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.
Zero-knowledge proof systems rely on a trusted setup phase to generate a Common Reference String (CRS), yet existing approaches are typically static, one-time ceremonies that are inflexible and vulnerable to long-term compromise. Offloading continuous, recurring trusted setups to a decentralized Layer 2 (L2) network introduces a fundamental coordination challenge arising from the mismatch between high-throughput transaction processing and the multi-round requirements of trusted setup ceremonies. This paper presents an L2-coordinated framework that safely decouples transaction pipelines from ceremony execution to achieve automated, continuous CRS generation without centralized coordination. We design and implement two protocol variants over a decentralized, PBFT-coordinated ZK-rollup architecture: an on-chain smart contract approach and an asynchronous peer-to-peer consensus variant. Both designs utilize non-interactive zero-knowledge proofs of knowledge alongside commit-reveal structures to eliminate adaptive manipulation vectors and isolate ceremony latency. Experimental evaluations under simulated wide-area network constraints and adversarial conditions demonstrate that our architecture successfully isolates ceremony liveness. Continuous setups complete reliably within practical time bounds despite node dropouts or malicious contributions, while preserving stable L2 transaction throughput.
Applying NFTs to urban planning offers revolutionary potential but raises serious equity concerns. Although NFTs are linked to digital art speculation, their underlying technology, decentralized ownership, smart contracts, and tamper-proof records could transform participatory planning, land-use management, and community asset stewardship. This article discusses how NFTs could decentralize planning power by enabling tokenized community land trusts, zoning-compliance automation, and neighborhood DAOs (Decentralized Autonomous Organizations). However, they also pose dangers, such as digital exclusion, environmental degradation from energy-hungry blockchains, and capture by technocratic elites. Mitigation is needed through hybrid digital-physical participation systems, public sector-led blockchain infrastructure, and equity-sensitive token design if these tools are to benefit the public interest. The article proposes a staged implementation approach, beginning with controlled pilots (e.g., NFT-tracked community benefit agreements) before scaling up to more advanced applications such as dynamic zoning regimes. Lastly, ethical use of NFTs in planning demands visionary public-sector leadership to avert the entrenchment of existing inequalities in the name of innovation. Planners must critically engage with this emerging technology not as eager consumers but as stewards shaping its trajectory toward more equitable, open, and democratic urban futures. The question is not whether NFTs will influence cities, but who will determine that influence and to what ends.
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.
Smart contracts manage high-value digital assets, making their security a critical priority. In this work, we present a preliminary ecosystem analysis of how smart contract vulnerabilities are currently classified, disclosed, and managed across academia and industry. Our findings reveal the fragmented nature of Web3 security, characterized by a history of attempted classification schemes and a lack of proper vulnerability disclosure. We propose several hypotheses for this divergence from traditional software standards, including ideological decentralization, reputation management, and misaligned financial incentives. A case study of Uniswap illustrates these challenges, revealing inconsistent reporting and the difficulty of verifying vulnerability data. Ultimately, this work serves as a foundational step toward establishing unified methodologies for the detection, management, and disclosure of smart contract vulnerabilities.
Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value.
Jin Ah Seo, Kun Hwa Lee, Vijayan Sugumaran, Jo Yeon Park · 5 authors
We build and evaluate a concrete Zero-Knowledge Machine Learning (ZKML)-based pipeline for epidemic diagnosis and show that it can enforce computational integrity without exposing raw medical data in a Web3 setting. In response to security challenges posed by centralized data handling in medical AI applications, particularly during public health crises such as COVID-19, ZKML offers a privacy-preserving alternative by combining machine learning and Zero-Knowledge Proofs (ZKP). We experimentally applied ZKML to a CNN (Convolutional Neural Networks)-based COVID-19 diagnostic model, achieving 87% accuracy and 0.35 loss. All proof generation and verification processes were executed entirely off-chain, with the verified outputs represented as committed public_vals recorded on-chain via smart contracts. To ensure authenticity, the system enforces dual ECDSA signature verification from both the model provider and the data provider. This mechanism prevents unauthorized submissions and confirms the validity of the result before it is stored on-chain. The system was tested under both normal and adversarial conditions, demonstrating robust and reliable operation. By enabling decentralized trust and self-sovereign control over data, this architecture aligns well with Web3 principles. The results indicate that ZKML can support the development of privacy-preserving and verifiable AI systems.
