<p>This paper examines the evolving challenges of intellectual property (IP) rights protection in the Metaverse, with particular focus on the intersection of traditional IP frameworks with emerging Non-fungible token (NFT) technology. While existing research has explored digital IP rights broadly, the unique characteristics of the Metaverse—its decentralized nature, platform interoperability, and novel ownership models—present unprecedented legal and technical challenges that warrant systematic investigation. The research addresses three key questions: 1) How do traditional territorial-based IP frameworks adapt to the borderless nature of the Metaverse? 2) What specific challenges does NFT technology present for trademark and copyright protection? 3) How can existing legal mechanisms be enhanced to provide effective IP protection in virtual environments? Through comparative analysis of recent case law from American and European courts, supported by examination of regulatory frameworks and technological capabilities, this study identifies critical gaps in current IP protection mechanisms. The analysis reveals that while NFTs offer new possibilities for digital rights management, their implementation raises complex issues regarding ownership verification, cross-platform rights enforcement, and jurisdictional authority. The paper proposes a three-tier framework for enhancing IP protection in the Metaverse: 1) Technical solutions incorporating blockchain-based verification systems with practical implementation considerations; 2) legal adaptations to address jurisdictional challenges; and 3) platform-level governance mechanisms. Each tier is evaluated against current technological constraints and legal precedents to ensure practical viability. This research contributes to both academic discourse and practical policy development by providing a structured analysis of Metaverse IP challenges and offering implementable solutions that balance innovation with rights protection. The findings suggest that effective IP protection in the Metaverse requires a hybrid approach combining adapted legal frameworks, technological solutions, and platform cooperation.</p>
We present a communication-efficient zero-knowledge proof of knowledge for the factorization of Blum integers, a special class of integers of the form n = p q , where p and q are distinct prime numbers satisfying p ≡ q ≡ 3 mod 4 and p ≃ q ≃ n . Existing protocols for proving such statements often incur significant communication costs, especially when demonstrating that p and q are of nearly equal size. We leverage the MPC-in-the-head paradigm, a cryptographic technique that transforms secure multi-party computation protocols into efficient zero-knowledge proof systems. In our protocol, the prover uses additive sharing of p and q over the integers. This approach simplifies proving the size relationship p ≃ q ≃ n and the congruence p ≡ q ≡ 3 mod 4 without requiring costly range proofs. To verify the primality of p and q , we employ the Boneh-Franklin biprimality test. Our protocol achieves a significant reduction in communication complexity. For a 2048-bit integer n and 128-bit security, we construct a proof as small as 12.3 KB, with prover and verifier computational costs comparable to existing protocols that require over 131 KB.
In this paper, the third part of a comprehensive study on blockchain's role in intellectual property management, we interviewed 27 experts to explore blockchain's impact on managing the intellectual property life cycle that is how copyrights, trademarks, trade secrets, and patents are created, protected, managed, enforced, and monetized. Semi-structured interviews provided insights into the benefits and limitations of blockchain in managing intellectual property. Attributes were clustered using a meta-matrix validated by expert feedback. Participants shared views on barriers to blockchain adoption in intellectual property and predicted its evolution over the next 5–10 years. Our findings highlight advantages such as proof of authenticity and ownership, smart contracts, tokenization (including non-fungible tokens or NFTs), and legal protection. However, challenges like scalability, interoperability, lack of a blockchain-intellectual property ecosystem, and limited use cases must be addressed to foster adoption. We summarized implications and recommendations for future research.
Cloud computing is popular in modern businesses because it is scalable, versatile, and cost-effective. Due to the spread and complexity of cloud infrastructures, fault tolerance is crucial. This research focuses on improved cloud failure tolerance using Distributed Ledger Technology (DLT). Decentralised and immutable DLT offers verifiable and transparent transaction history, data consistency, and decentralised recovery, promising to improve cloud infrastructure stability and robustness. This study examines how DLT improves cloud-based detection, recovery, and fault tolerance. Using blockchain and cloud services to create a fault-tolerant system is novel. Comparison of fault tolerance solutions in existing cloud settings with DLT integration is the plan. Data was obtained during failure testing simulations using genuine cloud infrastructure platforms and blockchain networks. Recovery time, defect detection, energy utilisation, and uptime were measured. Graphviz was used to create flowcharts, and Matplotlib was utilised for graphs and plots. Test results show improved fault tolerance due to lower fault impact and faster recovery from DLT. DLT can make cloud systems more fault-tolerant and resilient, setting the pace for distributed computing innovation, the research concludes.
