Blockchain Papers

Ebuka Chinaechetam Nkoro, Love Allen Chijioke Ahakonye, Dong‐Seong Kim

Smart Contracts (SCs), which are the backbone of automated transactions and digital assets within the Metaverse, ironically suffer from their own share of security vulnerabilities. While detecting these SC vulnerabilities using Artificial Intelligence (AI) and Deep Neural Networks (DNNs) has demonstrated remarkable performance and gained wide adoption, a critical limitation remains: the lack of explainability in these black box models. To facilitate meaningful progress in this field, our study addresses this gap by introducing a model-agnostic explanation framework that is both visual and quantitative, with human stakeholders actively involved to govern, verify, and interpret SC model predictions. The explainable SC outputs can be utilized for reward issuance and digital assets governance in the Metaverse. The effectiveness of our proposed Explainable AI (XAI) approach is validated using benchmark datasets, BCCC SCsVul 2024 and BCCC SCsVul 2023, comprising Ethereum SC entropy source codes, where it achieves an optimal detection accuracy of 97.13% alongside comprehensive explainability. To the best of our knowledge, this represents the first attempt at making Ethereum SC vulnerability detection within the Metaverse explainable, offering a valuable foundation for blockchain researchers, Metaverse security experts, and practitioners seeking verifiable, trustworthy, and auditable Ethereum SC vulnerability detection.

Muhammad Ilman Abidin, Ahmad M. Ramli, Laina Rafianti, Gautam Kumar Jha

Investment in Non-Fungible Tokens (NFTs) is rapidly emerging in Indonesia, presenting both opportunities and challenges for the digital creative industry. As unique crypto assets, NFTs enable new ways to own and trade digital and physical goods, but current regulations, including the Commodity Futures Trading Law and Bappebti guidelines, do not fully address these transactions, creating legal gaps and increasing risks of fraud, money laundering, and market manipulation. Despite this, NFT communities like the Superlative Secret Society in Bali, supported by the Ministry of Creative Economy, have fostered creativity and economic activity. This study employs a normative juridical and comparative law approach to explore legal theories suitable for protecting NFT investments, finding that frameworks based on Ahmad M. Ramli’s transformative law and Mochtar Kusumaatmadja’s developmental law can ensure legal certainty, security, and fairness. The study concludes that comprehensive legal reforms are essential to safeguard investors and sustain the growth and international competitiveness of Indonesia’s digital creative industry.

Parsa Hedayatnia, Tina Tavakkoli, Hadi Amini, Mohammad Allahbakhsh · 5 authors

Smart contracts concentrate high value assets and complex logic in small, immutable programs, where even minor bugs can cause major losses. Existing taxonomies and tools remain fragmented, organized around symptoms such as reentrancy rather than structural causes. This paper introduces an attack-centric, program-structure taxonomy that unifies Solidity vulnerabilities into eight root-cause families covering control flow, external calls, state integrity, arithmetic safety, environmental dependencies, access control, input validation, and cross-domain protocol assumptions. Each family is illustrated through concise Solidity examples, exploit mechanics, and mitigations, and linked to the detection signals observable by static, dynamic, and learning-based tools. We further cross-map legacy datasets (SmartBugs, SolidiFI) to this taxonomy to reveal label drift and coverage gaps. The taxonomy provides a consistent vocabulary and practical checklist that enable more interpretable detection, reproducible audits, and structured security education for both researchers and practitioners.

Zexu Wang, Jiachi Chen, Zewei Lin, Wenqing Chen · 10 authors

Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability. However, lacking checks on signature usage conditions can lead to repeated verifications, increasing the risk of permission abuse and threatening contract assets. We define this issue as the Signature Replay Vulnerability (SRV). In this paper, we conducted the first empirical study to investigate the causes and characteristics of the SRVs. From 1,419 audit reports across 37 blockchain security companies, we identified 108 with detailed SRV descriptions and classified five types of SRVs. To detect these vulnerabilities automatically, we designed LASiR, which utilizes the general semantic understanding ability of Large Language Models (LLMs) to assist in the static taint analysis of the signature state and identify the signature reuse behavior. It also employs path reachability verification via symbolic execution to ensure effective and reliable detection. To evaluate the performance of LASiR, we conducted large-scale experiments on 15,383 contracts involving signature verification, selected from the initial dataset of 918,964 contracts across four blockchains: Ethereum, Binance Smart Chain, Polygon, and Arbitrum. The results indicate that SRVs are widespread, with affected contracts holding $4.76 million in active assets. Among these, 19.63% of contracts that use signatures on Ethereum contain SRVs. Furthermore, manual verification demonstrates that LASiR achieves an F1-score of 87.90% for detection. Ablation studies and comparative experiments reveal that the semantic information provided by LLMs aids static taint analysis, significantly enhancing LASiR's detection performance.

