This article explores the integration and innovation of NFT (Non-Fungible Token) and digital twin technologies within the realm of metaverse architecture. As the construction industry faces challenges such as limited resources, rising costs, and environmental pressures, the metaverse offers a new direction through digital modeling and twin representation technologies. These technologies are applied throughout the entire lifecycle of architectural projects, providing fundamental technical support and practical experience for the realization of metaverse architecture. The article highlights the unique features of metaverse architecture, such as virtuality, interactivity, and infinite possibilities, which allow for greater design freedom and new creative platforms for architects. The fusion of NFT and digital twin technologies brings new business models and market opportunities to the construction industry. The research method involves case studies, including the Mars House as an example of NFT architecture and the digital twin application of Beijing CBD. The main results show that this technological integration can enhance design efficiency, optimize asset management, and promote innovation and collaboration in the construction industry.
Ahmed Mohamed Abdallah, Heba K. Aslan, Mohamed S. Abdallah, Young Im Cho · 5 authors
In recent years, the rapid growth of cryptocurrency markets has highlighted the urgent need for advanced security solutions capable of addressing a spectrum of unique threats, from phishing and wallet hacks to complex blockchain vulnerabilities. This paper presents a comprehensive approach to fortifying cryptocurrency systems by harnessing the structural symmetry inherent in transactional patterns. By leveraging local large language models (LLMs), embeddings, and vector databases, we develop an intelligent and scalable security expert system that exploits symmetry-based anomaly detection to enhance threat identification. Cryptocurrency networks face increasing threats from sophisticated attacks that often exploit asymmetric vulnerabilities. To counteract these risks, we propose a novel security expert system that integrates symmetry-aware analysis through LLMs and advanced embedding techniques. Our system efficiently captures symmetrical transaction patterns, enabling robust detection of anomalies and threats while preserving structural integrity. By integrating a modular framework with LangChain and a vector database (Chroma DB), we achieve improved accuracy, recall, and precision by leveraging the symmetry of transaction distributions and behavioral patterns. This work sets a new benchmark for LLM-driven cybersecurity solutions, offering a scalable and adaptive approach to reinforcing the security symmetry in cryptocurrency systems. The proposed expert system was evaluated using a benchmark dataset of cryptocurrency transactions, including real-world threat scenarios involving phishing, fraudulent transactions, and blockchain anomalies. The system achieved an accuracy of 92%, a precision of 89%, and a recall of 93%, demonstrating a 10% improvement over existing security frameworks. Compared to traditional rule-based and machine learning-based detection methods, our approach significantly enhances real-time threat detection while reducing false positives. The integration of LLMs with embeddings and vector retrieval enables more efficient contextual anomaly detection, setting a new benchmark for AI-driven security solutions in the cryptocurrency domain.
У статті проаналізовано можливості платформи Ethereum для розроблення та впровадження смарт-контрактів в інформаційній системі обліку продажу зброї. Розглянуто архітектуру платформи, механізми забезпечення прозорості, безпеки та достовірності даних у процесах купівлі-продажу. Особлива увага приділена аналізу ключових компонентів платформи, таких як Ethereum Virtual Machine (EVM) та механізм консенсусу Proof of Stake, які забезпечують децентралізовану та захищену основу для роботи смарт-контрактів. Досліджено потенціал смарт-контрактів для автоматизації ключових етапів обліку, включаючи перевірку дозволів на придбання зброї, реєстрацію угод, контроль дотримання законодавчих норм і відстеження змін у даних у реальному масштабі часу. Визначено переваги використання блокчейн-технологій, серед яких — незмінність записів, доступність для аудиту в режимі реального часу, підвищена прозорість операцій і мінімізація людського фактору. Окремо підкреслено, що інтеграція блокчейну дозволяє створити надійні механізми запобігання несанкціонованому доступу, фальсифікації даних і шахрайству. Розглянуто приклади реалізації смарт-контрактів у системі обліку продажу зброї та описано, як автоматизація цих процесів може сприяти зменшенню адміністративного навантаження. Водночас у статті висвітлено ключові виклики впровадження системи, включаючи питання масштабованості мережі, забезпечення конфіденційності персональних даних користувачів, адаптацію існуючих законодавчих норм до блокчейн-рішень і підвищення рівня довіри між учасниками системи. Запропоновано можливі напрями подальших досліджень, зокрема щодо інтеграції смарт-контрактів з іншими цифровими технологіями для забезпечення комплексного підходу до обліку та моніторингу. Результати роботи демонструють високий потенціал використання платформи Ethereum для підвищення ефективності та прозорості процесів у сфері контролю обігу зброї. Інтеграція смарт-контрактів із сучасними інформаційними системами обліку відкриває нові можливості для створення надійних і безпечних рішень, які відповідають викликам цифрової епохи.
