V Vishnu Prasad, Meta Dev Prasad Murthy, Rishika Jain
Premium non-fungible token (NFT) collections often fail to attract liquidity, while modest but coherent ones thrive, presenting an anomaly that classical signaling cannot explain. We reframe market-making as a coordination problem and introduce a Brand × Topology × Dispersion (BTD) framework, arguing that participation follows weakest-link clarity: the least clear signal dimension, not the average, governs action. A high-realism 2 × 2 × 2 experiment (N = 336) shows that brand capital, ownership topology, and value dispersion each raise willingness to trade, yet the minimum across them dominates conversion; discordant signals depress engagement more than concordant signals lift it; and signals act as complements in thin markets but substitutes in mature ones. A 6-month Ethereum panel, analyzed with fractional logit and Cox hazard models, replicates these patterns in the field. The studies extend signaling theory from dyadic quality revelation to multilateral coordination and yield a bottleneck-governance principle for marketers and platforms, suggesting that the weakest clarity dimension be repaired first.
O presente artigo analisa o custo-benefício energético de três mecanismos de consenso centrais no ecossistema de criptoativos – Proof-of-Work (PoW), Proof-of-Stake (PoS) e o modelo híbrido baseado em Proof-of-History (PoH) combinado com PoS – examinando como as diferenças de consumo energético entre esses paradigmas se relacionam a propriedades de segurança, desempenho e sustentabilidade econômica. A partir de dados recentes sobre o consumo energético de redes públicas de referência – entre as quais o Bitcoin, o Ethereum antes e depois da transição para PoS (Merge) e a Solana – discute-se em que medida a evolução dos desenhos de consenso permite reduzir o uso de eletricidade por ordens de grandeza, sem necessariamente comprometer segurança e descentralização. A metodologia combina revisão bibliográfica de estudos acadêmicos e relatórios técnicos sobre consumo energético em blockchains, análise de estimativas consolidadas de uso anual de eletricidade e de energia por transação e discussão conceitual dos trade-offs entre eficiência energética, robustez criptográfica, requisitos de hardware e impactos regulatórios. As evidências empíricas revisadas indicam que o Bitcoin, ancorado em PoW, mantém consumo anual estimado em torno de 120 a 130 terawatt-hora (TWh), ao passo que o Ethereum, após a migração para PoS em setembro de 2022, reduziu seu consumo em mais de 99,9%, operando com menos de 0,01 TWh por ano. Relatórios de eficiência energética apontam que redes que combinam PoH e PoS, a exemplo da Solana, apresentam consumo de energia por transação da ordem de centenas de joules, valor inferior tanto ao de redes PoW quanto ao de diversas redes PoS de menor vazão, embora existam ressalvas metodológicas e debates acerca dos efeitos de centralização de infraestrutura associados a requisitos elevados de hardware e conectividade. Conclui-se que PoS e esquemas híbridos com PoH oferecem vantagens substanciais em termos de eficiência energética, mas que a avaliação de custo-benefício deve incorporar conjuntamente a segurança econômica, a distribuição de poder entre participantes, a maturidade do ecossistema e o alinhamento com agendas de sustentabilidade que tendem a moldar a evolução da infraestrutura Web3 nas próximas décadas.
Sai Sakunthala Guddanti, Anupama Ray, Mrunal Arun Kumavat, Anil Prabhakar
This article explores the potential of Quantum Machine Learning (QML), specifically assessing a Quantum Support Vector Machine (QSVM) and a Variational Quantum Classifier (VQC) for detecting anomalies in real-world financial transaction data. While these QML methods outperform statistical methods, they fall short of cutting-edge deep learning techniques. To bridge this gap, we propose a hybrid quantum-classical ensemble framework that leverages the strengths of both domains. We demonstrate its effectiveness in detecting phishing in Ethereum transaction networks by combining complementary algorithms. The QSVM, whether used individually or in an ensemble, consistently delivered the lowest false negatives and higher recall rates, that are crucial for anomaly detection. To enhance individual models, we encoded the data using novel cascaded Quantum Random Access Coding (QRAC) schemes and compared it with the popular encoding ZZ feature map on both simulators and the IBM Heron quantum processor. For both QSVM and VQC, we consistently observed improvements (13% for QRAC-VQC and 3% for QRAC-QSVM) of QRAC over the ZZ feature map. Notably, certain QML algorithms exhibit remarkable resilience on the IBM Heron quantum processor, approaching simulator-level performance on devices with high quantum volume. This observation underscores the promise of QML despite hardware limitations.