Sabin Roman, Francesco Bertolotti

Technological developments and the impact of artificial intelligence (AI) are omnipresent themes and concerns of the present day. Much has been written on these topics but applications of quantitative models to understand the techno-social landscape have been much more limited. We propose a mathematical model that can help understand in a unified manner the patterns underlying technological development and also identify the different regimes in which the technological landscape evolves. First, we develop a model of innovation diffusion between different technologies, the growth of each reinforcing the development of the others. The model has a variable that quantifies the level of development (or innovation, discovery) potential for a given technology. The potential, or market capacity, increases via diffusion from related technologies, reflecting the fact that a technology does not develop in isolation. Hence, the growth of each technology is influenced by how developed its neighboring (related) technologies are. This allows us to reproduce long-term trends seen in computing technology and large language models (LLMs). We then present a three-dimensional system of supply, demand, and investment which shows oscillations (business cycles) emerging if investment is too high into a given technology, product, or market. We finally combine the two models through a common variable and show that if investment or diffusion is too high in the network context, chaotic boom-bust cycles can emerge. These quantitative considerations allow us to reproduce the boom-bust patterns seen in non-fungible token (NFT) transaction data and also have deep implications for the development of AI which we highlight, such as the arrival of a new AI winter.

Md Imran Khan, Ahmad Raza, Abdulrahman Alomair, Abdulaziz S. Al Naim

This study provides a comprehensive contribution to the current understanding of blockchain technology and non-fungible token (NFTs). Blockchain technology is a revolutionary data storage and management tool that records data shared across a network of computers globally, making it safe, transparent, and decentralized. Non-fungible token is a specific type of token built on a blockchain, enabling the authentication of digital assets and safeguarding them against copying or fabrication. The research employed information on 3760 abstract data collected for the period from January 1, 2017, to June 03, 2025. The search criteria for data retrieval are based on the following keywords: “blockchain”, “non-fungible token”, and “token”. The data sources are Scopus and Web of Science. The study highlights the multi-dimensional and evolving discussion around blockchain and NFTs, including elements of technology, security, money, digital rights, and decentralization. The Wordcloud indicates a strong and growing innovation ecosystem, proposing new study avenues in trust mechanisms, smart contract development, and tokenized economies. The correlation graph visualizes that AI, data, finance, and blockchain show their mutual dependence has revolutionized our view of autonomy and governance. The study highlights authors who actively research blockchain and NFTs, as well as correlations between them. China leads the way in this research area and USA leads in terms of citation. The study’s finding informs evidence-based decision-making regarding the regulation and governance of blockchain and NFT technologies. For industry practitioners, the study’s insights can guide the development of innovative applications and solutions leveraging blockchain and NFTs. By examining a vast dataset of academic papers, the inquiry illuminates the key themes, emerging trends, and potential research gaps within this rapidly evolving field.

Mohamed Ezz, Alaa Alaerjan, Ayman Mohamed Mostafa

In today's rapidly advancing healthcare landscape, integrating Artificial Intelligence (AI) and Machine Learning (ML) has the potential to significantly improve patient care and streamline medical processes. The utilization of confidential patient data to train and develop these technologies, however, raises significant concerns regarding authenticity, security, and privacy. In this study, we introduce MediChainAI, a safe and practical framework that allows patients full ownership over their own health data by integrating Self-Sovereign Identity (SSI), Blockchain, and sophisticated cryptography techniques. By clearly outlining the goals and parameters of this access, MediChainAI allows patients to safely and selectively share data with healthcare providers and researchers. While SSI guarantees that patients have ownership of their data, the framework uses Blockchain technology to keep things transparent and secure. Further, MediChainAI makes use of Merkle trees, which provide verified access to subsets of data without jeopardizing the privacy of the whole dataset. The encryption mechanism, which is based on smart contracts, is a distinctive feature of the framework that allows researchers and medical practitioners controlled and secure access to patient data. In order to improve the accuracy and reliability of medical diagnoses and treatment, this strategy makes sure that only confirmed, legitimate data is utilized to train medical models. A significant step toward safer and more personalized healthcare, MediChainAI encourages ethical and patient-focused innovation by effectively resolving essential issues regarding data security and patient privacy.

Diya Mamoria, Harshit Jain, Aswani Kumar Cherukuri

This research proposes a decentralized and cryptographically secure framework to address the most acute issues of privacy, data security, and protection in the ecosystem of medical insurance claim processing. The scope of this study focuses on enabling the management of insurance claims in a transparent, privacy-protecting manner while maintaining the efficiency and trust level needed by the patients, healthcare providers, and insurers. To accomplish this, the proposed system adds blockchain technology to provide an unchangeable, decentralized, and auditable claim transactions ledger which enhances overall claim-related processes and trust among all stakeholders. To protect critical patient information, the framework employs homomorphic encryption a modern form of cryptography to allow authorized insurance providers to perform necessary operations like claim adjudication and reimbursement on encrypted medical records without any decryption during the process. This method significantly reduces the third-party processing privacy risk because patient data can be kept secret even when third-party processing is done. In addition, smart contracts improve automation of the most important procedures in the claim processing pipeline, which decreases manual, operational, and susceptibility towards human blunders or deceitful acts. The integration of these two transformative technologiesblockchain and homomorphic encryption represents the core contribution of this work, enabling the coexistence of transparency and privacy which are usually viewed as competing objectives in traditional systems. As a result, these technologies are expected to foster the creation of a reliable, effective, and privacy safeguarding architecture that could transform the medical claim submission systems paradigm.