Non-fungible token (NFT) is a relatively new and developing trend in digital finance research that is fundamentally transforming the process of trading digital assets. The objective of this study is to present a concise overview and analysis of current research trends regarding NFT, with the intention of providing researchers with a framework for guiding future investigations in this field. In order to give researchers a framework for directing future studies in this area, the study objective is to provide a succinct review and analysis of current research trends. The objective of the study is to present a concise overview and analysis of current NFT research trends. We started by arranging framework of bibliometric study, setting appropriate keywords and operating the software. In the process, this study thoroughly examined 99 academic publications about NFTs that were listed in the Scopus database between 2009 and 2024 using VOSviewer. Our research shows that the analysis identifies four separate research clusters with a focus on digital economy, security, connections with other blockchains, and intellectual property. We conclude by making recommendations for the future based on the trends and gaps found in this bibliometric review.
This study introduces simple yet effective continuous- and discrete-variable quantum neural network (QNN) models as a transfer-learning approach for forecasting tasks. The CV-QNN features a single quantum layer with two qubits to establish entanglement and utilizes a minimal set of quantum gates, including displacement, rotation, beam splitter, squeezing, and a non-Gaussian cubic-phase gate, with a maximum of eight trainable parameters. A key advantage of this model is its ability to be trained on a single dataset, after which the learned parameters can be transferred to other forecasting problems with little to no fine-tuning. Initially trained on the Kurdistan load demand dataset, the model's frozen parameters are successfully applied to various forecasting tasks, including energy consumption, traffic flow, weather conditions, and cryptocurrency price prediction, demonstrating strong performance. Furthermore, the study introduces a discrete-variable quantum model with an equivalent 2- and 4-wire configuration and presents a performance assessment, showing good but relatively lower effectiveness compared to the continuous-variable model.
Sergio Demian Lerner, Martin Jonas, Ariel Futoransky
The BitVM and BitVMX protocols have long relied on inefficient one-time signature (OTS) schemes like Lamport and Winternitz for signing program inputs. These schemes exhibit significant storage overheads, hindering their practical application. This paper introduces ESSPI, an optimized method leveraging ECDSA/Schnorr signatures to sign the BitVMX program input. With Schnorr signatures we achieve an optimal 1:1 data expansion, compared to the current known best ratio of 1:200 based on Winternitz signatures. To accomplish this we introduce 4 innovations to BitVMX: (1) a modification of the BitVMX CPU, adding a challengeable hashing core to it, (2) a new partition-based search to detect fraud during hashing, (3) a new enhanced transaction DAG with added data-carrying transactions with a fraud-verifying smart-contract and (4) a novel timelock-based method for proving data availability to Bitcoin smart contracts. The enhanced BitVMX protocol enables the verification of uncompressed inputs such as SPV proofs, NiPoPoWs, or longer computation integrity proofs, such as STARKs.
In this paper I propose a novel approach to Volume Weighted Average Price (VWAP) execution that addresses two key practical challenges: the need for asset-specific model training and the capture of complex temporal dependencies. Building upon my recent work in dynamic VWAP execution arXiv:2502.18177, I demonstrate that a single neural network trained across multiple assets can achieve performance comparable to or better than traditional asset-specific models. The proposed architecture combines a transformer-based design inspired by arXiv:2406.02486 with path signatures for capturing geometric features of price-volume trajectories, as in arXiv:2406.17890. The empirical analysis, conducted on hourly cryptocurrency trading data from 80 trading pairs, shows that the globally-fitted model with signature features (GFT-Sig) achieves superior performance in both absolute and quadratic VWAP loss metrics compared to asset-specific approaches. Notably, these improvements persist for out-of-sample assets, demonstrating the model's ability to generalize across different market conditions. The results suggest that combining global parameter sharing with signature-based feature extraction provides a scalable and robust approach to VWAP execution, offering significant practical advantages over traditional asset-specific implementations.