Open access
Adversarial Robustness in Machine Learning
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
SPECTRA is a static-analysis framework for Solidity contracts that integrates symbolic execution, seven-domain abstract interpretation, semantic vulnerability detection, specification synthesis, SMT validation, and CEGAR refinement. Across 220 contracts, SPECTRA analyzes 216 (98.18%), infers 1,381 function-level specifications (6.39 per successful contract), and reports 673 vulnerability findings in 192 contracts (88.89%), with 0.70 s median runtime. These results indicate that specification-oriented analysis remains practical at benchmark scale.
Flawed funds allocation logic in DeFi contracts can result in disproportionate token distribution, reflecting fundamental errors in how contracts determine and assign user payouts. These issues extend beyond funds allocation logic and affect token distribution mechanisms. Existing defenses such as static analyzers and runtime detectors fail to capture these behaviors because each operation appears valid in isolation. In this work, we present AllocScope, an auditor-centric static analysis framework that identifies allocation manipulation vulnerabilities by modeling allocation-related fund flow semantics and generating vulnerability findings for auditors. AllocScope constructs a funds allocation graph to track the relationship between user contributions and received payouts, and identifies logic that result in unfair outcomes. Evaluated on over 8,000 real-world contracts, AllocScope achieves zero false negatives. A user study with experienced auditors confirms that its findings are accurate, actionable, and easy to integrate into standard auditing workflows.
Purpose This study develops a pricing and contract design framework for cryptocurrency catastrophe (CAT) bonds to transfer extreme crypto-native risks, including protocol exploits, exchange breaches and decentralized finance (DeFi) failures, to capital markets. The paper aims to address arbitrage-free valuation, sponsor-optimal contract design and trustless settlement under the unique informational and operational features of blockchain systems. Design/methodology/approach We propose a multi-trigger crypto CAT bond structure that jointly captures short-term catastrophic shocks and long-term systemic deterioration through oracle-reported loss metrics. An arbitrage-free valuation framework is developed under an incomplete market setting using the minimal martingale measure, while sponsor-optimal contract design is formulated under a dual-measure framework. Empirically, crypto loss dynamics are modeled using generalized extreme value distributions and copula-based dependence structures, whereas financial risk factors are modeled through ARIMA–GARCH and vine copulas. A smart-contract-enabled on-chain settlement architecture is further introduced to automate trigger evaluation and cash-flow execution. Findings Empirical results based on REKT crypto incident data demonstrate strong dependence between monthly extreme and aggregate losses, with heterogeneous dependence structures across blockchain ecosystems. Simulation studies show that trigger and principal repayment designs substantially affect bond price distributions and tail risk exposures. Conservative trigger structures generate more stable bond valuations, whereas aggressive structures exhibit greater downside dispersion. The proposed framework supports economically viable risk transfer while enabling transparent and timely settlement through blockchain-based execution. Originality/value This study develops, to the best of our knowledge, the first integrated framework for crypto native catastrophe bonds that combines arbitrage-free pricing, sponsor optimal contract design and smart contract-based on-chain settlement. Unlike traditional CAT bonds or cyber insurance-linked securities the proposed framework explicitly incorporates oracle-based observability, crypto-specific dependence structures and automated settlement, providing a novel mechanism for transferring systemic digital asset risks to capital markets.