Jingyu Liu, Bolin Zhang, Lin William Cong, Siguang Li · 5 authors
Ethereum's Glamsterdam upgrade introduces enshrined proposer-builder separation (ePBS), replacing relay-centric PBS with direct builder bids to proposers. We study how this shift changes the block-building microstructure through a general imperfect-information two-stage auction with verifiable messages, where an early bid serves as both a price offer and a signal. PBS and ePBS are modeled as restrictions of the same block-building game: PBS fixes stopping and disclosure exogenously, while ePBS lets the proposer choose stopping and disclosure ex post. Latency heterogeneity is captured by asymmetric information updates: fast builders observe disclosed early information before rebidding, while slow builders do not. We combine exact perfect Bayesian equilibrium characterizations in tractable cases with calibrated no-regret learning in finite games. For PBS, we show that separating equilibria preserve the standard first-price-auction payoff benchmark and provide conditions for their existence. For ePBS, we demonstrate a ratchet effect: because the proposer can defer block proposal and use early bid information in the second stage, builders anticipate ex-post extraction and shade or pool early bids, generating allocation inefficiency and revenue-efficiency valleys. We interpret this ratchet distortion as a commitment failure. Under full commitment, the optimal policy collapses to the static Myerson auction and removes the ratchet channel. To realize part of this commitment advantage in a feasible mechanism, we propose a Trusted Execution Environment (TEE) sidecar that enforces limited commitment. We formulate the revenue-maximizing TEE mechanism as a bilinear optimization problem. In conservative finite benchmarks, the TEE design increases the proposer revenue relative to the first-price benchmark by approximately \(25\%\).
Ignatia Bintang Filia Dei Susilo, Vidya Purnamasari, Sulistya Rini Pratiwi, Yelly Zamaya · 5 authors
The rapid development of smart-contract-based blockchain ecosystems has transformed the perception of digital assets. However, the extent to which these assets are influenced by macroeconomic conditions in emerging markets remains poorly understood. This study aims to examine the long-term and short-term relationships between three major smart-contract platforms: Ethereum (ETH), Build and Build (BNB) Chain, and Solana (SOL), and several Indonesian macroeconomic indicators: money supply (M2), consumer price index (CPI), the rupiah-to-US dollar exchange rate (IDR/USD), and the policy interest rate (BI Rate). The study draws on monthly data spanning April 2023 to September 2025. The findings reveal that each platform exhibits a distinct degree of sensitivity to Indonesian macroeconomic conditions. Overall, the three platforms demonstrate a strong long-run relationship with the selected macroeconomic variables. The rising money supply (M2) tends to have a positive effect on all three platforms, while the influence of the exchange rate varies across ecosystems. Furthermore, this study traces how shocks in macroeconomic variables are transmitted to cryptocurrency prices and identifies distinct volatility patterns across the three platforms. Its findings contribute to understanding the relationship between crypto assets and macroeconomic conditions. It also offers practical insights for portfolio diversification strategies and for developing regulatory frameworks in Indonesia's growing digital asset market.
