Doan B. L. Nguyen, Pham Khanh Nam, Nguyen Thi Hong Thu
This study examines factors considered in investor decisions to invest in physical and tokenized real estate in Vietnam using discrete choice modeling on data from 413 participants in Ho Chi Minh City. Results show that legality, transparency, transaction fees, and expected returns are key determinants of investment consideration. Older and higher-income investors exhibit lower preference for tokenized assets, while female, more educated, and blockchain-familiar investors show greater adoption tendencies. The findings highlight how legality and institutional quality shape emerging digital asset markets, underscoring the need for legal clarity, transparent data, and targeted education to foster tokenized real estate development.
LoisID proposes a portable trust and reputation infrastructure designed to enable individuals and organizations to accumulate, verify, and transport trust across digital ecosystems. The framework extends beyond identity verification and introduces a reusable trust layer for finance, education, employment, governance, and Web3 environments. By transforming trust into a portable and interoperable digital asset, LoisID seeks to address reputation fragmentation and establish a foundation for the next generation digital economy.
Recent innovation theories on economics remain largely grounded in assumptions of hierarchical firms and closed organizational boundaries, offering limited insight into how innovation unfolds within decentralized, digitally native organizations. Decentralized Autonomous Organizations (DAOs) represent an emerging form of innovation ecosystem characterized by blockchain-based transparency, open participation, and token-driven governance, in which sustainability can be embedded directly into organizational design. This study compares two standards, ERC-8004 and Google A2A, who address the same agent interoperability question, while the former is governed by DAO and the latter by corporation consortium. They are examined through an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures. The study provides evidence-based insights for scholars, policymakers, and designers seeking to align innovation, technological governance, and sustainability in future organizational forms.
Sequential trust detection in rating networks relies on continuous observation models that fail on real data. On Bitcoin-OTC, 56\% of ratings take a single value under standard mapping, breaking the distributional assumptions that parametric detectors require. This paper makes three contributions. It derives a Bayes-optimal F1 detection ceiling for per-node sequential detectors using empirically measured observation parameters. At Bitcoin-OTC's median in-degree of 2, this ceiling falls to 0.451 for strategic attacks, explaining why unsupervised methods cluster near $F1 \approx 0.4$. The analysis shows that detector-model matching, not information content, determines performance: binary models retain 86\% of mutual information while enabling exact parametric fit. A dual-regime architecture is presented where Bernoulli CUSUM detects behavioral shifts and triggers asymmetric scoring. Ablation reveals a co-design constraint: the modulation mechanism improves AUC by 0.030 on binary observations but degrades it by 0.094 on continuous observations. The combined system achieves AUC 0.749 on Bitcoin-OTC and 0.796 on Bitcoin-Alpha, beating GaaSTrust on all 8 attacks ($p < 0.003$), with founder-label AUC of 0.999.
Ju Yang, Weili Wang, Jianyu Niu, Jianzong Wang · 5 authors
Confidential blockchains leveraging Trusted Execution Environments (TEEs) have garnered extensive attention for transaction confidentiality. In this paper, we first taxonomize two classes of attacks against confidential blockchains, i.e., execution-inference and execution-replay attacks, which exploit TEEs' long-lasting side-channel and state-continuity issues to compromise the confidentiality of existing consortium blockchains. Then, we present ODYSSEY, a confidential blockchain that efficiently mitigates these attacks. The core innovations of ODYSSEY are the following: (1) Its delegation model: clients delegate transaction execution to their designated trustees, while other participants synchronize only the execution results, which significantly reduces the attack surface while preserving confidentiality and system performance. (2) Two novel techniques to improve ODYSSEY's efficiency and security: location-aware concurrent execution and delegation failure handler. Finally, we develop a prototype of ODYSSEY on FISCO BCOS, an enterprise-grade consortium blockchain platform. We have conducted various experiments, and our evaluation results show that in a WAN environment with 3 nodes, ODYSSEY can achieve about 4k throughput while keeping latency as low as 0.4-0.5s.
