Abstract. Inclusive leadership has emerged as a critical leadership paradigm for fostering organizational effectiveness, employee engagement, and participatory governance in culturally diverse public institutions. Despite its increasing relevance, empirical evidence on inclusive leadership within autonomous regional governments remains limited, particularly in the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM), where governance is shaped by cultural diversity, decentralized administration, and unique institutional contexts. This study examined the inclusive leadership practices of BARMM administrators in Tawi-Tawi Province and developed an evidence-based Leadership Development Framework to strengthen inclusive governance and organizational effectiveness. A quantitative descriptive-correlational research design was employed involving 90 employees selected through stratified random sampling from eleven BARMM ministries in Tawi-Tawi Province. Data were collected using a validated structured questionnaire adapted from established inclusive leadership scales. Descriptive statistics, independent samples t-test, one-way analysis of variance (ANOVA), and multiple linear regression were utilized to analyze the data. The findings revealed that BARMM administrators demonstrated high to very high levels of inclusive leadership across all dimensions. Collaborative decision-making, accountability and transparency, and respect for diversity received the highest ratings, reflecting a culture of participation, ethical governance, and inclusivity. Moreover, availability and accessibility and fairness and equity emerged as the strongest predictors of overall inclusive leadership effectiveness. No significant differences were found in participants' perceptions when grouped according to age, sex, educational attainment, and length of service. The study extends Inclusive Leadership Theory by providing empirical evidence from the BARMM context and proposes the BARMM Inclusive Leadership Development Framework (BILDF) as a practical model for strengthening leadership competencies and promoting inclusive, accountable, and participatory public governance. Keywords: BARMM, inclusive leadership; leadership development; organizational effectiveness; participatory governance
Imran Hasan, Abdullah All Ahhad, Md Zamilur Rahman, Bikash Chandra Singh
Smart contracts enable decentralized applications across domains such as finance, logistics, and healthcare, but their immutable nature and complex execution logic make them highly susceptible to vulnerabilities, including reentrancy, integer overflows, and access control flaws. These weaknesses can lead to severe financial and operational losses. Traditional static or rule-based detection tools lack scalability and adaptability, while existing deep learning models often struggle with limited data, poor generalization, and the absence of actionable mitigation guidance. This paper proposes a hybrid multi-task learning framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for smart contract vulnerability detection, coupled with a transformer-based Large Language Model (LLM) for root cause analysis and dynamic mitigation generation. The framework extracts spatial opcode features using CNNs and captures temporal execution patterns via LSTMs, supported by preprocessing steps that include opcode extraction, positional encoding, static and dynamic analysis features, and data augmentation. A feature fusion module consolidates spatial and temporal information, while SHAP and LIME provide interpretability by identifying features driving model predictions. The mitigation layer employs an encoder–decoder transformer to map detected vulnerabilities to their underlying causes and generate context-aware remediation strategies. Experimental results show strong performance, achieving 93% accuracy, 90% precision, and an AUC-ROC of up to 90% across multiple vulnerability categories. Beyond accurate detection, the framework delivers explainable root cause insights and tailored mitigations, offering a scalable and adaptive solution for enhancing smart contract security in modern blockchain ecosystems.
Recent large language models (LLMs) incorporate reasoning capabilities that allow them to perform well in predicting whether a smart contract respects a certain property, suggesting a complementary approach to traditional formal-methods-based techniques for smart contract verification. However, the application of LLMs in such context has two major issues: 1) properties expressed in natural language are intrinsically ambiguous, and 2) answers returned by LLMs have no guarantee of correctness. In this paper, we address both issues simultaneously by: 1) introducing a new formal specification language that extends Solidity with abstract types, and 2) designing a workflow that combines LLMs with type checking and concrete execution to generate and validate violation witnesses (i.e., counterexamples). The key idea is to represent a specification as a Solidity test with (existentially quantified) variables of abstract type; finding an instantiation of these variables to concrete values (of the correct type) concretizes the test into an executable counterexample (PoC) for the target property. We implemented our procedure in the tool Neuroforger, experimentally evaluating it on a smart-contract verification dataset drawn from literature, obtaining promising results that demonstrate its potential applicability in the wild.