Karina Kasztelnik

ABSTRACT This study examines how blockchain transparency and smart-contract automation, paired with anomaly-detection models, support early detection and calibrated deterrence of manipulation in cryptocurrency markets. Although transparent ledgers and rule-based execution raise the likelihood that irregular activity is flagged and investigated, they do not prevent fraud; my emphasis is detection, deterrence, and post-incident support. I analyze a long-horizon Bitcoin panel using rolling z-score screens and Isolation Forest to surface anomalies consistent with manipulative trading. I fix a false-positive budget ex ante and evaluate capacity-aware performance (Precision@k, PR-AUC, lead time), archiving time-stamped evidence bundles for auditability. Alerts cluster around episodes consistent with pump-and-dump behavior, large-holder moves, and event-driven dislocations, improving investigative triage without prevention claims. The framework provides actionable guidance for exchanges and regulators seeking to strengthen market integrity through auditable records and model-based alerts, and I release a human-in-the-loop agentic AI application that automates ingestion, screening, ranking, and auditable export. Data Availability: A replication package including the agentic AI GenApp (Streamlit code), requirements, and input templates (daily data, events, sentiment) is provided in Appendix B. The package reproduces the pipeline exactly as specified in Section IV and writes time-stamped artifacts for audit; it is intended for detection and deterrence workflows and makes no prevention claims. JEL Classifications: G12; G15; G18; G24; G14; G41; H83.

Risfiana Mayangsari, Hidayat Darussalam, Edi Mulyono

This article analyzes the communication patterns that emerge and develop from the integration of Smart Contracts in Islamic financial transactions. The adoption of Smart Contracts marks a fundamental shift from traditional sighat (ijab qabul) to automated and immutable programmed communication on the blockchain. This study finds that the communication patterns involved are divided into three main dimensions: first, formal human-to-contract communication, which is the process of coding and initial agreement of the contract (such as mudharabah or murabahah) where the sighat is represented by explicit digital input; second, fully automated system-to-system communication, where Smart Contracts communicate with external data (oracles) to verify conditions and trigger self-executing transactions; and third, contract-to-ledger communication, which results in transparent and immutable transaction recording on the blockchain. Although promising efficiency and improved Sharia Compliance through the elimination of operational gharar, this programmed communication pattern poses challenges related to contract flexibility and code error risks. Therefore, it is necessary to formulate clear Sharia code standards and digital governance mechanisms recognized by the Sharia Supervisory Board to ensure that this new communication pattern validly and ethically supports maqasid syariah (Sharia objectives).

Jiangquan Fu

The rapid emergence of Stable Coins has completely altered the global landscape of digital finance. The benefits of blockchain technology, along with the typical advantages of a fiat currency, in the form of a stable coin, have had a surreal effect on the world of finance. The paper investigates the evolution, comparative merits and systemic risks of Stable Coins compared to Bitcoin, also uses them for advantages in decentralized finance, liquidity and international transactions. The results clearly show that the Stable Coins have become essential infrastructures of finance because of their low volatility, transaction efficiency but also their sensitivity to such issues as regulation and transparency of reserves. The study of the literature of the BIS, IMF and ECB gives evidence of the fact that stable coins will co-exist with the Central Bank Digital Currencies (CBDC), rather than that they will replace them. The proposed method gives evidence of how a system of collaborative regulation and transparency of reserves can be achieved to facilitate innovations but also protect global economic stability.