Abstract Stateless Ethereum is one of the most promising directions for improving blockchain scalability, but witness size remains a major deployment challenge. Existing witness generation techniques often treat all transaction workloads uniformly, resulting in unnecessary communication overhead and reduced verification efficiency. This paper presents AWCV (Adaptive Witness Compression for Stateless Ethereum Using Verkle Trees), an adaptive compression framework that reduces witness size while preserving efficient verification. AWCV combines locality-aware transaction clustering, subtree deduplication, adaptive witness construction, and delta encoding to eliminate redundant proof data without modifying Ethereum's verification semantics. The proposed framework is evaluated across representative blockchain workloads and demonstrates significant reductions in witness size, bandwidth requirements, and verification latency compared with conventional witness generation approaches. AWCV remains fully compatible with Ethereum's Verkle Tree roadmap while improving the practicality of stateless client synchronization and scalable decentralized applications. The results suggest that adaptive witness compression provides an effective and practical path toward efficient Stateless Ethereum deployments without sacrificing security or correctness.
Crypto's dominant narrative—tokenizing treasuries, equities, and lending products—cedes value to incumbents who will treat any blockchain as a replaceable backend. The real opportunity is alternative markets: economic coordination problems that Wall Street structurally cannot or will not solve. We catalog 50 alternative markets across six categories, identify the ~20 that are genuinely blockchain-necessary, and estimate $200B–$1T in new annual GDP (0.2–1.0% of global output). We then argue that Ethereum will not pursue these markets—its ecosystem is structurally captured by the tokenization narrative, as evidenced by the shutdown of pioneering projects like Goldfinch and a broader exodus of builders from the ecosystem. We propose that a purpose-built, privacy-native blockchain is the correct vehicle, and lay out the architecture, cold-start sequencing, and talent recruitment strategy to build it.
Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar, Rahim Tafazolli
Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability. This paper reframes fraud control as blockchain-linked auditable decision management for synthetic telecom/IoT fraud-control requests, and its main result is that the QLoRA-tuned LLM branch becomes much more usable than zero-shot prompting but mainly approaches, rather than outperforms, a lower-cost centralized ensemble. The framework maps each synthetic deployment record to a managed request, blocks explicit out-of-boundary cases through a deterministic hard-fraud gate, scores non-hard requests using centralized ML (M1), federated meta-learning (M2), or LLM-family risk sources (M3), and resolves actions through a shared five-state policy, two-zone refinement mechanism, and local Ethereum-compatible audit layer. Evaluation uses separate synthetic training data and a 100,000-record deployment replay corpus, so the study should be read as controlled drift-replay evidence rather than field validation or proof of live deployability. On validation, M1 gives the strongest balance, with legitimate-request FPR 0.0890 under the 0.10 operating cap and soft-fraud recall 0.8341. On labeled deployment replay, however, the legitimate-FPR gap becomes large: M1 rises to 0.1646 and M3-QLoRA to 0.1801, while M3-QLoRA reduces the M3-Base legitimate FPR from 0.3915 and reaches 0.8240 soft-fraud recall. Blockchain telemetry shows that lifecycle gas, cost, latency, and throughput differences are driven by submitted off-chain decision profiles rather than changes in fraud logic.
We introduce NTRU-VRF, the first verifiable random function (VRF) constructed directly from the NTRU lattice hardness assumption, and instantiated concretely using the NIST-standardized Falcon-512 (FN-DSA) signature scheme. A VRF is a pseudorandom function that produces a publicly verifiable proof of correctness for each output. All currently deployed VRFs (IETF RFC 9381, Algorand, Ethereum) rely on elliptic-curve assumptions broken by Shor's algorithm. Prior post-quantum VRF constructions either require only a few-time security guarantee (Esgin et al., ePrint 2020/1222), rely on symmetric primitives that lack a worst-case lattice hardness reduction (Buser et al., ePrint 2021/302), or are based on Module-LWE/Module-SIS rather than NTRU. No prior work constructs a many-time, lattice-based VRF from NTRU hardness with a formal security proof. Our construction exploits a fundamental and previously unformalized property of Falcon's deterministic signing mode: for any fixed public key and input, there exists exactly one valid short-norm signature. This unique-signature property is the key structural feature that transforms a lattice signature into a VRF. We prove three theorems: Uniqueness: For any input, the NTRU-VRF output is unique. This follows directly from the unique-short-coset-vector property of the NTRU lattice. Pseudorandomness: If the Short Integer Solution (SIS) problem on NTRU lattices is hard and the hash function is modelled as a random oracle, then the VRF output is computationally indistinguishable from a uniformly random value. Provability: The Falcon signature is an efficient, publicly verifiable proof, checkable by any party holding the public key. As a concrete application, we define PQ-Sortition, a post-quantum proof-of-stake leader-election protocol that replaces ECVRF-based sortition with our NTRU-VRF construction. We provide the entropy-chain design, stake-weighted win condition, adaptive liveness mechanism, equivocation slashing, and a formal security analysis of the resulting consensus protocol. Instantiated with Falcon-512, NTRU-VRF achieves a VRF output of 32 bytes, a proof size of <= 666 bytes, evaluation time of ~0.8 ms, and verification time of ~0.1 ms on standard hardware—significantly outperforming all existing post-quantum VRF constructions and making it the first many-time, compact, lattice-based VRF suitable for high-throughput blockchain consensus.
