Monero, the leading privacy-focused cryptocurrency, relies on a peer-to-peer (P2P) network to propagate transactions and blocks. Growing evidence suggests that non-standard nodes exist in the network, posing as honest nodes but are perhaps intended for monitoring the network and spying on other nodes. However, our understanding of the detection and analysis of anomalous peer behavior remains limited. This paper presents a first comprehensive study of anomalous behavior in Monero's P2P network. To this end, we collected and analyzed over 240 hours of network traffic captured from five distinct vantage points worldwide. We further present a formal framework which allows us to analytically define and classify anomalous patterns in P2P cryptocurrency networks. Our detection methodology, implemented as an offline analysis, provides a foundation for real-time monitoring systems. Our analysis reveals the presence of non-standard peers in the network where approximately 14.74% (13.19%) of (reachable) peers in the network exhibit non-standard behavior. These peers exhibit distinct behavioral patterns that might suggest multiple concurrent attacks, pointing to substantial shortcomings in Monero's privacy guarantees and network decentralization. To support reproducibility and enable network operators to protect themselves, we release our examination pipeline to identify and block suspicious peers based on newly captured network traffic.
Recent advances in Large Language Models (LLMs) have shown remarkable capabilities in financial reasoning and market understanding. Multi-agent LLM frameworks such as TradingAgent and FINMEM augment these models to long-horizon investment tasks by leveraging fundamental and sentiment-based inputs for strategic decision-making. However, these approaches are ill-suited for the high-speed, precision-critical demands of High-Frequency Trading (HFT). HFT typically requires rapid, risk-aware decisions driven by structured, short-horizon signals, such as technical indicators, chart patterns, and trend features. These signals stand in sharp contrast to the long-horizon, text-driven reasoning that characterizes most existing LLM-based systems in finance. To bridge this gap, we introduce QuantHarness, the first multi-agent LLM framework explicitly designed for high-frequency algorithmic trading. The system decomposes trading into four specialized agents--Indicator, Pattern, Trend, and Risk--each equipped with domain-specific tools and structured reasoning capabilities to capture distinct aspects of market dynamics over short temporal windows. Extensive experiments across nine financial instruments, including Bitcoin and Nasdaq futures, demonstrate that QuantHarness consistently outperforms baseline methods, achieving higher predictive accuracy at both 1-hour and 4-hour trading intervals across multiple evaluation metrics. Our findings suggest that coupling structured trading signals with LLM-based reasoning provides a viable path for traceable, real-time decision systems in high-frequency financial markets.
Lei Yu, Jingyuan Zhang, Xin Wang, Jiajia Ma · 6 authors
Smart contracts automate the management of high-value assets, where vulnerabilities can lead to catastrophic financial losses. This challenge is amplified in Large Language Models (LLMs) by two interconnected failures: they operate as unauditable "black boxes" lacking a transparent reasoning process, and consequently, generate code riddled with critical security vulnerabilities. To address both issues, we propose SmartCoder-R1 (based on Qwen2.5-Coder-7B), a novel framework for secure and explainable smart contract generation. It begins with Continual Pre-training (CPT) to specialize the model. We then apply Long Chain-of-Thought Supervised Fine-Tuning (L-CoT SFT) on 7,998 expert-validated reasoning-and-code samples to train the model to emulate human security analysis. Finally, to directly mitigate vulnerabilities, we employ Security-Aware Group Relative Policy Optimization (S-GRPO), a reinforcement learning phase that refines the generation policy by optimizing a weighted reward signal for compilation success, security compliance, and format correctness. Evaluated against 17 baselines on a benchmark of 756 real-world functions, SmartCoder-R1 establishes a new state of the art, achieving top performance across five key metrics: a ComPass of 87.70%, a VulRate of 8.60%, a SafeAval of 80.16%, a FuncRate of 53.84%, and a FullRate of 50.53%. This FullRate marks a 45.79% relative improvement over the strongest baseline, DeepSeek-R1. Crucially, its generated reasoning also excels in human evaluations, achieving high-quality ratings for Functionality (82.7%), Security (85.3%), and Clarity (90.7%).