Xia, Yu

Secure Multi-party Computation (MPC) considers the problem where a set of mutually distrusting parties want to jointly compute a function over their private inputs, without revealing any extra information about these inputs other than what it can be inferred from the output of the function. This setting is well-motivated, and it has many real-world applications such as auction, voting, etc. MPC can be also seen as a generalization of many natural cryptographic primitives. For example, zero-knowledge (ZK) can be viewed as a special case of two-party secure computation. In ZK, a party, called prover aims to convince a second party, called verifier, that the prover’s private input witness w and a public input statement x belong to a relation R. An important research direction in secure computation is to find the trade-off between the required setup (e.g., the use of the broadcast channel, the use of common reference string (CRS) / public key infrastructure (PKI), the upper bound of the parties that can be corrupted, etc.), and the security guarantees that can be achieved. The setups can be viewed as some general assumptions that the protocol needs to satisfy, and they influence the usability of the protocol in real-world scenarios. In principle, having simpler (or no) setups mean that the protocol is more general and can be more useful in real-world scenarios. At the same time, having simpler setups may lead to weaker security guarantees. Therefore, finding the trade-off between setup and security guarantees is important and meaningful. In this thesis, we target MPC and ZK, and we focus on how to minimize the setup for MPC and ZK while still providing meaningful levels of security. More specifically: Regarding MPC, we focus on the dishonest majority (i.e., the adversary can corrupt all but one party), and we aim at 1) minimizing the use of broadcast channels. 2) studying the MPC with pre-processing when no setup is available. • Informally, a broadcast channel guarantees that when a message is sent, this reaches all the parties, without ambiguity. It also guarantees that if an honest party receives a message from a corrupted party, then it is guaranteed that all the honest parties have received the message. To realize broadcast, parties in the protocol could run the broadcast protocol, which may require many rounds of peer-to-peer communications. An alternative way is to rely on physical or external infrastructure such as blockchain. In both cases, broadcast is expensive, as such, we want to minimize its use. In particular, this thesis presents the following results: – When assuming no setup, we give a complete characterization with respect to the use of broadcast channels, and we obtain the optimal results. – We consider the same problem for the case that we only want to allow the black-box use (i.e., do not have access to the code of the algorithm) of the oblivious transfer protocol. We also give a characterization. • In the standard definition of MPC, the parties’ private inputs are fixed before the start of the protocol. However, there is another type of MPC named MPC with pre-processing, where the protocol can pre-compute some messages without using parties’ inputs, and these messages can accelerate computations in the online phase (i.e., other computations that require parties’ inputs). Since some expensive computations can be pre-computed, the online phase could be more lightweight. Therefore, we want to remove the dependency of the input from as many rounds as possible, so that we can do some pre-processing. In this direction, we explore the protocol with no setup. We provide a compiler that can turn a big class of MPC protocol that may require the inputs already to compute the first round, into a new protocol that needs the inputs only in the last two rounds. We also propose new MPC definitions that capture this delayed-input features. Regarding ZK, we do the following: • In standard single-theorem ZK definition, the security of the ZK protocol is guaranteed to hold only when one proof is issued. In the case where multiple zero-knowledge proofs need to be issued (i.e., to prove multiple NP statements), each new zero-knowledge proof requires a freshly generated setup. In the multi-theorem ZK definition, instead, one setup is sufficient for generating multiple zero-knowledge proofs for multiple instances. We propose a multi-theorem protocol (in the format of a compiler) that follows the Fiat-Shamir paradigm and relies on correlation intractable hash functions. Moreover, our protocol remains zero-knowledge and sound even against adversaries that choose the statement to be proven (and the witness for the case of zero-knowledge) adaptively on the key of the hash function. Prior works could achieve this adaptive security only inefficiently via NP reductions. • ZK protocols are secure only when all setups are correctly generated, but in real-world scenarios, some of the setups may not be correctly generated. For instance, to run a non-interactive zero-knowledge (NIZK) protocol, the setup CRS could be chosen with bias. In this case, the security of the NIZK protocol does not hold anymore. Instead of finding a secure ZK candidate, one alternative solution is to have multiple instantiations of ZK candidates and assume that only for a subset of them the setup is generated correctly. More formally, we consider the case where only a subset of the instances are secure. In more detail, given access to n candidate instantiations of a NIZK for some language, we want to have a construction that itself implements a NIZK for the same language without relying on any additional computational assumptions. We refer to this type of construction as combiner, and the combiner is secure assuming at least t of the given candidates are secure. In this work, we provide three different constructions of robust NIZK combiners and show that combiners are impossible to realize unless the majority of the input candidates are secure.

Ravi Khile, Shravani Karvande, Pradnya Katbane, Saniya Shaikh

Water quality degradation has become a pressing global challenge due to rapid industrialization, urbanization, and population growth. Conventional water quality monitoring systems rely on manual testing or cloud-based IoT frameworks, which often face vulnerabilities such as data tampering, network latency, and security breaches. To address these limitations, this research proposes a Blockchain-Based Secure Data Framework for IoT Water Monitoring using ESP32 and LoRa communication integrated with Firebase Cloud. The proposed system ensures tamper-proof, transparent, and decentralized data management for multi-parameter water quality monitoring. IoT sensor nodes equipped with pH, turbidity, TDS, and temperature sensors collect real-time data transmitted via LoRa gateways to a blockchain-enabled cloud interface. The blockchain layer secures sensor data through cryptographic hashing, consensus validation, and distributed ledger mechanisms. Experimental validation demonstrates that blockchain integration reduces unauthorized data manipulation by 98% and enhances system trust and traceability. The framework achieves an average latency of 1.2 seconds per transaction and consumes 27% less power compared to traditional cloud-only solutions. The results highlight blockchain’s potential to revolutionize secure environmental monitoring and ensure reliable, transparent water data management for sustainable smart cities.

Abhilash Deori

The proliferation of Non-Fungible Tokens (NFTs) has revolutionized digital asset ownership and trading, creating unprecedented opportunities for creators and collectors. However, existing NFT marketplaces face significant challenges, including limited user discovery mechanisms, inadequate recommendation systems, security vulnerabilities, and poor user experience design. This paper presents a new way to run an NFT marketplace using Blockchain and Artificial Intelligence. The system keeps everything secure by storing asset information on a distributed online ledger. With built-in AI, it helps users find content they'll like by giving personalized suggestions. It also uses multiple authentication steps to make sure the marketplace stays safe for everyone. The design uses decentralized storage through the InterPlanetary File System (IPFS). It employs smart contract automation for transaction processing and incorporates machine learning algorithms for fraud detection and user behavior analysis. We demonstrate the effectiveness of our approach with implementation results that show improved user engagement, reduced transaction costs, and better security compared to traditional NFT platforms. The system achieves a 47% improvement in user retention and a 63% increase in successful transactions through personalized recommendations. This research contributes to the growing field of blockchain-based digital asset management and provides a scalable framework for next-generation NFT marketplaces.