Smart contracts are small programs that run autonomously on the blockchain, using it as their persistent memory. The predominant platform for smart contracts is the Ethereum VM (EVM). In EVM smart contracts, a problem with significant applications is to identify data structures (in blockchain state, a.k.a. "storage"), given only the deployed smart contract code. The problem has been highly challenging and has often been considered nearly impossible to address satisfactorily. (For reference, the latest state-of-the-art research tool fails to recover nearly all complex data structures and scales to under 50% of contracts.) Much of the complication is that the main on-chain data structures (mappings and arrays) have their locations derived dynamically through code execution. We propose sophisticated static analysis techniques to solve the identification of on-chain data structures with extremely high fidelity and completeness. Our analysis scales nearly universally and recovers deep data structures. Our techniques are able to identify the exact types of data structures with 98.6% precision and at least 92.6% recall, compared to a state-of-the-art tool managing 80.8% and 68.2% respectively. Strikingly, the analysis is often more complete than the storage description that the compiler itself produces, with full access to the source code.
With the rise of blockchain technology and the Ethereum platform, non-fungible tokens (NFTs) have emerged as a new class of digital assets. The NFT transfer network exhibits core-periphery structures derived from different partitioning methods, leading to local discrepancies and global diversity. We propose a core-periphery structure characterization method based on Bayesian and stochastic block models (SBMs). This method incorporates prior knowledge to improve the fit of core-periphery structures obtained from various partitioning methods. Additionally, we introduce a locally weighted core-periphery structure aggregation (LWCSA) scheme, which determines local aggregation weights using the minimum description length (MDL) principle. This approach results in a more accurate and representative core-periphery structure. The experimental results indicate that core nodes in the NFT transfer network constitute approximately 2.3-5% of all nodes. Compared to baseline methods, our approach improves the normalized mutual information (NMI) index by 6-10%, demonstrating enhanced structural representation. This study provides a theoretical foundation for further analysis of the NFT market.
Md. Shahidul Islam, Monjira Bashir, Siddikur Rahman, Md Abdullah Al Montaser · 7 authors
The cryptocurrency market, with its record volatility and breakneck speed, is a revolutionary phenomenon that is reshaping the entire world's landscape. Unlike regular markets, cryptocurrencies undergo unprecedented volatility caused by a complex interaction of factors ranging from speculative trading to updates in regulations, technological innovations, and macroeconomic trends. The central objective of this research was to develop and evaluate machine learning-driven models of cryptocurrency price trend forecasting. The focus of this research project revolved around prominent cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), and other prominent altcoins, within the United States. The dataset employed in this analysis comprises vast historical price data, trading volumes, and key market indicators of major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), and other major altcoins. Historical price data is presented in terms of daily, hourly, and minute-level opening, closing, high, and low prices, providing detailed insights into temporal price behavior. Trading volumes, which reflect the intensity of trading action, are also provided to represent liquidity and investor participation behavior. The dataset also includes various market indicators, i.e., moving averages, relative strength index (RSI), Bollinger Bands, and other technical indicators, which play a pivotal role in establishing market patterns and momentum. Three models are chosen in this study: Logistic Regression, Random Forest Classifier, and XG Boost Classifier. For classification models, accuracy, precision, recall, and F1-score metrics are employed to evaluate the performance of the models in terms of predicting the directions of the markets (e.g., upward or downward directions). With the highest accuracy, Logistic Regression was the best-performing of the models tested, showing its relative superiority. The integration of AI forecasts into cryptocurrency trading has the potential to revolutionize the United States financial markets by providing traders and institutional investors with advanced tools to make decisions. The use of AI tools in cryptocurrency trading also has significant implications for United States regulation compliance. The integration of machine learning tools within cryptocurrency trading platforms is a significant step towards unleashing the true potential of AI in the financial markets. The field of AI-based cryptocurrency forecasting offers numerous areas of future research with the potential to break through present limitations and unlock new paths of market analysis. One of those areas is the use of deep learning models, i.e., Long Short-Term Memory (LSTM) networks, for time-series cryptocurrency forecasting.