Smart contract compilers are critical to ensuring the correctness of public blockchains whose defining characteristics are open-source and immutable code. We created SolSmith, a semantics-aware differential fuzz testing tool, to improve the quality of the Solidity compiler -- the most popular compiler for the Ethereum blockchain -- and spent over three years finding compiler defects that produce incorrect code. We call these defects miscompilation bugs. During this time period, we have discovered 25 miscompilation bugs that went unnoticed, some for multiple years. Our first contribution is to make compiler testing more rigorous. SolSmith achieves this goal by generating valid test programs that are likely to stress test code generation and optimization components. This helps SolSmith find bugs missed during routine testing that could potentially have serious implications for smart contracts and their users. Our second contribution is a qualitative and quantitative analysis of miscompilation bugs that we found in the Solidity compiler. We classify miscompilation bugs found by SolSmith based on their nature, root-causes, and impact on end-users. This sheds light on some pitfalls of optimizing compilers.
This working paper introduces selected findings from Flow Extraction Theory (FET), an independent research program studying economic-state representation in decentralized financial systems. The paper argues that event history is not equivalent to state, and that observed pressure is not equivalent to explained pressure. Using a bounded Aave V3 case study at Ethereum block 20,000,000, the paper distinguishes historical event evidence, frozen protocol state, token-level representation, account-level aggregate outputs, inference, and unknowns. The study shows that event-derived reconstruction can disagree with exact frozen state, and that health-factor distance can be observed with high confidence while the evidence required to explain that distance remains incomplete. The paper introduces representation risk as the risk created when different evidence classes are collapsed into one operational view of “state.” This public version summarizes selected findings only. It does not disclose implementation details, private tooling, execution logic, complete artifacts, or trading signals.
This chapter examines the ways in which blockchain smart contracts and responsible artificial intelligence (AI) are transforming many sectors. At the moment, typical contracts in industries like manufacturing or supply chains face several inefficiencies, delays, and the possibility of errors or even fraud. The issue is that those smart contracts lack the intelligence required for real-world scenarios where things are constantly changing, even though blockchain has helped by making things more automated and transparent. The idea here is to make blockchain contracts less rigid by incorporating AI and real-time data analysis. Contracts would adjust in response to events rather than simply adhering to predetermined guidelines. Additionally, the chapter explores how smart contracts are established by fusing AI tools, data feeds from services like Chainlink, and platforms like Ethereum. In general, it involves creating systems that are responsible and intelligent, which seems to be the only viable option at the moment. This chapter examines the evolution of AI in industrial contexts, analyzing its role before and after the integration of smart contracts. It presents relevant industrial case studies, applies responsible AI principles to the development of blockchain-based smart contracts, and underscores the adoption of international frameworks and standards to promote ethical, transparent, and accountable implementation across industries.