Nnaemeka Kingsley Ugwumba

No abstract is available for this record.

Eranga Bandara, Safdar Hussain Bouk, Sachin Shetty, Ravi Mukkamala · 10 authors

Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.

Anis Ur Rehman, M. J. Sanjari, Rajvikram Madurai Elavarasan, Taskin Jamal

Transformation of the energy sector is necessary to meet climate targets and ensure universal access to reliable and affordable energy. Despite progress, more than 675 million people still lack electricity and 770 million face an unreliable power supply. Renewable energy now provides nearly 30 % of global electricity generation and represents approximately 17.9 % of total final energy consumption. This amount is insufficient for the 1.5 ∘ C pathway and requires a tripling of renewable capacity by 2030. Energy efficiency also lags with average annual gains of 1.6 % compared with the 4 % required for climate-aligned energy scenarios. Therefore, this paper reviews pathways toward decentralized low-carbon solutions that can accelerate global energy transformation. The review paper examines how technologies such as microgrids, virtual power plants, energy storage systems, and vehicle-to-grid (V2G) solutions are reshaping modern energy systems. It highlights that digitalization, smart grids, and sector integration are key to building flexible and consumer-focused networks. However, achieving sustainable energy access requires more than new technologies. Strong governance, fair financing, and social inclusion are equally important to ensure a just and balanced energy transition. Case studies from Asia, Africa, and Latin America show how policy, innovative financing, and regional cooperation can drive progress despite challenges such as underinvestment, fossil fuel dependency, and energy poverty. The review demonstrates that an integrated approach, combining technological innovation, financial mechanisms, and inclusive policies, can collectively build low-carbon, resilient, and equitable energy systems. • Research gaps in sustainable energy supply on technology, policy, and equity are identified. • Sustainability-aligned pathways toward decentralized low-carbon solutions are reviewed. • Governance and planning are key for sustainable energy transitions. • A comprehensive framework of technical, economic, and social insights for sustainable transition is introduced.

John Carlo B. De Leon, Alfred Lehar

This paper surveys the growing empirical literature on decentralized finance (DeFi), emphasizing how protocol design and incentive structures shape economic outcomes in blockchain-based financial systems. We review evidence on tokens, decentralized exchanges, lending platforms, yield farming, derivatives, governance, infrastructure, and regulation. Across these domains, research highlights mechanisms of liquidity provision, price discovery, leverage, systemic fragility, and investor behavior, as well as vulnerabilities stemming from arbitrage frictions, liquidation dynamics, and maximal extractable value. We also examine the roles of audits, oracle networks, settlement mechanisms, and transparency tools in substituting for traditional oversight. The findings indicate that DeFi replicates many functions of traditional finance while introducing new risks linked to pseudonymity, smart contracts, and composability. The survey concludes by outlining open questions for research and policy on market efficiency, governance, systemic risk, and long-term sustainability.

Yining Lu, Wenyi Tang, Max Johnson, Taeho Jung · 5 authors

Existing retrieval-augmented generation (RAG) systems typically use a centralized architecture, causing a high cost of data collection, integration, and management, as well as privacy concerns. There is a great need for a decentralized RAG system that enables foundation models to utilize information directly from data owners who maintain full control over their sources. However, decentralization brings a challenge: the numerous independent data sources vary significantly in reliability, which can diminish retrieval accuracy and response quality. To address this, our decentralized RAG system has a novel reliability scoring mechanism that dynamically evaluates each source based on the quality of responses it contributes to generate and prioritizes high-quality sources during retrieval. To ensure transparency and trust, the scoring process is securely managed through blockchain-based smart contracts, creating verifiable and tamper-proof reliability records without relying on a central authority. We evaluate our decentralized system with two Llama models (3B and 8B) in two simulated environments where six data sources have different levels of reliability. Our system achieves a +10.7\% performance improvement over its centralized counterpart in the real world-like unreliable data environments. Notably, it approaches the upper-bound performance of centralized systems under ideally reliable data environments. The decentralized infrastructure enables secure and trustworthy scoring management, achieving approximately 56\% marginal cost savings through batched update operations. Our code and system are open-sourced at github.com/yining610/Reliable-dRAG.