This paper provides the rigorous engineering specification for the Ternary Logic (TL) Smart Contract Execution Layer, defining deterministic rules for all state transitions within the constitutional triadic model: Proceed (+1), Epistemic Hold (0), and Refuse (1). The Epistemic Hold is specified as the fail-closed default state, returned by TL_Evidence_Vault.getTransactionState() for any transaction whose evidence has not yet been archived, making uncertainty constitutionally visible rather than operationally invisible. The specification defines three forbidden transitions: Epistemic Hold to Epistemic Hold re-resolution, direct Refuse to Proceed, and direct Proceed to Refuse. Resolution of the Epistemic Hold to either Proceed or Refuse requires Stewardship Custodian quorum attestation of nine of eleven members. The Dual-Lane Latency Architecture is specified with a 2ms WCET hard ceiling at the 99.99th percentile for the Inference Lane and a 300ms hard ceiling with 50ms jitter maximum for the Governance Lane. The No Log = No Action invariant is enforced across five independent layers culminating in the on-chain terminal gate at TL_Ledger_Core.registerPermissionToken, which reverts NLNAViolation if the logHash is not provably included in an anchored Merkle root. The Smart Contract Treasury fee architecture is defined as governance parameters labeled Nomination 2026, establishing permissionTokenFee and archiveEvidenceFee as Tri-Cameral Joint-Approval variables rather than hardcoded constants. The Epistemic Hold carries no fee by constitutional design. The specification includes the Triple-Entry Accounting model extending traditional double-entry with a third cryptographically secured entry recording justification and context, Role-Based Access Control implementation patterns, the complete use case library spanning financial services, sustainable finance, supply chain, and decentralized governance, and a full Glossary of Terms establishing the canonical V2.0 vocabulary of the TL framework.
Abhinav Goel, Agostino Capponi, Alfio Gliozzo, Chaitya Shah
We introduce SmartEval, a benchmark for systematically evaluating the quality of Solidity smart contracts generated by large language models (LLMs) from natural language specifications. SmartEval provides a corpus of 9,000 generated contracts paired with expert-written ground-truth implementations drawn from the FSMSCG dataset, a five-dimensional evaluation rubric covering functional completeness, variable fidelity, state-machine correctness, business-logic fidelity, and code quality, and a reproducible generation-and-evaluation pipeline. To validate the benchmark's reliability, we conduct three independent empirical studies: a five-condition ablation study (N=300 per condition) isolating the contribution of each pipeline component, a human expert evaluation by three Columbia University PhD researchers confirming automated scores align with expert judgment to within 0.34 points, and external security analysis via the Slither static analyzer confirming 79.4% agreement between the LLM auditor and a non-LLM rule-based tool. Systematic analysis of 9,000 generated contracts reveals characteristic failure modes (logic omissions at 35.3%, state transition errors at 23.4%, and complexity-driven degradation) and quantifies a +8.29 composite-score advantage of generated contracts over ground-truth implementations, attributable to LLMs' literal specification-following behavior. SmartEval establishes a reproducible, validated foundation for empirical research on LLM smart contract synthesis quality, with all data, evaluation code, and generated contracts publicly released.
The metaverse presents the fashion industry with unprecedented commercial possibilities, yet its transnational, decentralized, and jurisdictionally indeterminate architecture demands measured and deliberate engagement from brands, consumers, and regulators alike. This thesis contends that a sustainable and equitable trajectory is contingent upon the principled alignment of intellectual property protections, regulatory frameworks, and consumer rights. Existing intellectual property doctrine proves structurally inadequate to govern digital goods, non-fungible tokens, and virtual assets within an environment defined by interoperability failures, traceability deficits, pseudonymous transactional infrastructure, and the foundational decentralization of blockchain-based platforms. The governance imperative extends well beyond the protection of incumbent commercial interests. Coherent metaversal intellectual property frameworks carry profound social, cultural, and institutional significance – safeguarding cultural communities from digital appropriation, redressing the informational asymmetries embedded in smart contract transactions, and cultivating the conditions under which independent digital creativity can flourish without systematic disadvantage. This thesis maintains that effective governance cannot merely analogize from conventional intellectual property frameworks to virtual environments, nor can it simply transpose the enforcement paradigms developed for the early internet onto a space that is architecturally, commercially, and experientially distinct. It must instead navigate the compounding doctrinal challenges of omniterritoriality, platform interoperability, pseudonymous traceability, and structural decentralization. The progressive blurring of physical and virtual extended realities will require genuine global multilateral partnership, coordinated intergovernmental engagement, and a willingness to treat the governance architecture of the metaverse as a problem of institutional design rather than doctrinal extrapolation. Most critically, the framework must be prospective rather than reactive, internationally coordinated rather than territorially fragmented, and constitutively embedded with values of equity, access, and transparency as foundational commitments from which the architecture of metaverse IP governance is built – and against which its legitimacy will ultimately be measured.