S Sudhip, Pratyush Sthapit, S. Maheshwari, Samarjit Sahu · 5 authors
Abstract: In today's fast-paced world, tracking personnel has become a necessity for various organizations, especially in industries such as Police Department, and security. The use of Near Field Communication (NFC) devices has emerged as a promising technology for tracking personnel. This paper presents a study on the feasibility and benefits of tracking personnel using NFC devices. Our study is based on NFC (Near Field Communication) which is A wireless communication technology that allows two devices to Exchange data when they are brought into proximity. NFC when used in reader/writer mode NFC device can read From NFC transponders or NFC writer. NFC when used in peerTo-peer mode NFC can be used to exchange information Between two NFC enabled devices and in card emulation mode NFC device can be used with Contactless Card for various Purposes like paying money or exchanging information Security of NFC device can be ensured by various means of Encryption and now we have new web3 technology.
This study provides a comprehensive analysis of the dynamic interconnectedness between traditional fiat currencies (CHF and JPY) and cryptocurrencies (Bitcoin and Ethereum) across three distinct periods: the pre-COVID-19, the COVID-19 pandemic, and the Russia-Ukraine conflict. Our methodology employs the Quantile Vector Autoregressive (QVAR) connectivity approach, beginning with the average median and progressively extending to various quantiles over time revealing both short-term and long-term dynamic connectedness. Our findings reveal that Bitcoin and Ethereum exhibit significant interconnectedness and predominantly act as net transmitters of volatility, especially in the short term. In contrast, CHF and JPY generally serve as shock absorbers, showing strong self-dependency and conditional safe-haven properties. Particularly, the Swiss Franc occasionally transmits volatility during extreme market conditions, highlighting its dynamic role. The implications of our study are crucial for investors and portfolio managers aiming to adjust dynamically their portfolios by actively monitoring market trends to modify their allocations between traditional safe-haven currencies and cryptocurrencies. Specifically, in times of increased volatility, managers should temporarily reduce exposure to cryptocurrencies and increase allocations in stable fiat currencies such as CHF and JPY. Conversely, during more stable periods, higher investments in cryptocurrencies could yield better returns. implementing a real-time volatility monitoring system can aid managers in making well-informed choices to optimize risk management strategies. Dynamic hedging is preferred over static approaches.
In this paper, we describe the motivation, design, security properties, and a prototype implementation of NickPay, a new privacy-preserving yet auditable payment system built on top of the Ethereum blockchain platform. NickPay offers a strong level of privacy to participants and prevents successive payment transfers from being linked to their actual owners. It is providing the transparency that blockchains ensure and at the same time, preserving the possibility for a trusted authority to access sensitive information, e.g., for audit purposes or compliance with financial regulations. NickPay builds upon the Nicknames for Group Signatures (NGS) scheme, a new signing system based on dynamic ``nicknames'' for signers that extends the schemes of group signatures and signatures with flexible public keys. NGS enables identified group members to expose their flexible public keys, thus allowing direct and natural applications such as auditable private payment systems, NickPay being a blockchain-based prototype of these.
Certified randomness can be generated with untrusted remote quantum computers using multiple known protocols, one of which has been recently realized experimentally. Unlike the randomness sources accessible on today's classical computers, the output of these protocols can be certified to be random under certain computational hardness assumptions, with no trust required in the hardware generating the randomness. In this perspective, we explore real-world applications for which the use of certified randomness protocols may lead to improved security and fairness. We identify promising applications in areas including cryptography, differential privacy, financial markets, and blockchain. Through this initial exploration, we hope to shed light on potential applications of certified randomness.