Hellwig, Alexis

(scroll down for English) Die GAIA Ökonomie – Kurze Gesamtdarstellung Falls Sie die PDF Dateien lesen, beginnen Sie bitte mit "Das Gaia System Buch". Das Buch diagnostiziert zunächst die strukturellen Grenzen des heutigen Geld- und Wirtschaftssystems: Zins- und Zinseszinseffekte verschieben Vermögen automatisch nach oben und erzeugen permanenten Wachstums- und Rationalisierungsdruck – mit sozialen, ökologischen und psychologischen Folgekosten. Reformschnipsel lindern Symptome, ändern aber nicht die Logik des Systems (Wachstumszwang, Hortungsanreiz). Eine anschauliche Metapher des „unsichtbaren Staubsaugers“ illustriert, wie Zinsmechaniken Jahr für Jahr Kaufkraft von vielen zu wenigen absaugen. Als Antwort entwirft GAIA eine neue ökonomische Infrastruktur aus zwei bewusst getrennten Modulen: (1) GAIA Coin als neutrales, nicht-spekulatives Zahlungssystem und (2) ein Impact-Modul als freiwillige Incentive-Schicht. Der Coin verankert einen leichten Umlaufdruck (Demurrage) im Code, damit Geld im Fluss bleibt und Horten unattraktiv wird („Fließen statt Horten“). Er dient nur als Tauschmittel (kein Vermögensvehikel), zielt auf Vermeidung von Vermögenskonzentration und entkoppelt Geld von Machtakkumulation. Technisch setzt das Zahlungssystem auf Dezentralität, Energieeffizienz und Datenschutz: pseudonyme Wallets und Zero-Knowledge-Proofs ermöglichen gültige Transaktionen ohne Offenlegung persönlicher Details; leichte Clients und energiearme Konsensverfahren sichern Alltagstauglichkeit; Open-Source-Code und Supermehrheit für Updates verhindern Machtmissbrauch. Zahlungen erfolgen in Sekunden, auch offline per QR/NFC möglich. Das Impact-Modul macht gesellschaftlich nützliche Wirkungen sichtbar und belohnbar – ohne Zwang und ohne Strafen. Es bewertet Beiträge zu Fürsorge, Bildung, Kultur, Ökologie oder Gemeinschaft mit Impact-Punkten, die den individuellen Coin-Wert leicht erhöhen. Die Bewertung kombiniert drei Säulen: Impact Smart Contracts (regelgebundene, transparente Prüfregeln), einen dezentralen Vertrauensgraphen aus validierenden Instanzen (z. B. NGOs, Universitäten, Bürgergremien) mit Reputation, sowie KI-gestützte Datenanalysen zur Mustererkennung und Manipulationsabwehr; zusätzlich schützen ZK-Nachweise die Privatsphäre. Für Governance und Schutz sorgen Gemeinwohlräte als dezentral organisierte, rotierende Kontrollinstanzen mit transparenter Arbeitsweise und Whistleblower-Schutz. Die Mitglieder erhalten eine anspruchsvolle Ausbildung (Rechtsstaat, Urteilsfähigkeit, Psychologie inkl. Gruppendynamik/Narzissmus/Trauma, technische GAIA-Grundlagen); Inhalte werden durch unabhängige Institutionen wie DGVT oder Alfred-Adler-Institut qualitätsgesichert. Grundlegende Systemänderungen bedürfen stets einer überwältigenden Supermehrheit. Die Einführung erfolgt politisch evolutionär statt revolutionär: Start in lokalen Pilotkreisen (Kommunen, Bürgerhaushalte, Genossenschaften), anschließende kommunale und regionale Integration; das bestehende Währungssystem bleibt parallel bestehen. GAIA wird als ergänzende Infrastruktur positioniert – anschlussfähig für unterschiedliche politische Lager – und perspektivisch durch klare Rechtsrahmen abgesichert (bis hin zur internationalen Non-Profit-Struktur bzw. zum öffentlichen Gut). Ein Schwerpunkt liegt auf Demografie: GAIA adressiert die Ursachen extremer Geburtenraten (Unsicherheit, fehlende Absicherung, Vereinbarkeitsprobleme) statt Symptome. Durch soziale Stabilität, Sichtbarmachung von Fürsorge und Förderung von Bildung/Familienplanung entsteht ein Balance-Effekt – in Regionen mit hoher Geburtenrate sinkt diese freiwillig; in alternden Gesellschaften wird Familiengründung wieder attraktiver. Konkrete Regionalkapitel (Afrika, Europa, Asien) illustrieren Wirkpfade und Folgewirkungen (Ressourcenschonung, weniger Not-Migration, sozialer Frieden). In Summe bietet das Buch einen juristisch-technischen und psychologischen Gesamtentwurf samt Anhang (Glossar, technische Spezifikationen, juristische Rahmenentwürfe) und einen praxisnahen Realisierungsplan für ein sofort pilotierbares System. Die Vision gilt als realistisch, weil GAIA auf Anreize statt Zwang setzt, technisch sicher und dezentral ist und von unten wachsen kann – mit spürbaren Vorteilen: mehr soziale Sicherheit, weniger Ungleichheit, wirksamer Umweltschutz. ______________________ VorwortWir sind sterbliche Zeugen einer verletzten Erde – und zugleich Träger einer unverletzlichen Würde. GAIA entsteht aus dieser Spannung: aus der Würde jedes Menschen und aus der Einsicht, dass Heilung dort geschieht, wo wir einander nicht verurteilen, sondern verstehen. Der Mensch ist im Innersten gut. Was wir „Böses“ nennen, wächst aus feindseligen Umfeldern, aus frühen und späten Traumata, aus unbewussten Abwehrmechanismen, die sich als irrationales Handeln zeigen. Darum fragt GAIA: Woher kommt jemand? In welchem Umfeld hat er gelernt zu denken? Wo liegen Brüche und Abspaltungen? Und wie führen wir Schritt für Schritt