Pearl, a Layer-1 blockchain with high-profile AI industry endorsements, markets its Proof-of-Useful-Work (PoUW) protocol as simultaneously securing the network and performing AI inference. We present the first systematic empirical measurement of a deployed PoUW system, finding that Pearl's 24 EH/s network -- representing approximately 320,000 GPU-equivalents consuming an estimated 112 MW -- produces zero useful AI computation. Budget GPU rental prices rose 38% and utilization surged from 57% to 94% following the mining software's public release, displacing legitimate research workloads. Our measurements span five dimensions: (1) network composition analysis of 8,012 workers shows all have inference-capable hardware, yet the dominant mining software contains no inference code; (2) the verification protocol accepts random matrices by design, confirmed by 44 pool-accepted shares from our open-source miner across NVIDIA, AMD, CPU, and Apple Silicon hardware; (3) statistical distribution checks are trivially defeated by adversarial Gaussian sampling; (4) mining economics are marginal at current PRL prices ($0.76), with ROI ranging from -1% to +67% depending on GPU tier -- near breakeven for most hardware; and (5) the mining computation is commodity integer arithmetic portable to any hardware platform, offering no vendor lock-in. These findings quantify the verifiability-usefulness tension identified theoretically by Leinweber et al., providing concrete measurements of its magnitude and economic consequences in a deployed system.
This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets. Although classical implementations of the strategy have proven successful in traditional equities, they frequently exhibit rigidity and suffer from severe divergence risks when applied to high-variance environments. To address this need, this research introduces novel concepts. To construct a robust system, we developed a hierarchical "Filter-then-Rank" pair selection methodology and a proprietary "Fixed Risk, Adaptive Mean" execution model. The system employs a Proximal Policy Optimization (PPO) agent with a Long Short-Term Memory (LSTM) layer to govern execution decisions within strict deterministic risk management boundaries. Evaluated on 1-hour interval data from the Binance USD-M Futures market, the optimized RL policy achieved an out-of-sample performance that substantially outperformed the heuristic baseline. A stationary circular block bootstrap robustness check confirms that the agent's risk-adjusted outperformance is statistically significant at the 10 percent level. Although falling marginally short of the stricter 5 percent threshold, this result highlights the extreme idiosyncratic variance characteristic of digital assets. Ultimately, this thesis contributes to the quantitative finance literature by introducing a hybrid architecture that combines statistical arbitrage with DRL execution policies. Furthermore, it delivers a novel framework for safe reinforcement learning via deterministic shielding, proving that anchoring a neural policy to statistically robust boundaries successfully mitigates severe divergence risks.
Muhammad Hadi, Muhammad Jahangir, Talha Shafique, Muhammad Khuram Shahzad
Federated Learning (FL) has emerged as an effective paradigm for collaborative intelligence while preserving data privacy. However, data heterogeneity arising from non-IID distributions and decentralized security threats remain significant challenges, particularly in resource-constrained enterprise environments. This paper presents TITAN-FedAnil+, a Trust-Based Adaptive Network for blockchain-enabled federated learning in intelligent enterprises. The proposed framework introduces affinity propagation-based adaptive clustered aggregation to identify and filter malicious updates without requiring prior knowledge of the number of attackers. In addition, GPU-accelerated vectorization is employed to improve computational efficiency, while a signed state jump mechanism enables lightweight blockchain resynchronization. Experimental results demonstrate substantial reductions in memory overhead, achieving up to 81% savings across 50 communication rounds on constrained 8 GB edge devices compared with the baseline framework. The results indicate that TITAN-FedAnil+ effectively improves robustness, scalability, and resource efficiency for secure federated learning deployments in intelligent enterprise environments.
Jeremy Avigad, Anat Ganor, Lior Goldberg, David Levit · 7 authors
StarkWare's S-two prover provides an efficient means for establishing, on blockchain, that a program written in the Cairo virtual machine language runs to completion. The latter claim is encoded by an algebraic intermediate representation (AIR) that captures the semantics of the Cairo language. The AIR asserts the existence of tables of values from a finite field satisfying certain algebraic constraints. A cryptographic interactive proof system, circle STARK, provides an efficiently-checked certificate that the AIR is satisfied. We describe our verification, using the Lean 4 proof assistant, that the AIR encoding is sound, which is to say, the satisfiability of the AIR implies the computational claim.