NIDIA HELENA SANDOVAL MESA, Roberto Albeiro Pava DÃaz
The drug supply chain is a critical process that involves multiple stakeholders, including pharmaceutical laboratories, logistics companies, pharmacies and patients. Distributed ledger technology such as blockchain can improve security and transparency in the drug supply chain, providing a decentralized and secure record of all transactions made in the process. Also, it allows defining mechanisms that help mitigate the falsification and manipulation of medicines, facilitating public health surveillance and providing a reliable environment for the acquisition and consumption of medicines. On the other hand, the implementation of blockchain tends to guarantee the security and privacy of the data associated with the actors in the supply chain, but one of its greatest challenges faces the limitations of interoperability, that is, the ability for different Blockchains can communicate with each other and share information. Finally, this article presents a bibliometric review of indexed publications in the period of time between 2018 and 2023, characterizing the line of publications in this area, with indicators associated with authors, publication sources and trends in keywords and topics associated.
Adzin Zhalifunnas, Alexander Agung Santoso Gunawan, Andien Dwi Novika
Indonesia's fractured KYC processes have resulted in inefficient verification steps, high costs, and poor user experience, which remains prohibitive to financial inclusion; even amidst tremendous digital growth in the country (93M internet users and a 300% payment increase from 2018 to 2019). This paper's proposed Future KYC (FKYC) is a blockchain framework that leverages Base Blockchain (Layer 2 Ethereum) for on-chain verification hashes and encrypted off-chain KYC data storage. Using an incremental development process, FKYC is developed as a hybrid framework utilizing the combination of multiple actors, for KYC that meets this standard's requirements and allows for verification across institutions via roles-based access control (individuals, institutions, or regulators) while minimizing or eliminating duplicate or redundant checks. The prototype illustrates cryptographic separation between the public hashes (on-chain) and private data (off-chain), providing privacy while conforming to Indonesia data localization laws. FKYC is designed to provide an efficient and scalable solution for lowering costs and expanding financial access; however, to realize its potential requires off-chain integration to be completed, institutional workflows to be modified, and policy frameworks for financial institutions to collaboratively develop.
Abstract Background Children with subtotally resected pediatric low-grade glioma (pLGG) often face multiple lines of treatment, which are seldom capable of eliminating the entire tumor. Genomics-based biomarkers are often used to select targeted therapies, but this paradigm only yields overall response rates of ∼50% optimally. Functional precision medicine (FPM), where patient-specific therapeutic efficacy is evaluated by directly treating individuals’ tumor outside their body, can predict individualized drug responses for some cancers, but pLGG is notoriously difficult to maintain outside the body, limiting development of FPM for pLGG. Methods We describe what is, to our knowledge, the first platform that can maintain, treat, and analyze zero-passage pLGG tumor tissue ex vivo , facilitating FPM testing. We engraft pLGG tumors onto a previously validated organotypic brain slice culture (OBSC) platform. After ensuring reproducible engraftment and maintenance of living pLGG tumor tissue on OBSCs, we measured MAPK pathway response to targeted therapies via immunoblotting. We then measured tumor ex vivo response to targeted therapies. Results Each zero-passage pLGG tumor tissue specimen exhibited reproducible growth on the OBSC platform. Western blot demonstrated each BRAF KIAA1549 fusion+ tumor exhibited expected paradoxical MAPK upregulation to dabrafenib treatment. Two of three tumors demonstrated cytotoxicity from trametinib as predicted, whereas one tumor did not. No clinical correlates were measured in this proof-of-concept study, though this mixed response to MEK inhibition may be in line with real-world clinical responses. Conclusion The OBSC platform supports ex vivo maintenance of passage-zero pLGG tumor tissue and enables personalized drug screening to yield a new functional biomarker of pLGG drug response.