zu Gesundheit, Einsicht und Würde zurück? Diese Haltung ist kein Dekor, sondern das Fundament: geprägt von meinem juristischen Studium – mit seinem Sinn für Klarheit, Logik, Rechtsphilosophie und Verantwortung – und von den Einsichten Eugen Drewermanns: Menschen kann man nur durch Liebe heilen. Kein Mensch verliert seine unverletzliche Würde. Ein System, das diesem Menschenbild gerecht werden will, muss Strukturen schaffen, die Zugehörigkeit ermöglichen, Angst reduzieren und die innere Güte aktivieren – nicht durch Beschämung, sondern durch Verstehen und Verlässlichkeit. Ich veröffentliche die folgenden Texte bewusst früh und unpoliert, als fortlaufenden Dialog aus Fragen und Antworten zwischen mir und ChatGPT. Nicht, weil Form unwichtig wäre, sondern weil die Zeit drängt. Es geht nicht nur um fortschreitende Umweltzerstörung. Die Schere zwischen Arm und Reich öffnet sich zunehmend – mit exponientieller Dynamik. Menschen arbeiten immer mehr für immer weniger reale Kaufkraft. Öffentliches Eigentum wird privatisiert und verscherbelt. Staaten verschulden sich, geraten in Abhängigkeiten und tun, was Finanzakteure ihnen diktieren; Politik wirkt entmachtet, Wahlen scheinen kaum noch Kurswechsel zu bewirken. Gleichzeitig entstehen Konflikte – bis hin zu Kriegen – um Ressourcen und um wirtschaftliche Vorherrschaft. In dieser Lage ist Abwarten keine Option. Ich stehe öffentlich dazu, dass hier künstliche Intelligenz mitgewirkt hat; die Verantwortung für Auswahl, Bewertung und Veröffentlichung trage ich. Diese Rohform ist Absicht: Sie macht Herleitungen sichtbar, dokumentiert Entscheidungen und erlaubt, den Denkweg nachzuvollziehen. Redaktionelle Glättungen können später folgen – vorrangig ist die Umsetzung. GAIA ist ausdrücklich kein Projekt des Klassenkampfes. Es will die Gefühle, Anreize und Motivationen aller berücksichtigen – der Reichen, des Mittelstands und der Armen. Ziel ist Ausgleich statt Frontbildung, Würde, Sicherheit und Sinn für alle. Ich bin überzeugt, dass das möglich ist. Ein zentraler Mechanismus ist dabei die Demurrage (Umlaufsicherungsgebühr): Dadurch muss Geld für das Gemeinwohl nicht erst über Steuern „zurückgeholt“ werden, sondern es entsteht ein kontinuierlicher Finanzstrom, der unmittelbar ins Gemeinwohl fließen oder gemeinwohlfördernde Investitionen gezielt subventionieren kann. Technisch gilt: Jeder Nutzer entrichtet automatisch eine kleine Umlaufgebühr (z. B. 0,5 % pro Monat). Diese Beträge werden nicht vernichtet, sondern in einem Treasury (Schatzkonto) des GAIA-Coin-Moduls gesammelt. Das Treasury liegt bewusst im Coin-Modul, damit der Geldkreislauf sauber bleibt und jederzeit exakt nachvollziehbar ist, wieviel Demurrage eingenommen wurde. Aus diesem Topf werden allgemeine Ausgaben (Betrieb, Sicherheit, Technik) gedeckt und gemeinwohlförderliche Maßnahmen finanziert; das Impact-Modul entscheidet lediglich über die Vergabe aus diesem Treasury – einschließlich möglicher Demurrage-Rabatte – nach transparenten Gemeinwohl-Kriterien. Kurz: Demurrage-Einnahmen fließen ins Treasury des Coin-Moduls, das Impact-Modul verteilt daraus wirksam und nachvollziehbar. Ich weiß nicht, ob dieses System in seiner heutigen Form vollständig funktionieren wird. Wenn nicht, ändern wir es, bis es trägt. Und falls Teile scheitern, bin ich mir sicher: In diesen Ideen liegt genügend Inspiration und Struktur, um gemeinsam die Version zu finden, die funktioniert. GAIA ist als lernendes System gedacht – Fehler sind änderbare Daten. Statt Perfektion auf Papier beginnt nun die Programmierung: GAIA Coin und Impact-Modul, iterativ, testbar, offen. Bauen, prüfen, verbessern – jetzt. Wer mitgehen will, ist eingeladen; wer zweifelt, kann uns beim Beweisen zusehen. GAIA ist kein Schaukasten, sondern eine Baustelle für Gemeinwohl, Gerechtigkeit und Umweltschutz. Helme auf. An die Arbeit.E-Mail: info@dzydent.com _____________________ Executive Summary des Manuskripts „Das GAIA-System – Gesamtdarstellung“ (für Entscheider in Politik, Verwaltung, Technik und Stiftungen): Ausgangslage & ZielDas Buch reagiert auf strukturelle Fehlanreize des bestehenden Geldsystems (Horten, Vermögenskonzentration, Wachstumsdruck) und skizziert eine praxistaugliche, rechts- und techniknahe Alternative: GAIA als ergänzende Infrastruktur, die freiwillig parallel läuft und ohne Systembruch eingeführt werden kann. Kernlösung in zwei Modulen GAIA Coin (Zahlungsmittel): nicht-spekulativ, mit leichtem Umlaufdruck (Demurrage) im Protokoll verankert, um Geldfluss zu sichern und Hortung unattraktiv zu machen. Architektur: energiearme Konsensverfahren, pseudonyme Wallets, Zero-Knowledge-Proofs (ZKPs) für Datenschutz, offene Codebasis, Supermehrheit + Timelock für Änderungen. Zahlungen in Sekunden; Offline-Weitergabe per QR/NFC möglich. Impact-Modul (opt-in): freiwillige Wirkungsschicht, die gemeinwohlfördernde Handlungen (z. B. Fürsorge, Bildung, Kultur, Umweltschutz) erfasst und mit Impact-Punkten belohnt; keine Überwachung, keine Ideologie, s