Ilham Qasse, Po-Yu Tseng, Mohammad Hamdaqa, Gísli Hjálmtýsson
Smart contract security audit reports contain rich information about vulnerabilities and code quality issues in Web3 projects. However, these reports are scattered across different sources and formats, making large-scale analysis difficult. We present SCAR (Smart Contract Audit Repository), an open-source dataset and tool that automatically aggregates these audit reports. SCAR crawls reports from leading security firms (e.g., OpenZeppelin) and community contests (e.g., Code4rena), parses them into a structured JSON schema, and offers a queryable API for accessing the data. Its pipeline includes a crawler, a text-mining module to standardize findings (e.g., vulnerability types, severity, code references), and a web API for retrieving insights. With hundreds of audits covering thousands of issues, SCAR enables empirical studies of smart contract vulnerabilities at scale. The SCAR project repository is available on GitHub, and the screencast demo is available at this link.
Sai Srikanth Madugula, Peplluis Esteva De La Rosa, Daya Shankar
Decentralized Finance (DeFi) lending protocols currently rely on heuristic, utilization-based bonding curves that mandate severe over-collateralization, systematically excluding under-collateralized assets like corporate invoices. This paper introduces a mathematically optimal pricing mechanism for decentralized credit: the Reverse Kelly Automated Market Maker (rkAMM), the core engine of our proposed lending framework. By inverting the Kelly Criterion, traditionally used for optimal bet sizing, we construct a dynamic interest rate discovery protocol that explicitly prices individual loan risk. The rkAMM ingests real-time Probability of Default (PD) streams from an off-chain Explainable AI oracle and dynamically calculates the exact interest rate required to sustain target liquidity provider (LP) yields. We mathematically derive the Reverse Kelly pricing function ($r = \frac{y + PD}{1 - PD}$), proving its strictly convex superiority over Aave and Compound's static utilization curves in managing capital efficiency. Furthermore, we deploy the rkAMM architecture via Solidity smart contracts, optimizing for gas-efficient 1e18 (WAD) floating-point arithmetic. To ensure decentralized transparency, our simulation infrastructure leverages MLflow for tracking yield hyperparameters, Data Version Control (DVC) linked to DagsHub for versioning Real-World Asset (RWA) data arrays, and localized edge-inference via Ollama (Llama-3) and Hugging Face (FinBERT) for zero-cost predictive modeling. Monte Carlo simulations across 10,000 macroeconomic stress scenarios confirm that the rkAMM maintains protocol solvency and stabilizes LP yields at 12-15\% net of expected credit losses. This work provides the foundational financial engineering required to bridge the \$2 trillion global supply chain finance gap using permissionless blockchain infrastructure.
Trusted execution environment (TEE) based confidential smart contract networks promise privacy but remain vulnerable to storage access pattern attacks that can link senders and recipients in token transfers. When contracts update recipient balances during transfers, the unique storage keys accessed reveal transaction relationships even when data is encrypted. This paper introduces two novel data structures to address this vulnerability: the Delayed Write Buffer (DWB) and the Bitwise-Trie of Bucketed Entries (BTBE). The DWB delays recipient balance updates by buffering pending transfers and randomly settling entries, breaking the direct correlation between transfer execution and recipient storage access. The BTBE further enhances privacy by grouping addresses into constant-sized buckets, preventing flooding attacks and creating anonymity sets for balance queries. Additionally, we present a private notification system enabling real-time, privacy-preserving push notifications for confidential contracts. Our domain-specific approach leverages the unique characteristics of token transfers -- asymmetric balance updates and tolerance for delayed settlement -- to achieve practical performance with probabilistic anonymity guarantees.
Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.