Food banks can improve food donation administration, provide real-time inventory tracking, and guarantee compliance with food safety regulations by incorporating blockchain technology. The efficiency, openness, and dependability of food bank supply chains are greatly increased by this integration, leading to more sustainable and successful operations. This study focuses on two primary objectives: identifying key barriers to effective Food bank supply chain (FBSC) operations in blockchain adoption and exploring the interrelationships among these barriers. Barriers were categorized into external and internal frameworks and analyzed using insights from academics and FBs experts. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology was employed to model and quantify the causal relationships among these barriers. DEMATEL's strength lies in its ability to map interdependencies and feedback loops, providing a nuanced understanding of the links between independent and dependent variables in a cause-and-effect network. To address subjectivity and ambiguity in expert opinions during group decision-making, rough theory was integrated with DEMATEL, ensuring a robust approach to handling conflicting perspectives and uncertainty.
The COVID-19 pandemic has had a significant impact on small and medium-sized enterprises (SMEs), leading to disruptions in supply chains, financial losses, and closures. To overcome these challenges, organizations, including those in developing economies like Malaysia, are turning to blockchain technology as a solution to enhance traditional supply chain management frameworks. This study aims to identify the factors that influence the acceptance of blockchain technology among SMEs. By drawing on established adoption theories such as the technology acceptance model (TAM), diffusion of innovation (DOI) theory, and theory of planned behavior (TPB), the researchers developed a research framework. They utilized partial least square structural equation modeling (PLS-SEM) to analyze the causal relationships between different constructs and test their hypotheses. The findings confirmed that the constructs of the technology acceptance model, specifically perceived usefulness, perceived ease of use and attitude were significantly associated with the intention to use blockchain technology. Additionally, the constructs of the diffusion of innovation theory, relative advantage and compatibility, showed significant associations with perceived ease of use, while complexity had a negligible relationship with perceived usefulness and perceived ease of use. The construct of subjective norms from the theory of planned behavior exhibited a significant relationship with perceived usefulness and an insignificant relationship with intention to use. Finally, perceived behavioral control demonstrated a positive relationship with intention to use. The study's findings provide valuable insights for blockchain developers and organizations aiming to make informed decisions regarding the application of blockchain technology as a process innovation in SMEs.
Mohd Anjum, Naoufel Kraïem, Hong Min, Ashit Kumar Dutta · 6 authors
The healthcare industry, aided by technology, leverages the Internet of Things (IoT) paradigm to offer patient/user-related services that are ubiquitous and personalized. The authorized repository stores ubiquitous data for which access-level securities are granted. These security measures ensure that only authorized entities can access patient/user health information, preventing unauthorized entries and data downloads. However, recent sophisticated security and privacy attacks such as data breaches, data integrity issues, and data collusion have raised concerns in the healthcare industry. As healthcare data grows, conventional solutions often fail due to scalability concerns, causing inefficiencies and delays. This is especially true for multi-key authentication. Dependence on conventional access control systems leads to security flaws and authorization errors caused by static user behaviour models. This article introduces an Opportunistic Access Control Scheme (OACS) for leveraging access-level security. This approach is a defendable access control scheme in which the user permissions are based on their requirement and data. After accessing the healthcare record, a centralized IoT security augmentation and assessment is provided. The blockchain records determine and revoke the access grant based on previous access and delegation sequences. This scheme analyses the possible delegation methods for providing precise users with interrupt-free healthcare record access. The blockchain recommendations are analyzed using a trained learning paradigm to provide further access and denials. The proposed method reduces false rates by 11.74%, increases access rates by 13.1%, speeds up access and processing by 12.36% and 13.23%, respectively, and reduces failure rates by 9.94%. The OACS decreases false rates by 10.64%, processing time by 15.62%, and failure rates by 10.95%.
Cryptocurrency in Indonesia has begun to develop and is starting to be widely used by businessmen in Indonesia. This is a phenomenon given the need for accounting treatment for cryptocurrency transactions. This research seeks to explore and test cryptocurrency and blockchain technology with the approach and review of PSAK in Indonesia and focuses on accounting treatment for cryptocurrencies in Indonesia. The purpose of this study is to conduct an accounting study for cryptocurrencies based on PSAK that applies in Indonesia. The study used a review literature model to figure out accounting for cryptocurrencies.