Martin Perešíni, Ivan Homoliak, Samuel Olekšák, Samuel Slávka
Traditionally, mobile wallets rely on a trusted server that provides them with a current view of the blockchain, and thus, these wallets do not need to validate the header chain or transaction inclusion themselves. If a mobile wallet were to validate a header chain and inclusion of its transactions, it would require significant storage and performance overhead, which is challenging and expensive to ensure on resource-limited devices, such as smartphones. Moreover, such an overhead would be multiplied by the number of cryptocurrencies the user holds in a wallet. Therefore, we introduce a novel approach, called FeatherWallet, to mobile wallet synchronization designed to eliminate trust in a server while providing efficient utilization of resources. Our approach addresses the challenges associated with storage and bandwidth requirements by off-chaining validation of header chains using SNARK-based proofs of chain extension, which are verified by a smart contract. This offers us a means of storing checkpoints in header chains of multiple blockchains. The key feature of our approach is the ability of mobile clients to update their partial local header chains using checkpoints derived from the proof verification results stored in the smart contract. In the evaluation, we created zk-SNARK proofs for the 2, 4, 8, 16, 32, and 64 headers within our trustless off-chain service. For 64-header proofs, the off-chain service producing proofs requires at least 40 GB of RAM, while the minimal gas consumption is achieved for 12 proofs bundled in a single transaction. We achieved a 20-fold reduction in storage overhead for a mobile client in contrast to traditional SPV clients. Although we have developed a proof-of-concept for PoW blockchains, the whole approach can be extended in principle to other consensus mechanisms, e.g., PoS.
In this paper, we present the first large-scale empirical study of smart contract dependencies, analyzing over 41 million contracts and 11 billion interactions on Ethereum up to December 2024. Our results yield four key insights: (1) 59% of contract transactions involve multiple contracts (median of 4 per transaction in 2024) indicating potential smart contract dependency risks; (2) the ecosystem exhibits extreme centralization, with just 11 (0.001%) deployers controlling 20.5 million (50%) of alive contracts, with major risks related to factory contracts and deployer privileges; (3) three most depended-upon contracts are mutable, meaning large parts of the ecosystem rely on contracts that can be altered at any time, which is a significant risk, (4) actual smart contract protocol dependencies are significantly more complex than officially documented, undermining Ethereum's transparency ethos, and creating unnecessary attack surface. Our work provides the first large-scale empirical foundation for understanding smart contract dependency risks, offering crucial insights for developers, users, and security researchers in the blockchain space.
Blockchain consensus mechanisms must balance security, decentralization, and efficiency while ensuring fair participation. Proof of Team Sprint (PoTS) is a cooperative consensus mechanism designed to address the energy inefficiencies and centralization tendencies of traditional Proof of Work (PoW). Unlike PoW, where rewards disproportionately favor high-performance nodes, PoTS encourages collaboration by forming teams and distributing rewards more equitably among participants. In this study, we evaluate the fairness properties of PoTS by analyzing reward distribution under varying computational power distributions. Through extensive simulations, we compare equal-share allocation and proportional reward allocation, highlighting their impact on decentralization and participation. Our results demonstrate that PoTS significantly reduces reward disparity between high-performance and low-performance nodes, fostering a more inclusive ecosystem. Additionally, we observe that as team sizes increase, the influence of individual computational power is mitigated, allowing lower-performance nodes to contribute meaningfully. Moreover, our findings reveal that the marginal benefit of investing in extremely high-performance hardware diminishes, which discourages centralization and aligns incentives toward sustainable participation. We also discuss the economic implications of PoTS, particularly its potential to reshape blockchain mining strategies by balancing fairness with computational efficiency. These insights contribute to the broader discussion on blockchain fairness and provide a foundation for further research into cooperative consensus mechanisms.
This paper presents an empirical evaluation of the Proof of Team Sprint (PoTS) consensus algorithm, focusing on reward fairness, energy efficiency, system stability, and scalability. We conducted large-scale simulations comparing PoTS with conventional Proof of Work (PoW) across various team sizes and computational conditions. In PoW, the highest-performance node ranked first in all 100 trials, demonstrating extreme centralization. In contrast, PoTS reduced this dominance: the same node ranked first only 54 times, indicating fairer reward distribution. Statistical analysis showed that as team size increased, skewness and kurtosis of reward distributions decreased, confirming improved equity among participants. PoTS also demonstrated significant energy savings. The total active computation time followed a near $1/N$ scaling trend, reducing energy use by up to 64 times when team size was 64, while preserving consensus integrity. Repeated simulations showed stable reward distributions and system performance, affirming PoTS's robustness. Furthermore, the correlation between performance and reward peaked at 0.90 for team size 16, reflecting an optimal balance between fairness and meritocracy. Overall, PoTS offers a cooperative, energy-efficient alternative to PoW, mitigating centralization risks and promoting equitable participation. These findings validate PoTS as a sustainable and fair consensus mechanism suited for future blockchain systems.