Tanishka Ahire, Jyotsana Bagul, Dr. Archana Bendale
Abstract: The idea of cryptocurrency is really interesting. It started as a money idea and now it is changing how the world thinks about money and technology. Cryptocurrency began with Bitcoin in 2008. Now it includes ideas like blockchain and special kinds of contracts. There are also kinds of money from central banks and unique digital things called NFTs. This paper looks closely at the technology behind cryptocurrency. How it affects the economy, people and laws. It talks about the things that cryptocurrency can do which will probably help it grow. It also talks about the problems that cryptocurrency is facing which might slow it down. The paper looks at what might happen with cryptocurrency in the future and how it will affect the world and money systems. After looking at a lot of research from 2008 to 2023 it seems that cryptocurrency is a concept that could be really big, in the future. For it to really work some technical and other issues need to be figured out. Cryptocurrency has to deal with these issues to be sustainable. The idea of cryptocurrency is still very promising. It needs to solve some problems.. Keywords: Cryptocurrency, Blockchain Technology, Decentralized Finance (DeFi), Smart Contracts, Consensus Mechanisms
Morena Barboni, Filippo Lampa, Andrea Morichetta, Andrea Polini
The immutability of deployed smart contracts increases the impact of undetected faults. Mutation testing evaluates test suite effectiveness by injecting controlled faults (mutants) into contract code and observing whether existing tests detect them. However, the number of generated mutants and the required test executions make mutation testing costly, limiting its scalability in realistic settings. We present ASCENT , an online test prioritization technique based on a Neural Monte Carlo Tree Search (Neural-MCTS) algorithm that dynamically learns and adapts test execution strategies during mutation analysis. The approach prioritizes tests in real time without requiring prior knowledge of the system under test, reducing execution cost while preserving fault detection effectiveness. This paper provides a comprehensive evaluation across five real-world Solidity projects. We analyze behavior under a wide range of hyperparameter configurations, examine how different state representations influence prioritization performance, and evaluate an asynchronous execution model that decouples search, inference, and training to reduce wall-clock execution time. The results show consistent reductions across all projects, ranging from 28–61% depending on project features and hyperparameter configurations, alongside a parallelized variant that substantially reduces wall-clock execution time with controlled trade-offs in prioritization performance.
Semia Guesmi, Alberto Casagrande, Carla Piazza, Sabina Rossi
Maximal Extractable Value (MEV) refers to the maximum profit that Blockchain users–including miners and validators–can extract through strategic transaction manipulation and block reordering. With the rise of smart contracts, MEV has become increasingly pervasive, distorting the intended behavior of smart contracts and accumulating to billions of dollars annually. In response, Flashbots has emerged as a community focused on developing solutions to mitigate MEV. These solutions primarily rely on private pools of users and miners, bypassing the public mempool to protect transactions by directly proposing new blocks. However, it has been observed that such approaches merely shift the problem to a different level, redistributing MEV gains rather than eliminating them. In this paper, we take the perspective of a user preparing to deploy a new smart contract on the Blockchain, aiming to understand how their contract might become a target for MEV and how modifications could reduce the risk of exploitation. Specifically, we explore the use of noninterference, formalized through unwinding conditions, to identify potential sources of MEV, while leveraging oracles and other techniques to create an additional verification layer that can be integrated before executing high-value transactions. We demonstrate our methodology through a case study of a betting contract, using formal modeling to show both the strengths and limitations of our approach.
This study examines the transformation of artistic ownership in fine arts through blockchain-based non-fungible tokens (NFTs). It explores how NFTs reshape traditional systems of provenance, authenticity, and value creation by decentralising ownership verification and embedding transaction records within blockchain infrastructure. Using a qualitative research design based on secondary data analysis, the study synthesises findings from academic literature, industry reports, and case studies of NFT marketplaces and institutional adoption. The analysis is grounded in Actor-Network Theory, Institutional Theory, and Cultural Economics, enabling a multidimensional interpretation of technological, institutional, and economic change. Findings indicate that NFTs reconfigure artistic ownership through programmable smart contracts, disrupt traditional intermediary roles in the art world, and introduce new forms of digital scarcity that drive speculative valuation. However, challenges such as regulatory ambiguity, environmental concerns, and market volatility remain significant. The study concludes that NFTs represent not merely a technological innovation but a structural transformation of ownership systems in contemporary fine arts. Keywords: Blockchain, NFTs, Artistic Ownership, Fine Arts, Digital Art Markets, Cultural Economics, Institutional Change
Zhengqing Liu, Alberto Sonnino, Igor Zablotchi, Eleftherios Kokoris-Kogias · 5 authors
Modern blockchains rely on a modular architecture that decouples consensus from execution. Recent advances in consensus algorithms have shifted the bottleneck to the execution layer, which must deterministically follow the consensus order and handle increasingly complex, compute-intensive smart contracts. We identify that single-node validators cannot keep up, motivating the need for a scale-out design. We design Remora, a scale-out smart contract execution engine. Remora adopts an efficient asymmetric architecture with centralized transaction dispatching and distributed execution, and depends on an object versioning scheme with a strict ownership model to guarantee deterministic scale-out execution. Remora achieves up to 3x throughput improvement compared to state-of-the-art deterministic execution schemes, scales up to 250k TPS, matching modern consensus performance, and reduces latency by up to 5ms. We also show that Remora elastically adapts to bursty workloads and dynamic access patterns using real-world traces. Remora's main performance benefits come from a novel stateless-stateful separation during smart contract execution, which overlaps the execution of state-independent tasks with consensus, and a new locality-aware and load-balanced scheduling scheme.
Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existing LLM-based methods struggle to generate this knowledge automatically: prompt-based methods rely on manually crafted detection rules, while fine-tuning requires massive labeled datasets that are inherently scarce in this domain. We present EvoVuln, an automated framework that reformulates vulnerability detection as a procedural knowledge evolution problem, synthesizing and refining detection logic using only a minimal number of labeled samples. To achieve this, EvoVuln introduces two key mechanisms. First, a Runtime with an Inversion of Control (IoC) architecture compiles detection rules into Executable Policies. This strictly decouples deterministic control flow from LLM semantic reasoning, ensuring faithful logical adherence and producing dense diagnostic telemetry for precise error localization. Second, a two-phase evolution pipeline refines the rule via abductive semantic debugging without any parameter updates: Cold Start bootstraps and stress-tests an initial rule using auto-synthesized corner cases; Few-Shot Evolving then grounds the policy in real-world semantics using only five vulnerable and five safe examples per vulnerability type. Evaluated across five real-world vulnerability types, EvoVuln achieves a 71% macro-average F1-score, outperforming all baselines. The evolved procedural knowledge is portable across models: it enables a lightweight, low-cost model to surpass a much larger zero-shot model by 19 percentage points, and transfers to other LLMs without retraining, at a one-time evolution cost under $50.
ASEGUNLOLUWA E. BABALOLA, DAVID O. ILESANMI, PREYE ADEOLA
Electronic voting can improve the speed of ballot processing and result generation, but conventional systems often depend on centrally controlled infrastructure that may create concerns relating to record alteration, transparency and administrative control. This study presents the development of a blockchain based electronic voting prototype that integrates election creation, candidate management, voter address authorization, ballot submission and result retrieval within a web application. The system adopts an Ethereum based architecture comprising a Next.js user interface, Web3 communication, MetaMask wallet connection, Solidity smart contracts and a local blockchain environment provided by Ganache. A factory smart contract is used to create separate election contracts, enabling each election to maintain its own candidates, authorized voter addresses, election status and vote totals. Before a ballot is accepted, the relevant election contract verifies that the election is active, that the submitting address is authorized and that the address has not previously voted. The developed prototype provides interfaces for election creation, voting and result presentation, demonstrating the integration of the web application with the smart contract and blockchain components. The study provides a basis for the independent management of multiple elections through separate smart contract instances.
Smart Contracts participate in contract performance by converting contractual terms into executable code, presenting new challenges to traditional contract law. Portugal’s current contract legislation lacks explicit provisions for Smart Contracts, with key legal issues including the determination of contract formation and validity, the coordination between automatic execution mechanisms and contractual remedies, and liability allocation for technical risks. Through analyzing Portugal’s contract law framework and the technical characteristics of Smart Contracts, as well as referencing international legislative and judicial experiences, this study proposes an applicable solution within Portugal's existing contract law system. By maintaining technical neutrality and contractual functionality, Smart Contracts can achieve harmonious application through holistic assessment of conduct, differentiated execution remedies, and tiered liability allocation.