Developing a computer program(smart contract) using reliability, a programming language for Ethereum blockchain. This smart contract will automate and secure the crowdfunding process by enabling druggies to contribute finances to a design, and the finances will be released to the design only when certain conditions are met, icing translucency and trust in the fundraising process. The design leverages the advantages of blockchain, similar as invariability and decentralization, to produce a more effective and dependable crowdfunding system. With vision of Government fund allocation through this platform. Developing a smart contract using reliability on the Ethereum blockchain to produce a secure and automated crowdfunding platform. The platform enables druggies to contribute finances to systems, with finances released only when specific conditions are met. This ensures translucency and trust, as all deals are recorded immutably on the blockchain. Decentralized governance allows contributors to share in backing opinions, enhancing the popular nature of the process. Also, the platform envisions integration with government fund allocation, furnishing a transparent and effective system for managing public finances and reducing the threat of corruption. Overall, the use of blockchain technology ensures a more dependable and responsible crowdfunding system. Harness the power of blockchain technology to produce a more effective, transparent, and secure crowdfunding platform. By automating fund operation through smart contracts and icing translucency through the Ethereum blockchain, we give a result that benefits both private systems and public fund allocation. This innovative approach has the implicit to transfigure crowdfunding and government backing, making fiscal processes more popular and responsible..
Succinct arguments are proof systems that allow a powerful, but untrusted, prover to convince a weak verifier that an input x belongs to a language \(L \in \mathsf {NP}\) , with communication that is much shorter than the \(\mathsf {NP}\) witness. Such arguments, which grew out of the theory literature, are now drawing immense interest also in practice, where a key bottleneck that has arisen is the high computational cost of proving correctness. In this work, we address this problem by constructing succinct arguments for general computations, expressed as Boolean circuits (of bounded fan-in), with a strictly linear size prover. The soundness error of the protocol is an arbitrarily small constant. Prior to this work, succinct arguments were known with a quasi- linear size prover for general Boolean circuits or with linear-size only for arithmetic circuits, defined over large finite fields. In more detail, for every Boolean circuit \(C=C(x,w)\) , we construct an \(O(\log |C|)\) -round argument-system in which the prover can be implemented by a size \(O(|C|)\) Boolean circuit (given as input both the instance x and the witness w ), with arbitrarily small constant soundness error and using \(\mathrm{poly}(\lambda ,\log |C|)\) communication, where \(\lambda\) denotes the security parameter. The verifier can be implemented by a size \(O(|x|) + \mathrm{poly}(\lambda , \log |C|)\) circuit following a size \(O(|C|)\) private pre-processing step, or, alternatively, by using a purely public-coin protocol (with no pre-processing) with a size \(O(|C|)\) verifier. The protocol can be made zero-knowledge using standard techniques (and with similar parameters). The soundness of our protocol is computational and relies on the existence of collision resistant hash functions that can be computed by linear-size circuits, such as those proposed by Applebaum et al. (ITCS, 2017). At the heart of our construction is a new information-theoretic interactive oracle proof ( \(\mathsf {IOP}\) ), an interactive analog of a \(\mathsf {PCP}\) , for circuit satisfiability, with constant prover overhead. The improved efficiency of our \(\mathsf {IOP}\) is obtained by bypassing a barrier faced by prior \(\mathsf {IOP}\) constructions, which needed to (either explicitly or implicitly) encode the entire computation using a multiplication code.
This paper applies the Lévy-GJR-GARCH model to explore the empirical dynamics of Bitcoin, Ethereum, and Ripple. It highlights volatility clustering, pronounced skewness, and high kurtosis in cryptocurrency markets. The study finds that models integrating innovation distributions more accurately capture and explain the volatility processes and tail risks in these assets. Advanced models, especially those accounting for extreme tail-end and asymmetric jump effects, are better suited for adapting to market changes and providing precise risk indicators, effectively identifying potential losses.