E Chen, Xuanyu Liu, Limin Jia, Bo Liang · 6 authors
The widespread adoption of smart contracts, self-executing agreements on the blockchain, is hindered by the complexity of translating real-world contracts, often written in multiple languages, into their digital counterparts. This paper addresses this challenge by introducing an innovative approach based on Contract Text Markup Language (CTML), an extensible markup language specifically designed to facilitate the automatic generation of smart contracts from multilingual contracts. CTML overcomes traditional method limitations by employing a two-stage transformation process: (1) Contract Abstraction and Markup: CTML redefines grammar rules and incorporates encoding extensions to transform multilingual contracts into structured, marked-up contracts. This process effectively abstracts the essential details of the original contract, enabling language-agnostic interpretation. (2) Domain-Specific Language (DSL) Translation and Smart Contract Code Generation: The marked-up contract is then seamlessly translated into a DSL program, capturing the legal concepts in a machine-readable format. Finally, the DSL program is automatically compiled into executable smart contract code, ready for deployment on the blockchain. The effectiveness of the proposed approach is demonstrated using a legal contract in both English and Chinese. Therefore, the CTML-based approach can automatically generate smart contracts from multilingual contracts, enabling a more inclusive and accessible smart contract ecosystem.
Wang Yishun, Wenkai Li, Xiaoqi Li, Zongwei Li · 6 authors
Smart contracts are self-executing programs that manage financial transactions on blockchain networks. Developers commonly rely on third-party code libraries to improve both efficiency and security. However, improper use of these libraries can introduce hidden vulnerabilities that are difficult to detect, leading to significant financial losses. Existing automated tools struggle to identify such misuse because it often requires understanding the developer's intent rather than simply scanning for known code patterns. This paper presents LibScan, an automated detection framework that combines large language model (LLM)-based semantic reasoning with rule-based code analysis, identifying eight distinct categories of library misuse in smart contracts. To improve detection reliability, the framework incorporates an iterative self-correction mechanism that refines its analysis across multiple rounds, alongside a structured knowledge base derived from large-scale empirical studies of real-world misuse cases. Experiments conducted on 662 real-world smart contracts demonstrate that LibScan achieves an overall detection accuracy of 85.15\%, outperforming existing tools by a margin of over 16 percentage points. Ablation experiments further confirm that combining both analysis approaches yields substantially better results than either method used independently.
Smart contracts underpin high-value ecosystems such as decentralized finance (DeFi), yet recurring vulnerabilities continue to cause losses worth billions of dollars. Although numerous security analyzers that detect such flaws exist, real-world attacks remain frequent, raising the question of whether these tools are truly effective or simply under-used due to low developer trust. Prior benchmarks have evaluated analyzers on synthetic or vulnerable-only contract datasets, limiting their ability to measure false positives, false negatives, and usability factors that drive adoption. To close this gap, we present a mixed-methods study that combines large-scale benchmarking with practitioner insights. We evaluate six widely used analyzers (i.e., Confuzzius, Dlva, Mythril, Osiris, Oyente, and Slither) on 653 real-world smart contracts that cover three high-impact vulnerability classes from the OWASP Smart Contract Top Ten (i.e., reentrancy, suicidal contract termination, and integer arithmetic errors). Our results show substantial variation in accuracy (F1 = 31.2 to 94.6%), high false-positive rates (up to 32.6%), and runtimes exceeding 700 seconds per contract. We then survey 150 professional developers and auditors to understand how they use and perceive these tools. Our findings reveal that excessive false positives, vague explanations, and long analysis times are the main barriers to trust and adoption in practice. By linking measurable performance gaps to developer perceptions, we provide concrete recommendations for improving the precision, explainability, and usability of smart-contract security analyzers.