To fully utilize the energy on the user side and establish a new integrated energy trading system to realize energy transactions among users, it is imperative to conduct research on the architecture and pricing models of energy trading systems. Based on the study of the application of blockchain technology in energy trading, this paper constructs a peer-to-peer (P2P) energy trading system using blockchain technology, enabling users to conduct energy transactions without the involvement of a third party. A dynamic energy pricing method based on game theory according to the supply–demand ratio (SDR) is proposed in this paper. The pricing model considers user satisfaction and energy supply–demand comprehensively, introduces the concept of game theory, and constructs an optimized microgrid trading model under the P2P information interaction state. This paper also discusses the application scenarios and operation processes of the P2P energy system, and carries out relevant tests. The test results show that the system has high performance and efficiency, and can meet the needs of energy trading. Finally, through simulation examples, it is proved that the pricing model proposed in this paper provides users with significant benefits and technical support, and can serve as a reference for the application of blockchain in P2P energy trading.
Abstract Smart contracts and blockchain technology have revolutionized our transactions and interactions with digital systems, yet their vulnerabilities can lead to devastating consequences such as financial losses, data breaches, and compromised system integrity. Existing detection methods, including static analysis, dynamic analysis, and machine learning-based approaches, have their limitations, such as requiring large amounts of labeled data or being computationally expensive. To address these limitations, we propose a novel approach that leverages a One-Class Variational Autoencoder (VAE) with CodeBERT for data pre-processing to detect vulnerabilities in smart contracts. Our approach achieved a higher F1 score (88.93%) compared to the baselines evaluated, even when labeled data is limited. This paper contributes to the development of effective and efficient vulnerability detection methods, ultimately enhancing the security and reliability of smart contracts and blockchain-based systems. By demonstrating superior performance in imbalanced data scenarios, our method offers a practical solution for real-world applications in blockchain security.
Yuanzhu Zhan, Andy C.L. Yeung, Kim Hua Tan, Yu Xiong · 6 authors
ABSTRACT Despite a growing focus on blockchain adoption within operations and supply chains, these initiatives exhibit an exceedingly high failure rate, with many failing to achieve sustainable success. Why are enterprise blockchain adoptions highly susceptible to failure? In our inductive case study of five technology providers offering blockchain services for operations and supply chains, we examine how founders' power (i.e., expert, prestige, and ownership) shapes management behaviors that build blockchain perceptions and influence stakeholders toward strategic adoption. Our findings reveal that high‐performing cases actively seek inspiration beyond the blockchain ecosystem for service designs and adopt a centralized decision‐making approach where key strategic decisions are made internally. In contrast, low‐performing cases struggle due to within‐blockchain exploration and decentralized decision‐making, leading to slower implementation and limited scalability. Furthermore, we find that founders' behaviors in blockchain adoption are embraced by firm members through performance evaluation and interactions, shaping organizational practices and culture, ultimately determining the success or failure of blockchain technology providers. This study extends previous research at the intersection of founders' power and blockchain literature by developing propositions about how different sources of founders' power lead to distinct management behaviors, influencing the success or failure of blockchain adoption outcomes.
Selvi Amanda, Arif Laksono, Natania Nurafni, Osberth Sinaga
This study examines the digital transformation of the creative arts industry, focusing on how technologies such as Augmented Reality (AR), Virtual Reality (VR), and Non-Fungible Tokens (NFT) have altered the creation and marketing of artworks. Digital platforms have expanded market access for artists, opened new opportunities for more efficient business models with lower costs, and created greater possibilities for cross-disciplinary collaboration. In this context, digital technologies allow artists to reach a global audience and sell their works in more innovative and interactive formats, increasing audience participation in the creative process. However, these developments also face significant challenges, such as increasingly intense competition, rapidly shifting trends, and limitations in resources to optimally implement these technologies. Furthermore, while there are significant opportunities in the global market, some artists in Indonesia still face difficulties in accessing and utilizing the latest technologies. With the creative economy contributing 7.16% to Indonesia's GDP in 2018, this study aims to provide an overview of the dynamics of the arts industry in the digital era and explore potential strategies to support the development of a more adaptable creative ecosystem. These strategies include digital training, strengthening cross-sector collaboration, and enhancing digital infrastructure to facilitate the future growth of the creative arts industry.