With the rapid development of the Internet of Things (IoT), the security and privacy of personal data has received widespread attention. Federated learning models protect personal privacy data through distributed collaborative training models, but it has been shown that personal privacy data can be inferred from uploaded parameters. Federated learning models also face the challenges of privacy leakage risk, computational inefficiency and lack of verifiability. Existing differential privacybased federated learning models and homomorphic encryptionbased federated learning models are unable to balance model accuracy and security. They also face the problem of inefficient computation of client-side local data and high communication overhead. Therefore, in this paper, we propose a federated learning framework (RGC-FL) based on Re-randomizable Garbled Circuits (RGC), which achieves a balance between privacy protection and computational efficiency through dynamic encryption and re-randomization techniques. The model updates are first encrypted at the client using the obfuscated circuits and then uploaded to the server, and then the ciphertext updates are aggregated by the re-randomization technique to avoid the leakage of the original data. Secondly, the client verifies the correctness of the server’s aggregation results by zero-knowledge proof. Finally based on DDH assumption and Kilian randomization technique to defend against hybrid attacks in dynamic input scenarios. We experimentally show that the model accuracy of RGC-FL on MNIST and CIFAR-10 datasets is 97.3% and 83.9%, respectively, which is close to plaintext federated learning and significantly outperforms the Differential Privacy (DP-FL) and Fully Homomorphic Encryption scheme (FHE-FL). In terms of efficiency, the training time for a single round is only 32% of that of FHE-FL (12.4 sec vs. 38.7 sec), and the communication overhead is reduced by $80 \%(5.2 \mathrm{MB}$ vs. 25.6 MB). This paper provides an efficient and secure solution for federated learning in highly privacy-sensitive domains and promotes the wide application of AI under compliance requirements.
Sudarsono Sudarsono, Adhi Surya Harahap, John Sihar Manurung
This study reviews recent international literature (2024–2025) on the application of blockchain in financial management. The findings indicate that blockchain contributes significantly across various areas, including supply chain finance (SCF), financial reporting, working capital management, asset tokenization, and decentralized finance (DeFi). In SCF, blockchain improves transaction traceability, reduces information asymmetry, and lowers the risk of supply chain disruptions. In financial reporting, blockchain-based e-invoicing enhances transparency, accountability, and reduces the cost of equity by improving investor confidence. The integration of blockchain into working capital management enables real-time synchronization of financial and operational data, thereby strengthening decision-making and liquidity optimization. Meanwhile, asset tokenization creates opportunities for democratizing investment access and diversifying funding sources. DeFi, while offering innovative financing alternatives and disrupting traditional financial intermediaries, remains strongly influenced by global macroeconomic dynamics and regulatory frameworks. Furthermore, blockchain enhances cross-border trade efficiency by streamlining document verification, reducing transaction delays, and fostering trust among international trading partners. Despite these substantial benefits, blockchain adoption continues to face challenges, such as regulatory uncertainty, cybersecurity risks, scalability limitations, and digital asset volatility. Overall, the synthesis of literature highlights blockchain not only as a technological innovation but also as a strategic pillar in modern financial governance. This study also suggests that future research should examine the integration of blockchain with global regulatory frameworks, green finance initiatives, and sustainable financial practices to ensure both scalability and long-term resilience.
This paper presents an enhanced blockchain-based salary certification system that leverages FISCO BCOS, Node.js, and MySQL to address critical challenges in traditional salary management systems. The system introduces more substantial advancements in blockchain topology design, performance optimization, and security mechanisms. It features a hierarchical consortium blockchain architecture that balances decentralization with operational efficiency, ensuring robust security and transparency. The system employs advanced techniques such as dynamic validator set management, batch transaction processing, and parallel validation pipelines to achieve higher transaction throughput and reduced latency. Additionally, it integrates zero-knowledge proofs and a multi-active data center architecture for enhanced data protection and disaster recovery. Performance evaluation demonstrates significant improvements in transaction throughput and latency, with the system achieving high success rates in both open and query type tests. The modular design allows for flexible deployment across various organizational structures, providing a comprehensive solution for secure, transparent, and efficient salary data management.
This study examines the impact of cryptocurrency ownership on corporate volatility, focusing on external financial conditions, internal financial conditions, and liquidity crises. The research utilizes secondary data from publicly traded companies in the United States listed in the Refinitiv database for the period 2018-2023. To enhance the validity of the results, a matching procedure was implemented, in which each cryptocurrency-owning company was paired with a similar non-cryptocurrency-owning company to create a balanced control group. The analysis employed panel data regression on 384 publicly traded companies in the U.S. The findings indicate that the ratio of cryptocurrency ownership has a significant positive effect on corporate volatility. Additionally, liquidity levels also have a significant positive impact on the volatility of companies holding cryptocurrencies, suggesting that liquidity crises amplify the effect of cryptocurrency ownership fluctuations on corporate volatility. Internal financial conditions, measured by Return on Assets (ROA), exhibit a significant negative effect on the volatility of companies holding cryptocurrencies, implying that strong internal financial health mitigates the impact of cryptocurrency ownership fluctuations on volatility. Conversely, external factors such as company Beta do not influence increased volatility, which contrasts with the expectation that external factors would amplify the effect of cryptocurrency ownership fluctuations on corporate volatility. This study offers important implications for financial managers and regulators in designing risk mitigation strategies against digital asset price fluctuations.