Eduardo Sardenberg Tavares, Antonio José G. Busson, Sérgio Colcher

Smart contracts are fundamental to blockchain ecosystems, but remain susceptible to security vulnerabilities that can lead to severe financial losses. Recent advances in agentic AI systems, powered by large language models (LLMs), enable autonomous code analysis and decision-making without explicit task-specific supervision. These systems leverage prompt engineering and zero-shot reasoning to detect vulnerabilities in smart contracts without prior fine-tuning. In this work, we evaluate the effectiveness of agentic LLM-based approaches in identifying vulnerabilities using prompt engineering and zero-shot reasoning across a curated dataset of Solidity smart contracts. Our findings highlight the limitations of current LLMs in automated vulnerability detection, providing insights into their practical applicability for securing decentralized applications. Our best-performing configuration, which integrates zero-shot reasoning with the Tree of Thoughts framework, achieved an F1-score of 73.66%.

Mr. DEVENDAR, Nandi J. Reddy, B.Sahasra, T.Srileka

Artificial intelligence and the quick development of photograph editing software in latest years have made it very simple to regulate virtual pix covertly. The authenticity and dependability of digital media utilized in social networks, journalism, and criminal proof have come below scrutiny because of manipulations like copy-circulate forgery and deepfake creation. The aim of this work is to perceive photograph forgeries via combining deep gaining knowledge of-based class techniques with traditional feature extraction methods.The cautioned device extracts precise neighborhood functions from input images the usage of the oriented speedy and turned around brief (ORB) algorithm. For powerful feature matching, 2-Nearest Neighbor (2NN) and Hierarchical Agglomerative Clustering (HAC) are then used. A Convolutional Neural community (CNN) model is trained to distinguish among authentic and manipulated photos by means of figuring out pixel-degree irregularities and texture changes if you want to growth type accuracy. examined on the publicly reachable MICC-F220 and MICC-F2000 datasets, the device outperforms baseline SVM strategies with a ninety% detection accuracy and a zero.1 false tremendous charge

Lucas Bastos Germano, Júlio César Duarte

Decentralized finance protocols are frequently exploited, creating a demand for fast and reliable repair of vulnerable smart contracts and validation that reflects runtime security. Large language models are an emerging source of patches, yet many evaluations rely on manual checks or self-assessment, which cannot confirm whether attacker profit is actually prevented. We introduce an executable benchmark that replays verified real-world exploits against patched Solidity contracts under a resilient protocol that permits alternate attack paths and controlled state variation. Our framework compiles candidate patches, deploys them on a forked chain, and tests whether the exploit still yields profit. The benchmark covers six test cases drawn from reproducible incidents and is released as open-source. Among the nine evaluated models, GPT-5, GPT-4.1, and Claude Opus 4.1 performed the best, mitigating four of six test cases. Microsoft Phi-4 was the most reliable open-source model, mitigating two of six exploits and producing compilable patches for the remaining cases. No model mitigated the H2O case once resilient checks were enabled, while a simpler access control flaw, BTNFT, was often repaired with minimal edits. Grounding validation in executable exploit replay provides a precise and scalable method to measure whether proposed repairs harden contracts at runtime.

Liviu Ionut Epure

The convergence of quantum physics and machine learning presents unprecedented opportunities for developing ultra-secure authentication systems. This comprehensive paper investigates the integration of quantum random number generators (QRNGs) with advanced machine learning architectures, including quantum neural networks (QNNs), long short-term memory (LSTM) networks, and hybrid quantumclassical models, to establish authentication mechanisms with information-theoretic security guarantees. We provide rigorous theoretical foundations spanning quantum entropy theory, min-entropy estimation, and randomness certification, complemented by detailed analyses of contemporary QRNG hardware implementations including photonic integrated circuits achieving generation rates exceeding 20 Gbps. The paper explores deep learning architectures for biometric authentication, demonstrating how QNN-enhanced systems achieve superior performance through quantum superposition and entanglement. Furthermore, we examine the application of quantum entropy sources in zero-knowledge proof protocols, particularly zk-SNARKs and zk-STARKs, addressing post-quantum security concerns. Through comprehensive mathematical formulations, algorithmic implementations, and security analyses, we establish that hybrid quantum-classical authentication systems combining QRNG-derived cryptographic keys with ML-based behavioral authentication provide provably secure, practical solutions for next-generation cybersecurity applications. Experimental results from current quantum hardware platforms validate theoretical predictions and demonstrate real-world applicability.