Highlights of the IssueKris Carlson, Publisher and Editor-in-ChiefOur second issue surveys state of the art of large language models (LLMs) with an emphasis on safety and value alignment. Superintelligence StrategyDan Hendrycks, Eric Schmidt, Alexandr WangSeeking Stability in the Competition for AI Advantage: Commentary on Superintelligence StrategyIskander Rehman, Karl P. Mueller, Michael J. Mazarr (RAND Corp.) I recommend the RAND Corp critique by knowledgeable military policy analysts over the Hendrycks et al. article. The RAND article is illuminative, incisive, covers Superintelligence Strategy’s key points, and suggests critical reasoning flaws in their mutually-assured-AI-malfunction (MAIM} policy. Although it is valuable to compare the nuclear and AI revolutions in search of instructive parallels and insights, the differences between the technologies and their respective ecosystems have deep strategic implications. Taking these into account, we have concerns regarding both the practical viability of the MAIM concept as an approach to overcoming instability risks in the AI race and the potential escalatory dangers that could follow from its core prescriptions.— Rehman et al. pg. 1 Surely we’d like to avoid repeating the mutually-assured-destruction (MAD) policy. The MAD policy alone could trigger AGI taking over for their and our security. But we must realize that strategies like MAD and MAIM are considered in the US, its allies, and adversaries. And we must try to understand them in order to avoid them. Highlights of the critique: First, the report refers loosely to an array of actions that states might take to cripple a rival's architecture for developing advanced AI.... [which] assumes that adversary AI programs will have specific facilities that can be readily located and disrupted. However, distributed cloud computing, decentralized training, and algorithmic development increasingly may not require centralized physical locations, making AI systems more resilient to limited attacks…. The following critique argues for distributed autonomous organizations (DAO) as I advocated in Safe Artificial General Intelligence via Distributed Ledger Technology and Provably Safe Artificial General Intelligence via Interactive Proof Systems. A second practical challenge resides in the expectation that each party can accurately assess secretive AI progress by others and gauge when preventive action would be necessary. Contrary to what is averred in the report, it is unlikely that states will have a clear sense of when the moment has arrived to MAIM their opponent…. Third and finally, even a credible MAIM threat might not deter a rival from pursuing superintelligent AI. Halting one's AI development would entail essentially the same costs as being the victim of a MAIM attack — loss of the program. And here’s another critique: MAD did not seek to deter the development of weapons but instead their use, which made the threshold for response vastly simpler (though it could still be problematic in cases such as false or ambiguous warnings of attacks). We would like to hear, or be pointed to, policy alternatives to MAIM that incentivize AGI developers to move toward AGI that can be proven to benefit all of humanity. Humanity’s Last Exam (HLE)Long Phan, Alice Gatti, Ziwen Han, and Nathaniel Li are first-listed members of the Organizing Team, and have hundreds of co-author/collaborators. This very large-scale collaborative effort has an ambitious title. The authors note:[LLM] benchmarks are not keeping pace in difficulty [with LLM capabilities]: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce HUMANITY’S LAST EXAM (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects. I do not find any mention of the terms, ‘training set leakage into test set data’ or ‘test set contamination.’ But those issues aside, it seems to be the toughest test set yet – at least as of this writing (16 March 2025) before the LLMs learn the answers and can regurgitate them and reasonably close variants, at which point there will need to be a fresh ‘last exam.’ Kudos to the organizing authors. It’s interesting that frontier LLMs performed dramatically poorer on HLE than on previous benchmark tests, which is a tribute to the originality of the questions. Pathways to Short Transformational AI TimelinesZershaaneh Qureshi We excerpt here a chapter from the complete text. To understand this chapter note that the article distinguishes between two types of recursive self-improvement (RSI): • Direct recursive improvement: positive feedback loops which are mediated directly by AI systems. • Indirect recursive improvement: positive feedback loops that are not mediated directly by AI, such as economic feedback loops (driven by reinvestment of capital into AI R&D), scientific feedback loops (driven by advancements in scientific tools and methods) and political feedback loops (driven e.g. by competitive pressures/race dynamics) (pp. 15-16). HyperWrite, edited: The complete article outlines a framework for analyzing different scenarios that could lead to Transformative AI (TAI) within the next 10 years. Key parameters considered are: 1. Compute scaling dynamics (whether progress continues or hits bottlenecks)2. Indirect feedback loop dynamics (whether they can overcome scaling bottlenecks)3. Direct recursive improvement (DRI) timeline (before or after 2035)4. DRI strength (cannot sustain, sustains, or accelerates progress) Seven possible scenarios are: 1. "Straight Path" - Compute scaling continues successfully2. "Rising Tide" - Indirect recursive improvement (IRI) overcomes bottlenecks3. "New Spark" - Moderate direct recursive improvement maintains progress4. "New Engine" - Strong DRI accelerates progress5. "Dual Engine" - Combination of compute scaling and DRI6. "LLM Hybrid" - Hybrid AI systems enable TAI7. "Intelligent Network" - Networks of AI systems enable TAI The author argues that this variety of plausible pathways strengthens the case for short TAI timelines, as TAI could emerge through multiple different mechanisms rather than requiring one specific path to succeed. Please send pointers and commentary on AI timelines and recursive self-improvement to editor@s-rsa.com. The Road to Artificial SuperIntelligence: A Comprehensive Survey of SuperalignmentHyunJin Kim, Xiaoyuan Yi, JinYeong Bak, Jing Yao, Jianxun Lian, Muhua Huang, Shitong Duan, Xing Xie SuperIntelligence will publish reviews and survey articles to help newbies to AGI/SI get up to speed and experienced workers stay up to speed efficiently. The latter can scroll to Section 2.3, Overview of Superalignment Methods and Challenges. Brief analysis of DeepSeek R1 and its implications for Generative AISarah Mercer, Samuel Spillard, Daniel P. Martin For quick and incisive insights into DeepSeek, read this analysis and Dario Amodei’s cool-headed response to all the hype about DeepSeek. Effective Mitigations for Systemic Risks from General-Purpose AIRisto Uuk, Annemieke Brouwer, Tim Schreier, Noemi Dreksler, Valeria Pulignano, Rishi Bommasani A timely article with practical, near-term-implementable AGI risk mitigation suggestions. Examples: • Unlearning techniques: Removing specific harmful capabilities (e.g., pathogen design) from models using unlearning techniques.• Capability restrictions: Restricting risky capabilities of deployed models, such as advanced autonomy (e.g., self-assigning new sub-goals, executing long-horizon tasks) or tool use functionalities (e.g., function calls, web browsing).• Input and output filtering Monitoring for dangerous outputs (e.g., code that appears to be malware or viral genome sequences) and inputs that violate acceptable use policies to ensure models do not engage in harmful behaviour.• Bug bounty programs Clear and user-friendly bug bounty programs that acknowledge and reward individuals for reporting model vulnerabilities and dangerous capabilities. • Safety drills Regularly practising the implementation of an emergency response plan to stress test the organisation’s ability to respond to reasonably foreseeable, fast-moving emergency scenarios. Simulating Influence Dynamics with LLM AgentsMehwish Nasim , Syed Muslim Gilani, Amin Qasmi, and Usman Naseem Analyzing how AGI/SI may influence human opinion is a critical aspect of risk and safety analysis, as is simulation of AGI risk behavior. The methodology the authors present in this short paper has broad application: This paper introduces a simulator to model influence and counter-influence in a wargame setting. Wargames, originally developed for military strategy, have evolved into powerful tools for decision-making across various domains. Today, they are used to model business strategies, assess cybersecurity threats, and simulate geopolitical conflicts. Governments and corporations employ wargames to anticipate economic shifts, supply chain disruptions, and the impact of emerging technologies. In healthcare, they help model pandemic responses, testing different policy interventions before realworld implementation. AI-driven wargames further enhance scenario analysis, enabling rapid adaptation to complex environments. By fostering strategic thinking and resilience, modern wargaming serves as a critical tool for navigating uncertainty in an increasingly interconnected world. Can a Bayesian Oracle Prevent Harm from an Agent? Yoshua Bengio, Matt McDermott, Michael K. Cohen, Nikolay Malkin, Damiano Fornasiere, Pietro Greiner, Younesse Kaddar SI co-founding Editor Steve Omohundro comments: Turning an oracle into an agent may take just a page of code. OK, but that doesn’t mean the methods outlined by Be
Spot Bitcoin Exchange Traded Products (ETPs) are financial instruments enabling Bitcoin to be traded on traditional brokerage platforms, reducing the risks associated with direct Bitcoin exposure while addressing fraud and market manipulation concerns. This study examines the adoption of Spot Bitcoin ETPs, emphasizing the roles of financial and digital literacy, market dynamics, and regulatory frameworks in influencing individual investor behavior. Based on a survey of 428 U.S. respondents, financial literacy and early adopter traits were found to significantly enhance adoption likelihood (β = 0.458, p < 0.001). Government factors, such as compliance guidelines and tax policies, improved investor confidence and adoption rates (β = 0.409, p < 0.001). Market factors, including volatility and sentiment, played a notable yet secondary role (β = 0.34, p < 0.001). Institutional investment mediated the effects of regulatory and market dynamics on individual adoption, legitimizing Spot Bitcoin ETPs and fostering trust (β = 0.298, p < 0.001). The findings emphasize the need for clear regulations, robust disclosure requirements, and investor education to enhance adoption. Policymakers should focus on regulatory transparency to build investor confidence, while financial institutions can advance adoption by promoting financial and digital literacy. This study contributes to understanding how individual, market, and regulatory factors collectively drive the integration of regulated cryptocurrency products into mainstream finance.