Traceable ring signatures (TRSs) allow a signer to create a signature that maintains anonymity while enabling traceability if needed. It merges the characteristics of traditional ring signatures with the ability to trace signers, making it ideal for applications that demand both confidentiality and accountability. In a TRS scheme, a ring of potential signers generates a signature on a message without disclosing the actual signer’s identity. However, the identity can be traced if the signer uses the same tag for multiple signatures. This paper introduces a novel formal construction of TRS under universally composable (UC) security. We integrate verifiable random functions (VRFs) and zero-knowledge proofs for membership, employing Pedersen commitments. Our signature schemes maintain a logarithmic size while preserving the UC security guarantees. Additionally, we explore the potential to extend the property of one-time anonymity in TRS to K-time anonymity.
Decentralized Federated Learning (DFL) enables collaborative model training without a central server, but it remains vulnerable to privacy leakage because shared model updates can expose sensitive information through inversion, reconstruction, and membership inference attacks. Differential Privacy (DP) provides formal safeguards, yet existing DP-enabled DFL methods operate as black-boxes that cannot track cumulative noise added across clients and rounds, forcing each participant to inject worst-case perturbations that severely degrade accuracy. We propose PrivateDFL, a new explainable and privacy-preserving framework that addresses this gap by combining a HyperDimensional Computing (HD) model with a transparent DP noise accountant tailored to decentralized learning. HD offers structured, noise-tolerant high-dimensional representations, while the accountant explicitly tracks cumulative perturbations so each client adds only the minimal incremental noise required to satisfy its (epsilon, delta) budget. This yields significantly tighter and more interpretable privacy-utility tradeoffs than prior DP-DFL approaches. Experiments on MNIST (image), ISOLET (speech), and UCI-HAR (wearable sensor) show that PrivateDFL consistently surpasses centralized DP-SGD and Renyi-DP Transformer and deep learning baselines under both IID and non-IID partitions, improving accuracy by up to 24.4% on MNIST, over 80% on ISOLET, and 14.7% on UCI-HAR, while reducing inference latency by up to 76 times and energy consumption by up to 36 times. These results position PrivateDFL as an efficient and trustworthy solution for privacy-sensitive pattern recognition applications such as healthcare, finance, human-activity monitoring, and industrial sensing. Future work will extend the accountant to adversarial participation, heterogeneous privacy budgets, and dynamic topologies.
Mahendran Chinnaiah, A. Kumar Chandra Gupta, Saurabh Srivastava, Ashok Ghimire
At a time when data privacy laws and cyber-attacks are on the rise, Zero-Knowledge Proofs (ZKPs) and Artificial Intelligence (AI) hold the potential of a transformational paradigm of safe (privacy-preserving) machine learning (ML) inferences. In this paper, we present a new architecture that facilitates Zero-Knowledge AI, in which sensitive data inputs and internal model parameters remain unknown during the model inference procedure across distributed ecosystems. The proposed framework can help preserve privacy standards like GDPR and HIPAA, inference accuracies, and scalability of these inferences by utilising mechanisms to observe cryptographic zero-knowledge protocols, as well as federated learning protocols. We describe the construction of ZK-friendly models to apply to neural inference pipelines, efficient zk-SNARK-based model validation, decentralized trusting schemes, and privacy-respecting model auditing. Testing over a variety of healthcare and financial datasets indicates that our Zero-Knowledge AI solution results in high privacy guarantees with limited throughput losses. The work provides a strong basis on how to implement trusted and privacy-first AI systems in the real life and distributed operating environment.