Ethereum Layer-2 (L2) ecosystems improve scalability but also fragment users, liquidity, gas funding, and execution across rollups. Consequently, cross-rollup interoperability is not only a bridging problem but also a wallet, execution, and validation problem. Ethereum Interop Layer (EIL) proposes a voucher-based architecture in which users create voucher requests on an origin chain and redeem XLP-signed vouchers on a destination chain. When reproducing the evaluated SDK version in a controlled local environment, we observed a compatibility issue in the \texttt{UserOperation} path: paymaster-related data can differ after signing, preventing a stable comparison between the user-authorized representation and the representation later inspected by the local validation flow. This paper presents a reproducible two-L2 validation framework and a controlled compatibility mitigation for that issue. We build a deterministic local testbed over Arbitrum- and Optimism-style development chains, deploy the core paymaster and bridge-related components, implement mock bundlers and event-driven XLP providers, and introduce a sanitized paymaster-data handling path together with a compatible multichain account wrapper. Using this framework, we execute the core voucher lifecycle from request creation to destination-chain voucher redemption and asset release. The contribution is an empirical diagnosis of an implementation-level compatibility barrier, a bounded mitigation that restores controlled end-to-end execution, and an inspectable validation artifact for studying voucher-based interoperability. The work does not claim a new interoperability protocol, universal wallet compatibility, or production readiness; it identifies the remaining gaps toward standard-account validation, one-signature multichain authorization, and full dispute-settlement support.
This paper introduces Crossroads, a smart contract layer for chain-abstracted assets. In Crossroads, assets from nearly any chain are represented on a single backend blockchain as ERC-20 tokens. As a result, any asset can participate in smart-contract-based exchange, lending, or privacy applications on a single unified platform. So while Crossroads offers cross-chain bridging, a common, partial approach to alleviating the fragmentation of the blockchain ecosystem today, this is just one service within Crossroads' general-purpose chain-abstraction model. Crossroads relies on key encumbrance: a threshold signing committee holds encumbered keys controlling assets on each integrated chain, signing transactions only as authorized by smart contracts on the backend blockchain. Asset movements are fee-efficient, as ownership changes are recorded on the backend blockchain and users may set the transaction fee for withdrawals. Crossroads enables permissionless, modular integration of new blockchains using pluggable oracles with flexible design options (zkBridge, TEE-based, hybrid). Asset deposits into Crossroads benefit from strong, chain-specific finalization guarantees, minimizing the risk of reorg attacks. Unlike existing bridges, however, third-party smart contracts in Crossroads can provide fast, optimistic access to funds before finalization completes. We prove that Crossroads satisfies soundness: given an honest quorum of signing committee members, any user can unilaterally generate a withdrawal transaction transferring their net balance to an account on an integrated blockchain. We implement a proof of concept across multiple public blockchains: Bitcoin, Ethereum, and Solana. We catalog a range of applications enabled by Crossroads, including universal wallets, cross-chain staking and lending, privacy-preserving payments, and private management of public blockchain assets.
Educational institutions require secure, transparent, and tamper-resistant systems to manage academic records, examination data, and student results while ensuring accountability and data integrity. Conventional marks management systems primarily rely on centralized databases, making them susceptible to unauthorized modifications, security breaches, limited traceability, and single points of failure. The proposed blockchain-based university student marks management framework utilizes academic information collected from institutional administrative records, including student details, faculty information, academic structures, subject allocations, examination schedules, marks, and result data. The workflow incorporates secure user authentication using SHA-256 hashing, AES-based encryption of sensitive marks data, role-based access control, blockchain transaction validation, and smart contract execution for academic operations. Ethereum blockchain, Solidity smart contracts, Flask, Web3.py, MetaMask, and Ganache are integrated to implement secure record management, immutable storage, result publication, audit trail generation, and academic analytics. Performance evaluation is conducted using blockchain transaction processing, encryption efficiency, data integrity verification, access control validation, audit traceability, and result dissemination correctness. Experimental results demonstrate reliable storage of academic records, secure handling of examination information, accurate result processing, comprehensive audit logging, and effective protection against unauthorized modifications while maintaining complete transaction transparency. The proposed architecture significantly enhances the security, reliability, transparency, and trustworthiness of university examination and academic record management systems.
The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.