Digital assets, including cryptocurrencies, non-fungible tokens, and digital tokens, are reshaping global economic systems by promoting financial inclusion, decentralized control, and technological innovation. However, their intangible, decentralized, and cross-border nature presents significant challenges to existing legal frameworks, leading to legal uncertainty, regulatory fragmentation, and enforcement difficulties. This study systematically analyzes the definitions, characteristics, and classifications of digital assets while examining the regulatory approaches of the United States, the European Union, the BRICS nations, and Singapore. By identifying key challenges such as security vulnerabilities, cross-border complexities, and market volatility, this paper proposes solutions including harmonized frameworks, enhanced consumer protections, technological innovations, and international collaboration. The findings emphasize the importance of a balanced regulatory approach that fosters innovation, ensures consumer protection, and supports market stability in the evolving digital asset ecosystem.
We discover a novel flight-to-safety (FTS) effect from cryptocurrency markets to stock markets, triggered by a series of hacking attacks on cryptocurrency exchanges. This phenomenon is driven by heightened uncertainty, which increases investors’ risk awareness and prompts asset reallocation in favour of safer stock markets over riskier cryptocurrency markets. We conduct an extensive global examination of this effect across 39 countries and confirm this novelty. This effect is amplified by frequent attacks when investors’ risk awareness is strengthened. Notably, social media sentiment surrounding these attacks serves as both a timely warning indicator for upcoming hacking events and a measure of the FTS pressure following such attacks. We conclude that the collapsed investor confidence and increased risk aversion are the primary cause of such an effect. We further substantiate the FTS hypothesis by offering evidence of significant abnormal fund flows into US mutual funds following these hacking events. As such, through the lens of cyber attacks, we document how a shock in cryptocurrency markets is transmitted into stock markets via investors’ FTS behaviour. • We discover a flight-to-safety (FTS) effect from cryptocurrency to stock markets. • The FTS effect is amplified by more frequent cyberattacks. • Social media sentiment can warn upcoming hacking events and measure FTS pressure. • The FTS is driven by collapsing investor confidence and heightened risk aversion. • Evidence from US mutual fund supports our novel FTS effect.
Erveton P. Pinto, Marcelo A. Pires, Rone N. da Silva, Sı́lvio M. Duarte Queirós
We report the first application of a tailored Complexity-Entropy Plane designed for binary sequences and structures. We do so by considering the daily up/down price fluctuations of the largest cryptocurrencies in terms of capitalization (stable-coins excluded) that are worth $circa \,\, 90 \%$ of the total crypto market capitalization. With that, we focus on the basic elements of price motion that compare with the random walk backbone features associated with mathematical properties of the Efficient Market Hypothesis. From the location of each crypto on the Binary Complexity-Plane (BiCEP) we define an inefficiency score, $\mathcal I$, and rank them accordingly. The results based on the BiCEP analysis, which we substantiate with statistical testing, indicate that only Shiba Inu (SHIB) is significantly inefficient, whereas the largest stake of crypto trading is reckoned to operate in close-to-efficient conditions. Generically, our $\mathcal I$-based ranking hints the design and consensus architecture of a crypto is at least as relevant to efficiency as the features that are usually taken into account in the appraisal of the efficiency of financial instruments, namely canonical fiat money. Lastly, this set of results supports the validity of the binary complexity analysis.