Purpose The purpose of this study is to examine the response of various cryptocurrency market classes to the Federal Reserve’s quantitative easing (QE) announcements. Design/methodology/approach We used the time-varying parameter vector autoregressive model to analyze the price spillover and interconnectedness between the US market assets/indices, and cryptocurrencies, explicitly focusing on Layer 1 tokens, DeFi tokens, Exchange-Based Tokens, Smart Contracts and Stablecoins. Findings The findings reveal that most cryptocurrency classes exhibit notable price spillovers from US assets/indices. However, the analysis suggests that only high-return tokens show significant responses during the Federal Reserve QE announcements and receive price spillover. In contrast, leading tokens remain unaffected by such spillovers, which suggests that during QE periods investors tend to seek higher returns and are more likely to invest in high return assets. It reflects a preference for assets that could offer greater returns when monetary policy is easing while more stable cryptocurrencies are less impacted by policy changes, implying that investors may adjust their strategies by shifting toward high return cryptocurrencies during periods of QE to capitalize on these market movements. Research limitations/implications This study focuses on a limited selection of cryptocurrency classes and specific QE events, which may not fully capture all market dynamics or incorporate newer cryptocurrency assets due to insufficient historical data. Another key limitation of this study is the inclusion of the COVID-19 pandemic period, which represents an extraordinary macroeconomic environment that may not be representative of normal market conditions. Future research could explore a broader range of crypto assets over an extended timeframe and sub-period analysis excluding the pandemic years or incorporate regime-switching models to account for structural breaks. Practical implications Understanding the response of different cryptocurrency assets to QE announcements can significantly assist investors in making informed decisions regarding asset allocation during these periods, guiding them in identifying the most profitable cryptocurrencies as alternatives to traditional assets. Originality/value In contrast to prior research, which primarily concentrates on the impact of QE on financial markets or confines its analysis to major and prominent crypto assets, this study provides a comprehensive examination of how specific cryptocurrency classes respond to macroeconomic policy changes.
Smart contracts have emerged as a transformative force in contract law, leveraging blockchain technology to automate transactions and reduce reliance on human intermediaries. However, their widespread adoption is hindered by significant legal challenges, particularly in determining liability for transaction failures. This Note examines the accountability problems inherent in smart contracts, focusing on the critical role of oracles—third-party entities that feed external data into blockchain-based agreements. While existing scholarship explores the theoretical foundations and potential applications of smart contracts, this Note shifts focus to liability allocation and proposes a novel framework: default oracle liability. Under this proposal, oracles bear primary responsibility for transaction errors arising from inaccurate data sourcing or validation failures. If oracles demonstrate that they functioned correctly, liability shifts to smart contract developers, who are responsible for ensuring secure and error-free code. By clarifying accountability, this framework incentivizes higher standards for data accuracy and software integrity, ultimately fostering a more reliable and legally-viable environment for smart contracts to operate.
Bitcoin's limited scripting capabilities and lack of native interoperability mechanisms have constrained its integration into the broader blockchain ecosystem, especially decentralized finance (DeFi) and multi-chain applications. This paper presents a comprehensive taxonomy of Bitcoin cross-chain bridge protocols, systematically analyzing their trust assumptions, performance characteristics, and applicability to the Artificial Intelligence of Things (AIoT) scenarios. We categorize bridge designs into three main types: naive token swapping, pegged-asset bridges, and arbitrary-message bridges. Each category is evaluated across key metrics such as trust model, latency, capital efficiency, and DeFi composability. Emerging innovations like BitVM and recursive sidechains are highlighted for their potential to enable secure, scalable, and programmable Bitcoin interoperability. Furthermore, we explore practical use cases of cross-chain bridges in AIoT applications, including decentralized energy trading, healthcare data integration, and supply chain automation. This taxonomy provides a foundational framework for researchers and practitioners seeking to design secure and efficient cross-chain infrastructures in AIoT systems.
There are a variety of use cases and benefits to using blockchain technology in either a permissionless or permissioned context. Proof-of-work or proof-of-stake protocols are frequently utilized by public blockchains, while Raft or Solo consensus methods are more commonly utilized by private blockchains. Private blockchains are more suited to trustworthy networks. Several blockchain tools, frameworks, and wallets are discussed in this chapter. Some of the tools and wallets that are discussed are MetaMask, Ethereum, Ganache, Hyperledger Fabric, Alchemy, and Solidity. Furthermore, it illustrates how various users might put them to use in order to construct public as well as private blockchain networks; this is demonstrated.
