified portfolio. We find that peripheral assets enhance portfolio stability and returns, while links bridging network communities carry higher risk. Thereby, these results highlight the importance of structural diversification in volatile markets. In addition, we contribute to refining pairs trading strategies by proposing the Hurst exponent to identify rapid mean-reversion opportunities. We show that anti-persistent values of H lead to faster reversion and consistent returns---minimizing trading costs and enabling broader portfolio construction. In conclusion, this thesis provides an interdisciplinary analytical framework that advances our understanding of DeFi markets. By introducing network-based indicators, trust metrics, uncertainty-aware forecasts, and diversification strategies grounded in market structure, we provide new tools for investors and researchers to navigate the complexity and volatility inherent in decentralized financial systems. RESUMEN El auge de las Finanzas Descentralizadas (DeFi), impulsado por la tecnología blockchain, ha dado lugar a ecosistemas financieros más accesibles y transparentes que contrastan con los sistemas financieros tradicionales. DeFi amplía el panorama financiero actual e introduce nuevos retos, como la necesidad de un nuevo modelo de valoración de los activos. No obstante, el hecho de que todas las transacciones son públicas, ofrece una oportunidad única para modelar y comprender la dinámica del mercado mediante nuevas herramientas analíticas. Esta tesis tiene como objetivo principal proponer nuevos métodos para comprender la dinámica del mercado desde la perspectiva de los sistemas complejos y el aprendizaje automático. Para ello, nos centramos en cuatro objetivos específicos: (i) evaluar si la información estructural de las redes de transacciones aporta señales predictivas más allá de los indicadores tradicionales; (ii) desarrollar métricas de valoración de los protocolos DeFi basadas en la confianza de los inversores; (iii) construir un marco metodológico para predecir series temporales financieras mediante arquitecturas de aprendizaje automático que incorporen incertidumbre; (iv) construir portfolios diversificados utilizando representaciones de la red de relaciones entre criptomonedas. En primer lugar, utilizando Ethereum como caso de estudio, analizamos la influencia de la red de transacciones sobre la tendencia del mercado comparando dos modelos de aprendizaje automático: uno que emplea indicadores de análisis técnico y de redes sociales comunes en la literatura, y otro incluyendo propiedades estructurales de la red de transacciones. Los resultados muestran que incluyendo información de la red podemos anticipar un 46% más de tendencias alcistas y un 19% más de tendencias bajistas, lo que subraya el poder predictivo de la red de transacciones. En segundo lugar, introducimos las bandas TVL/MCAP para identificar períodos de sobreconfianza y desconfianza en el mercado DeFi. Demostramos que valores extremos de este indicador anticipan movimientos en el precio: valores por encima del percentil 95 se asocian con un rendimiento 15% superior en el mes siguiente, mientras que valores por debajo del percentil 5 anticipan caídas. En tercer lugar, abordamos la necesidad de predicciones que no solo anticipen tendencias del mercado, sino que también cuantifiquen la incertidumbre. Para ello, integramos Reservoir Computing (RC) con métodos de predicción conforme para generar predicciones estadísticamente rigurosas junto con intervalos de confianza. Mostramos que RC supera a los modelos econométricos tradicionales, especialmente anticipando la tendencia del precio. Además, los métodos conformes ---en particular las variantes de cuantiles--- mejoran significativamente la fiabilidad de las predicciones al adaptarse a la volatilidad del mercado. Por último, abordamos el problema de optimización de portfolios mediante métodos basados en redes. Específicamente, modelamos la red de relaciones entre criptomonedas para seleccionar un portfolio más diversificado. Observamos que evitar pares que conectan distintas comunidades en la red y priorizar activos periféricos aumenta el rendimiento y disminuye el riesgo, demostrando así la importancia de una diversificación estructural. Además, proponemos el uso del exponente de Hurst para identificar oportunidades que revierten antes a la media en estrategias de pairs trading. En conclusión, esta tesis propone un marco analítico interdisciplinar que contribuye al entendimiento de los mercados DeFi. Al introducir indicadores basados en redes, métricas de confianza, predicciones con estimación de incertidumbre y estrategias de diversificación basadas en la estructura del mercado, ofrecemos nuevas herramientas para que inversores e investigadores naveguen la complejidad y volatilidad propias de los sistemas financieros descentralizados.
This study explores Bitcoin's value formation through the Granular Interaction Thinking Theory-Value Theory (GITT-VT). Rather than stemming from material utility or cash flows, Bitcoin's value arises from informational attributes and interactions of multiple factors, including cryptographic order, decentralization-enabled autonomy, trust embedded in the consensus mechanism, and socio-narrative coherence that reduce entropy within decentralized value-exchange processes. To empirically assess this perspective, a Bayesian linear model was estimated using daily data from 2022 to 2025, operationalizing four informational value dimensions: Store-of-Value (SOV), Autonomy (AUT), Social-Signal Value (SSV), and Hedonic-Sentiment Value (HSV). Results indicate that only SSV exerts a highly credible positive effect on next-day returns, highlighting the dominant role of high-entropy social information in short-term pricing dynamics. In contrast, SOV and AUT show moderately reliable positive associations, reflecting their roles as low-entropy structural anchors of long-term value. HSV displays no credible predictive effect. The study advances interdisciplinary value theory and demonstrates Bitcoin as a dual-layer entropy-regulating socio-technological ecosystem. The findings offer implications for digital asset valuation, investment education, and future research on entropy dynamics across non-cash-flow digital assets.
Decentralized Finance (DeFi) has undergone significant expansion, evolving from a niche market into a complex alternative financial ecosystem. This burgeoning landscape now encompasses a diverse array of financial services, including decentralized exchanges, lending and borrowing platforms, stablecoins, derivatives, yield optimization services, prediction markets, and privacy-enhancing technologies such as token mixers. While the total value locked in DeFi protocols—estimated at approximately 77 billion USD—underscores its increasing significance, it simultaneously highlights the critical necessity for robust security measures. This workshop aims to address the pressing security challenges in the maturing DeFi space by convening leading experts from the fields of cryptography, game theory, economics, and cybersecurity. Our primary objective is to foster interdisciplinary dialogue and showcase cutting-edge research that rigorously examines the current state of DeFi security and charts a comprehensive path forward. The anticipated outcomes include a prioritized research agenda, new collaborative initiatives bridging theoretical advancements with practical implementations, and a strategic roadmap for enhancing security in the rapidly evolving DeFi ecosystem. This year's program features a keynote talk by Prof. Vassilis Zikas, two invited talks by the winners of the Best DeFi Paper Award (theoretical research track and applied research track), and four presentations of accepted original papers, showcasing both fundamental advances and real-world applications.