Abstract AI-generated forgeries of financial documents—such as invoices, audit reports, ledgers, and balance sheets—expose a critical fault line in legal proof. These hybrid visual–textual artefacts derive evidentiary authority from their jurisvisual form: logos, seals, signatures, and tabular architecture, whose visual grammar indexes authenticity and institutional power. Drawing on Charles Sanders Peirce’s triadic semiotics (representamen–object–interpretant), this study demonstrates that deepfake technologies dissolve the sign-relation underwriting documentary proof by engineering synthetic representamina that mimic the indexical and symbolic features of authentic documents. At the same time, the underlying financial event may be absent. The evidentiary economy is thereby reconfigured within a videosphere where image-like documents perform the truth. This article advances a layered remediation architecture: (i) provenance anchoring through cryptographic signatures, content hashing, and distributed ledgers; (ii) content forensics integrating AI-assisted detection with forensic semiotics—indexical stress tests and symbolic authenticity challenges; and (iii) procedural safeguards including calibrated evidentiary thresholds, adversarial authenticity hearings, and robust chain-of-custody protocols. It argues that restoring evidentiary confidence requires cultivating semiotic literacy among judges, auditors, and legal practitioners as core professional competence, enabling legal systems to navigate the post-textual landscape with epistemic rigour.
The quick progress of the gig economy and the appearance of digital nomadism have transformed traditional employment and financial ecosystems, increasing reliance on digital financial services. FinTech modernizations, including movable banking, digital cases, blockchain technologies, and decentralized finance, have significantly enhanced admittance to financial facilities. However, the extent to which these innovations contribute to meaningful financial inclusion remains a critical area of inquiry. This study presents a wide-ranging analysis of poetry examining the part of FinTech in enhancing financial inclusion among digital nomads. Using a narrative review approach, the study synthesizes research across themes such as ordinal finance embracing, financial literacy, gig economy dynamics, and platform-based monetary amenities. The verdicts signpost that while FinTech improves accessibility and efficiency, tests such as regulatory barriers, trust deficits, financial literacy gaps, and cross-border complexities persist. The study highlights the need for integrating technological, behavioral, and policy perspectives to achieve inclusive financial systems. The review contributes to the literature by providing a multidimensional understanding of FinTech-enabled inclusion and offers inferences for representatives, financial establishments, and gig workers.
The Internet of Vehicles (IoV) is changing the contemporary mobility, as it allows real-time communication between vehicles, infrastructure, and cloud services. Nevertheless, such growing connectivity brings on serious privacy, regulatory, and trust issues especially because sensitive behavioral and location information is exposed. The current IoV-security systems tend to be based on identity-based checks, or centralized trust authorities, which can lead to infringement of user privacy and cause surveillance and profiling threats. The paper is inspired by privacy-preserving architectures in the Metaverse to suggest a decentralized trust system of IoV systems on the basis of zero-knowledge proofs, namely zk-SNARKs. The suggested solution allows vehicles to cryptographically verify that they meet regulatory or operational regulations- i.e. valid insurance, safety test, or emissions- without revealing personal identifiers or raw information. The framework enables building scalable, low-latency and audible trusts and following data minimization principles through combining zk-SNARK verification and Layer 2 blockchain solutions.
This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of N=10 ETFs over T=514 trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment × BrandScore interaction is significant (β^=2.930, p=0.038), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects (p=0.012) and winsorization (p=0.004). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets.
Salience-Queue Occupation Theory (SQOT) is a protocol-relative mathematical framework for analyzing when finite-budget operational processes lose effective control over their priority queues under persistent or adversarial salience sources. The manuscript formalizes salience occupation using observable histories, budget ledgers, diagnostic reserves, queue morphisms, finite certificate grammars, checkable ledgers, typed risk composition, self-auditing kernels, and route-sound checker semantics. The theory is designed for artificial, distributed, or post-biological operational systems, but it does not rely on subjective psychology or normative claims about what a process should attend to. Instead, it studies finite, auditable conditions under which a process can preserve diagnostic capacity, response or no-action availability, rollback or quarantine options, semantic-egress safety, mechanism-compatible incentives, and bounded verification cost. The results include finite checker semantics soundness, checked non-circular sovereignty certificates, typed risk composition, adaptive succinct-session soundness, egress abstraction refinement, and payoff-reflected mechanism robustness. SQOT explicitly limits its claims to declared validity domains and does not assert absolute physical, cryptographic, economic, or base-reality guarantees.