UAVs (Unmanned Aerial Vehicles) enhance sustainability by enabling precise and efficient environmental monitoring with minimal ecological disruption. This study looks at how integrating artificial intelligence (AI) with blockchain technology can increase operational independence, scalability, and security of UAV systems. Blockchain technology is all about being decentralised and unchangeable, which means it keeps UAV operations secure and reliable by ensuring that data remains intact and preventing any unauthorised changes. Moreover, this research in the importance of blockchain consensus algorithms -Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Delegated Proof of Stake (DPoS), specifically for UAV applications. AI integration improves the optimization of processes on the fly and helps to make smarter decisions, leading to plausible improvements in transaction validation and overall network efficiency. The experimental results show how effective the AI-augmented PoS and DPoS algorithms are, highlighting that they are a great fit for scalable and self-sufficient UAV applications.
Permasalahan manipulasi data dalam sistem presensi konvensional, seperti presensi fiktif dan pengubahan waktu kehadiran, masih sering terjadi di berbagai organisasi, termasuk Asosiasi Planters Muda Indonesia (APMI). Sistem manual tidak dapat menjamin keakuratan maupun keamanan data. Oleh karena itu, dibutuhkan pendekatan baru yang lebih andal dalam autentikasi dan pencatatan kehadiran. Autentikasi biometrik berbasis face recognition menjadi alternatif menjanjikan karena mampu mengenali identitas secara otomatis. Namun, untuk menjaga integritas data, diperlukan teknologi blockchain guna mencatat informasi secara aman dan tidak dapat dimodifikasi. Penelitian ini bertujuan mengembangkan sistem presensi berbasis face recognition yang terintegrasi dengan blockchain guna menghadirkan solusi yang praktis serta meningkatkan keamanan dan privasi data presensi. Sistem dibangun menggunakan Python dengan library utama face_recognition dan OpenCV untuk deteksi wajah, serta Flask sebagai backend web. Data presensi disimpan pada database lokal (XAMPP) dan dicatat dalam bentuk hash ke blockchain lokal (Ganache) berbasis Ethereum melalui smart contract dan Web3. Pengujian dilakukan terhadap aspek fungsionalitas, akurasi pengenalan wajah pada berbagai kondisi, serta validasi hash untuk mendeteksi manipulasi data. Hasil menunjukkan sistem mampu mengenali wajah secara akurat, mencatat hash secara permanen di blockchain, dan mendeteksi perubahan data di database lokal. Penelitian ini membuktikan bahwa integrasi face recognition dan blockchain memberikan solusi presensi yang praktis serta meningkatkan keamanan dan privasi data.
Jamil Abedalrahim Jamil Alsayaydeh, Mohd Faizal Yusof, Nor Adnan Yahaya, Viacheslav Kovtun · 5 authors
In today's digital world, cryptocurrencies like Bitcoin can secure transactions without banks. However, the rise of quantum computing poses significant threats to their security, as traditional cryptographic methods may be easily compromised. In addition, the existing algorithms face difficulties like slow transaction speeds, interoperability issues between different cryptocurrencies, and privacy concerns. Hence, Quantum Crypto Guard for Secure Transactions (QCG-ST), a novel blockchain framework, is introduced, offering enhanced security and efficiency for cryptocurrency transactions. The QCG-ST employs lattice-based cryptography to provide robust protection against quantum threats and incorporates a new consensus mechanism to increase the transaction speed and reduce energy consumption. The QCG-ST system uses lattice-based encryption that is based on the Ring Learning With Errors (Ring-LWE) issue to protect itself from quantum assaults. It uses sharding, a Proof-of-Stake (PoS) consensus method, and a threshold signature scheme (TSS) to make the system more scalable and use less energy. Zero-knowledge proofs (ZKPs) are used to check transactions without giving out private information. We offer a cross-chain atomic swap protocol that uses hashed time-lock contracts to make sure that it works on all platforms. Blockchain transaction data utilized in testing originated from the Bitcoin Historical Dataset available on Kaggle, and quantum resistance has been assessed using the Qiskit Aer simulator. It evaluated the framework's performance to that of traditional methods like Payment Channel-Lightning Network (PC-LN), Variational Quantum Eigensolver (VQE), and Cross-Chain Transaction with Hyperledger (CCT-H). Results show that QCG-ST does far better than traditional systems in terms of transaction success rate (up to 98.5%), speed, energy efficiency, latency, and throughput, especially when tested in a quantum-simulated environment. This study completes in an essential vacuum in blockchain technology by suggesting a strong, quantum-resistant, privacy-protecting architecture that can handle the problems that could arise up in decentralized digital banking in the future.