Multi-scalar multiplication (MSM) is the primary building block in many pairing-based zero-knowledge proof (ZKP) systems. MSM at large scales has become the main bottleneck in ZKP implementations. Inspired by existing SIMD-accelerated work, we are focused on accelerating MSM computing efficiency using SIMD instructions in a single CPU environment. First, we propose a SIMD-accelerated MSM computing architecture with no write conflicts and constant memory overheads. This architecture utilizes multithreading to achieve task-level and loop-level parallelism and employs a three-tier buffer mechanism to maximize the utilization of the SIMD engine. Instanced with AVX512-IFMA instructions, we implement six SIMD elliptic curve arithmetic engines for different point addition in three coordinate systems and two groups. Moreover, we integrate our AVX-MSM implementation into the libsnark library, naming it AVX-ZK. In more detail, point deduplication and “Three-Stage” memory optimization are proposed to address problems existing in practical applications. Based on the RELIC library, our performance results on the BLS12-381 curve show that our AVX-MSM achieves up to 27.86x speedup over the most popular Pippenger algorithm. Compared with libsnark, our AVX-ZK implementation achieves over 11.53x (up to 20.26x) speedup under standard benchmarks.
Xander Pottier, Thomas De Ruijter, Jonas Bertels, Wouter Legiest · 6 authors
The Multi-Scalar Multiplication (MSM) is the main barrier to accelerating Zero-Knowledge applications. In recent years, hardware acceleration of this algorithm on both FPGA and GPU has become a popular research topic and the subject of a multi-million dollar prize competition (ZPrize). This work presents OPTIMSM: Optimized Processing Through Iterative Multi-Scalar Multiplication. This novel accelerator focuses on the acceleration of the MSM algorithm for any Elliptic Curve (EC) by improving upon the Pippenger algorithm. A new iteration technique is introduced to decouple the required buckets from the window size, resulting in fewer EC computations for the same on-chip memory resources. Furthermore, we combine known optimizations from the literature for the first time to achieve additional latency improvements. Our enhanced MSM implementation significantly reduces computation time, achieving a speedup of up to x12.77 compared to recent FPGA implementations. Specifically, for the BLS12-381 curve, we reduce the computation time for an MSM of size 224 to 914 ms using a single compute unit on the U55C FPGA or to 231 ms using four U55C devices. These results indicate a substantial improvement in efficiency, paving the way for more scalable and efficient Zero-Knowledge proof systems.
Irma Tsuraya Choirinnida, Yoseph Septianus Ronaldo Lopez
The rapid growth of technology has driven the popularity of Non-Fungible Tokens (NFTs), widely regarded as digital assets for showcasing creative projects and offering investment opportunities. Public interest, amplified by influencers, has led to diverse motivations to purchase NFTs, though many consumers remain unaware of the associated risks. This study examines the factors influencing NFT purchase intentions among Indonesian buyers with prior experience. Using purposive sampling, data from 150 respondents were analyzed through multiple linear regression in SPSS software. Key variables include perceptions of price, design quality, Perceived Usefulness, Perceived Ease of Use, and Perceived Risk. Findings reveal that perceptions of price and design quality significantly impact purchase intentions. Perceived Usefulness and Ease of Use highlight consumer acceptance of NFTs as a new technology, while respondents view NFT purchases as beneficial despite acknowledging risks. The study provides valuable insights into consumer behavior in the NFT market, emphasizing the need for greater awareness of risks while identifying key drivers of adoption in this emerging digital landscape.
Lukas Weidener, Fabio Laredo, K. I. Pavan Kumar, Karlin Compton
This study presents a systematic scoping review of delegated voting (DV) in decentralized autonomous organizations (DAOs), focusing on its governance implications, implementation forms, and challenges. DV refers to a mechanism through which token holders transfer their voting rights to other participants, often called delegates, who vote on their behalf. While DV is often adopted to address low participation and mitigate the cognitive burden of direct involvement, the existing literature highlights its potential to exacerbate centralization, particularly when whales or influential networks are disproportionate. This creates tension between the intended efficiency gains of the delegation and the unintended concentration of power. Various implementation models, including off-chain platforms (e.g., Snapshot), hybrid governance architectures, and token-based delegation systems, exhibit distinct trade-offs in transparency, cost, and adaptability. Although innovations such as quadratic voting, weighted delegation constraints, and reputation-based governance show promise for improving fairness and accountability, they also face vulnerabilities, such as gaming, collusion, and high implementation complexity. To explore the diverse approaches to DV, this review organizes and synthesizes key findings from recent scholarly publications examining its implementation, risks, and governance outcomes. Synthesizing insights from 13 publications, this review identifies key governance trade-offs, implementation patterns, and risks associated with DV. It also outlines future research directions, including multi-tiered governance structures and decision-support mechanisms, to guide more inclusive and context-aware DAO governance.