C. Rupa, K. Vijaya Bhaskar Reddy, Srinivas Jagirdar, Srinivas Rao Pulluri · 6 authors
Land registration and record management systems worldwide continue to face significant challenges, including document fraud, long processing times, and inefficient maintenance procedures. Traditional methods involve several technical limitations that reduce reliability and transparency. To address these issues, the proposed system leverages blockchain technology to improve process efficiency, data integrity, and security in land registration workflows. In the proposed framework, users upload property details and supporting land documents while initiating a sale. However, fraudulent document uploads remain a common issue, enabling sellers to receive payments using forged records without the buyer’s knowledge. To mitigate such risks, a Convolutional Neural Network (CNN) is integrated to authenticate and validate uploaded land documents before further processing. Only documents verified as authentic are stored on the blockchain. The government authority converts these validated documents into Non-Fungible Tokens (NFTs) and mints them on the Ethereum blockchain. A unique hash is generated for each document, enabling secure verification and traceability through platforms such as Etherscan. Once the documents are confirmed to be valid, the property is approved for sale, and the ownership transfer between the seller and buyer is securely executed through the blockchain enabled system. We evaluated the performance of proposed framework by considering both blockchain performance metrics and CNN evaluation metrics.
Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high-impact models, but enforcement rests on self-reporting because no technical verification primitive for training exists. Any future international agreement on frontier AI faces the same problem at higher stakes: coordinated regulation of technologies with significant externalities has historically rested on technical verification, without which agreements are declaratory. Recent governance analyses judge zero-knowledge proofs a promising candidate but currently impractical at frontier scale [26, 4]. We argue the impracticality is paradigm-bound rather than fundamental, and propose a verification architecture for frontier dense pre-training combining a pre-committed training specification, inter-node network observations, and on-the-fly Merkle commitments of intermediate computation, verified through a zero-knowledge Virtual Machine (zkVM) with native BF16/FP32 precompiles. The proof checks the actual floating-point computation the GPU performed rather than a fixed-point approximation, and preserves model-architecture confidentiality through a private training specification. The protocol produces three proof types: a genesis proof at initialisation, in-training step proofs across the run, and ex-ante attestations enforcing policy-relevant claims as running invariants, turning the training record into a governance-enforceable artefact. We estimate a deployable proof of concept within approximately 36 months at single-digit-percent training-side overhead, against a six-to-ten-year cycle for verification-grade custom silicon. Thirteen open research and engineering problems are catalogued as a research agenda for external contribution
Decentralized Finance (DeFi) services are usually constructed by composing a variety of smart contracts. While composability is a key driver of the success of DeFi, it also creates security risks: adversaries may exploit interactions between newly deployed contracts and the pre-existing ones to inflict economic losses. We introduce MEV non-interference, a formal security notion for DeFi composability requiring that the maximal extractable value from a set of newly deployed contracts is not increased by interactions with the existing blockchain state. To support this notion, we define local MEV, a novel measure of economic attacks that focusses on the loss of a given set of victim contracts. We study two adversarial models, with bounded and unbounded wealth, and establish sufficient conditions and locality principles that enable modular reasoning about secure composability. We apply the framework to representative DeFi compositions, including exchanges, AMMs, options, lending pools, routers, and arbitrage contracts, showing how it distinguishes secure compositions from vulnerable ones. Our results provide a formal foundation for reasoning about the economic security of DeFi compositions.
Few forces have reshaped organizational life as quickly as digital transformation. The way firms create value, manage risk, and hold their competitive ground now depends on systems that grow more entangled with one another every year. A typical enterprise sits at the center of a constant flow of data drawn from its operations, its cloud platforms, the sensors embedded in its products, its planning systems, and the many places where it meets its customers. Artificial intelligence (AI), machine learning, big data analytics, blockchain, and cybersecurity have each made it easier to turn that flow into useful judgment. Yet most organizations still adopt these tools one at a time, and the habit quietly erodes the strategic payoff that integration could deliver. This paper sets out an Autonomous Decision Intelligence (ADI) framework that gathers AI, cybersecurity, big data analytics, blockchain, and management information systems (MIS) into one coherent architecture built for resilient digital enterprises. The argument rests on a synthesis of recent work in decision intelligence, predictive analytics, business intelligence, federated learning, cloud computing, blockchain governance, cyber threat intelligence, and enterprise risk management. From that body of evidence, we construct a conceptual model for organizational decision-making that is trustworthy and capable of improving itself over time. The framework gives weight to secure data governance, explainable AI, privacy-preserving analytics, blockchain-based trust, cyber-resilience, and intelligent automation. It then asks how such a design might reinforce critical infrastructure protection, supply chain resilience, economic sustainability, IT project governance, and day-to-day agility. The contribution is at once theoretical and practical, because it shows how converging technologies can turn conventional decision-support systems into adaptive ecosystems that learn. Organizations that pair AI-driven analytics with strong security and decentralized trust look best placed to absorb uncertainty, keep operating under stress, and pursue digital transformation that lasts.