Carlo Beltracchi, Ahmed Elmaraghy, Pierpaolo Ruttico, S. Maccagnan
This contribution proposes a way to broaden access to computational design by combining: (1) an agentic workflow where AI micro-agents translate natural-language prompts into executable, self-verified parametric graphs; (2) a data-driven economy in which each reuse of logic triggers automatic micropayments; and (3) a decentralised network that stores versions, rights and transactions on-chain. Assessor, provider and validator agents assemble, check and publish sub-graphs serialised as semi-fungible tokens; a blockchain ledger tracks lineage and redistributes royalties. The platform merges open-source principles with Web3 incentives: newcomers gain ready-to-use solutions, experienced designers monetise know-how, and the community governs parameters via on-chain voting. Supported by robotic 3D-printing partners, the framework targets XR adoption: tokenised parametric graphs power virtual configurators for (1:1) design alternatives; users and curators vary parameters within constraints and record reuse on-chain, supporting an inclusive creator economy across the generative process.
Global supply chains today operate in an environment marked by unprecedented complexity, interdependence, and susceptibility to both market and operational disruptions.Traditional supply chain management systems, which rely heavily on centralized coordination and rigid contractual structures, often fall short in providing the transparency, adaptability, and resilience demanded by modern logistics ecosystems.This paper introduces a novel, decentralized framework that synergistically combines blockchain technology, multi-agent reinforcement learning (MARL), and dynamic smart contract optimization to achieve autonomous and adaptive supply chain operations.In the proposed architecture, each stakeholder in the supply chain-ranging from raw material suppliers to end retailers-is modeled as an intelligent agent capable of perceiving its environment, learning from historical outcomes, and making optimized decisions in real time.The agents interact and transact over a permissioned blockchain network, ensuring transparency, data immutability, and trustless collaboration.Smart contracts govern the terms of these interactions and are designed to be dynamically adaptable, adjusting key contractual parameters such as pricing, delivery schedules, and penalties based on real-time environmental inputs and the evolving strategies of agents.By integrating MARL into the decision-making loop, the system continuously improves coordination and performance across the supply chain.Simulation results across a multi-tier supply network demonstrate that this framework significantly outperforms traditional models, achieving: ï‚· Up to 34% improvement in cost efficiency, ï‚· 47% reduction in contract breaches, and ï‚· Faster convergence of agent policies leading to more robust and scalable autonomous operations.The results underscore the potential of blockchain-enabled autonomous systems in redefining supply chain resilience, agility, and operational intelligence.
The specification of state machine replication (SMR) has no requirement on the final total order of commands. In blockchains based on SMR, however, order matters, since different orders could provide their clients with different financial rewards. Ordered consensus augments the specification of SMR to include specific guarantees on such order, with a focus on limiting the influence of Byzantine nodes. Real-world ordering manipulations, however, can and do happen even without Byzantine replicas, typically because of factors, such as faster networks or closer proximity to the blockchain infrastructure, that give some clients an unfair advantage. To address this challenge, this paper proceeds to extend ordered consensus by requiring it to also support equal opportunity, a concrete notion of fairness, widely adopted in social sciences. Informally, equal opportunity requires that two candidates who, according to a set of criteria deemed to be relevant, are equally qualified for a position (in our case, a specific slot in the SMR total order), should have an equal chance of landing it. We show how randomness can be leveraged to keep bias in check, and, to this end, introduce the secret random oracle (SRO), a system component that generates randomness in a fault-tolerant manner. We describe two SRO designs based, respectively, on trusted hardware and threshold verifiable random functions, and instantiate them in Bercow, a new ordered consensus protocol that, by approximating equal opportunity up to within a configurable factor, can effectively mitigate well-known ordering attacks in SMR-based blockchains.