We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that, similarly to “financial LEGO bricks”, are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571) and discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.
Masterminds are entities organizing, coordinating, and orchestrating cryptocurrency pump-and-dump schemes, a form of trade-based manipulation undermining market integrity and causing financial losses for unwitting investors. Previous research detects pump-and-dump activities in the market, predicts the target cryptocurrency, and examines investors and \ac{osn} entities. However, these solutions do not address the root cause of the problem. There is a critical gap in identifying and tracing the masterminds involved in these schemes. In this research, we develop a detection system \textsc{Perseus}, which collects real-time data from the \acs{osn} and cryptocurrency markets. \textsc{Perseus} then constructs temporal attributed graphs that preserve the direction of information diffusion and the structure of the community while leveraging \ac{gnn} to identify the masterminds behind pump-and-dump activities. Our design of \textsc{Perseus} leads to higher F1 scores and precision than the \ac{sota} fraud detection method, achieving fast training and inferring speeds. Deployed in the real world from February 16 to October 9 2024, \textsc{Perseus} successfully detects $438$ masterminds who are efficient in the pump-and-dump information diffusion networks. \textsc{Perseus} provides regulators with an explanation of the risks of masterminds and oversight capabilities to mitigate the pump-and-dump schemes of cryptocurrency.
Ning Xia, Xiaolei Zhao, Yimin Yang, Yixuan Li · 5 authors
This study delves into the tokenization of real-world assets (RWAs) on the blockchain with the objective of augmenting liquidity and refining asset management practices. By conducting an exhaustive analysis of the technical procedures implicated and scrutinizing case studies of existing deployments, this research evaluates the advantages, hurdles, and prospective advancements of blockchain technology in reshaping conventional asset management paradigms.
Smart contracts, predominantly written in Solidity and executed on blockchains like Ethereum, are immutable, making functional correctness paramount: once deployed, bugs and vulnerabilities become permanent. Despite rapid progress in transformer-based code LLMs, existing evaluations of Solidity code completion rely heavily on surface-form metrics (e.g., BLEU, CrystalBLEU) or hand-grading, which poorly correlate with functional correctness. Unlike Python, Solidity lacks large-scale and execution-based benchmarks, hindering systematic assessment and optimization of LLMs for smart contract development. To bridge this research gap, we introduce SolBench, a comprehensive benchmark and automated testing pipeline for Solidity, designed to emphasize functional correctness via differential fuzzing. SolBench contains 28,825 functions from 7,604 contracts collected from Etherscan (genesis to 2024), spanning 10 popular domains. We benchmark 14 diverse LLMs (open/closed, 1.3B to 671B parameters, general/code-specific, with/without reasoning). The dominant failure mode is missing crucial details (e.g., type definitions, state variables) in intra-contract context. Providing full-contract context mitigates this and improves code completion accuracy. However, full-context inference can be prohibitively expensive in practice. Generating outputs with large context windows using state-of-the-art models often incurs significant costs, rendering naive context scaling economically impractical. Crucially, most of a contract is irrelevant to implementing a given function; only a small subset of details is needed. To exploit this, we propose Retrieval-Augmented Repair (RAR), which integrates retrieval into code repair: it uses the executor's error messages to extract only the most relevant snippets from the full contract. RAR sharply reduces input length for function completion, improving accuracy while significantly cutting computational cost. We further analyze retrieval and code repair strategies within RAR, showing substantial improvements in accuracy and efficiency. SolBench and our RAR framework enable principled evaluation and cost-effective improvement of Solidity code generation. Dataset and code are available at https://github.com/ZaoyuChen/SolBench.