Trusted Execution Environments (TEEs) have emerged as a critical technology for safeguarding sensitive data and ensuring code integrity in modern computing systems. However, relying on a single TEE implementation makes systems vulnerable to a central point of attack. Building distributed-trust systems leveraging heterogeneous TEEs helps disperse trust but still faces threats from centralized management and adaptive mobile adversaries. To address these challenges, this paper introduces TeeDAO, a novel three-layer framework that automatically organizes multiple heterogeneous TEE instances and provides unified interfaces to support diverse applications, while ensuring long-term guarantees of availability, integrity, and confidentiality. TeeDAO couples BFT-ordered governance with heterogeneity-aware Distributed Proactive Secret Sharing (DPSS) and Secure Multi-Party Computation (MPC) so that attestation-driven committee changes are consistently reflected in secret recovery, resharing, and computation across a dynamic committee of heterogeneous TEEs. We implement a prototype of TeeDAO, integrating COBRA's DPSS scheme with the HotStuff BFT consensus protocol, and adapt it for Intel SGX, TDX, and Hygon CSV. Evaluations demonstrate that TeeDAO achieves up to 1.8x higher key-value store throughput in a large cluster with 61 nodes compared to state-of-the-art systems, efficient autonomous management, and minimal computation overhead (<18%) for multi-party computation tasks.
Abstract : This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries. LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.
We introduce the Polymarket-v1 Database: the complete on-chain trade archive of Polymarket's first-generation CTF Exchange on Polygon, spanning 2022-11-21 to 2026-04-28 and covering the full contract lifecycle from first settlement to natural termination. The dataset comprises 1.20 billion trade records across 1.30 million markets with $61 billion in nominal volume. Its defining feature is 100% ground-truth aggressor direction derived from the blockchain settlement layer, a property unavailable in existing prediction market archives, which rely on heuristic inference. We use this truth-aligned archive to benchmark standard microstructure tools and document three findings. First, the tick rule and bulk volume classification achieve near-random aggregate accuracy (49.83% and 50.51%), but this masks a systematic, correctable price-level gradient driven by positive trade direction autocorrelation and concentrated market-making -- two structural features of prediction markets that violate the mean-reversion assumption embedded in classical classifiers. Second, these classification errors propagate into downstream metrics: inferred VPIN diverges substantially from ground-truth VPIN, and OFI estimates are directionally biased, with material consequences for Transaction Cost Analysis. Third, ground-truth microstructure quality predicts forecasting performance in ways that classification-based proxies cannot recover: True VPIN positively predicts Brier scores, while Gibbs spread negatively predicts them -- a selection effect reflecting that high-spread niche markets attract informed specialists rather than noise traders. Replacing ground-truth metrics with classified proxies attenuates both relationships, illustrating that measurement accuracy at the transaction level is a prerequisite for reliable inference about prediction market design and probability calibration.
Antonio J. Fernández-Pinto, Manuel Bravo, Gregory Chockler, Alexey Gotsman
Unauthenticated Byzantine consensus protocols achieve optimal failure resilience while relying only on authenticated point-to-point channels, not authenticated messages. They are an attractive building block for blockchains that do not mandate symmetric trust assumptions as well as for future post-quantum settings. We consider unauthenticated Byzantine consensus in partially synchronous networks and focus on optimizing its good-case latency - the worst-case time for correct processes to reach a decision under favorable conditions. A recently proposed ForgetIT protocol achieves an optimal good-case latency of 3 message delays but employs a highly complex design. We show that this complexity is unnecessary. To this end, we present Fast TetraBFT - an unauthenticated Byzantine consensus protocol that achieves optimal good-case latency by augmenting an existing TetraBFT protocol with a simple fast-path wrapper. Our solution lowers the good-case latency of TetraBFT from 5 to 3 message delays while preserving its bounded space requirements and low communication complexity.