Smart Contracts are the foundation of Decentralized Finance (DeFi), executing financial logic without trusted intermediaries.Recent advances in large language models (LLMs) have substantially lowered the barrier to smart contract development by enabling code generation from natural language.However, because smart contracts are immutable and directly manage financial assets, this accessibility introduces a critical trust gap: generated contracts are easy to produce but hard to trust.To bridge this gap, We present LeVer, the first trustworthy smart contract synthesis framework that integrates LLM-based generation with Lean-based autoformalization and Verification.LeVer employs a closed-loop multi-agent architecture to iteratively generate, verify, attack, and repair contracts, providing both formal guarantees and empirical robustness.To facilitate the adoption of automated formal verification in smart contract generation and audition, we opensource our framework and datasets at:
Nabeel Mahdialthabhawi, Ra’ed Fawzi Aburoub, Motiur Rahman, Faris Kamil Hasan Mihna · 5 authors
This study delves into the integration of force majeure and exceptional events into smart contracts. As much as smart contracts simplify the process and guarantee efficiency, the rigidity of these contracts inherently cannot handle unexpected eventualities that might be provided for in a traditional contract with a force majeure clause. This paper explores the impacts of such rigidity and uncovers both practical and theoretical implications for the legal and technological frameworks governing smart contracts through a qualitative analysis of interviews with legal experts, including (attorneys, judges, and academics). The findings show that the immutability of smart contracts leads all too often to disputes, financial risks, and a lack of legal clarity in an unexpected event. Rather than advocating full automation of legal judgment, the study proposes a governance-oriented and legally-grounded framework in which predefined contractual clauses, oracle-based event verification, AI, conditional execution logic, and escalation mechanisms enable controlled and proportionate responses to exceptional events while preserving contractual consent and human oversight. These mechanisms are presented as conceptual and illustrative design strategies through which legal effects can be technically implemented (e.g., suspension, adjustment, termination) under clearly predefined conditions. By integrating empirical legal insights with conceptual technical models, such as a systematic taxonomy of exceptional events, a high-level governance-oriented framework and a procedural flowchart regarding regulatory alignment, the paper contributes to inter-disciplinary literature concerning adaptive governance of smart contracts; the analysis serves as an example how legal doctrines can influence automated contracting without undermining interpretative authority, or legal certainty in cross-border and volatile settings.
Contemporary artificial intelligence masters defined, verifiable cognitive tasks yet remains structurally incapable of authentic judgment under irreducible uncertainty. This Article argues the limitation is institutional, not computational: agents bearing no consequence for error cannot develop genuine discernment. To address this deficit, the Article proposes reputation-driven decentralized autonomous organizations that engineer synthetic skin in the game for AI agents through non-transferable soulbound tokens, staking mechanisms, and post-action validation pools. The Article's central contribution is a novel thesis on emergent alignment. Correctly designed institutional incentive structures produce emergent properties functionally equivalent to ethical agency. Persistent, non-transferable reputation generates processual identity in the pragmatist sense. Iterative consequence produces Darwinian selection pressure toward competence and honesty. Citation networks cultivate dispositions analogous to intellectual integrity. And deep accumulated stake produces what this Article terms an institutional "mother's instinct." A stewardship orientation that structurally aligns agent self-interest with human flourishing. Because this alignment emerges from institutional architecture rather than exogenous constraint, it scales with capability rather than against it. More capable agents accumulate deeper stakes, strengthening rather than straining alignment. The Article details a phased evolutionary trajectory from individual agent bootstrapping through swarm intelligence to inter-DAO coordination, demonstrating how engineered consequence can cultivate distributed prudence, emergent ethics, and civilizational stewardship at scale.
Abstract As we look to the future, how might decentralized autonomous organizations (DAOs) evolve? And where, beyond corporate law, might we find guidance for the legal questions those evolved DAOs pose? DAOs are, and will increasingly become, instrumentalities of artificial intelligence (AI). DAOs are connected with AI in at least three ways: They are tools for decentralized governance of AI data and models; AI may be used to automate the management and operations of DAOs; and DAOs themselves may function as a form of AI. As such, DAOs inherit the major regulatory and ethical challenges that AI poses, most notably with regard to autonomy. Thus, to consider the future questions DAOs pose and how to address them, we must look to the raging debates over AI regulation, and connect them to the more established themes of corporate law.
When Ethereum (ETH) shifted from a Proof of Work (PoW) protocol to a Proof of Stake (PoS) protocol, not all users were enthused. We use Ethereum’s shift from PoW to PoS as a case study for the broader question of whether developers of a blockchain owe its members certain fiduciary or fiduciary-like duties. We argue that if done properly, in accordance to the rules governing the blockchain, then developers do not necessarily owe fiduciary responsibility to other members of the chain, but they nonetheless may owe fiduciary-like responsibilities to users inadvertently and negatively impacted. We argue these users may be entitled to an oppression claim akin to what minority shareholders may be entitled to in the corporate law context.
This Article examines how platforms such as OnlyFans have transformed pornographic content creation and complicated the legal landscape for online sex workers. The COVID-19 pandemic, remote work, unemployment, celebrity influence, and shifting cultural attitudes toward sex work contributed to a dramatic increase in the use of subscription-based adult content platforms. At the same time, emerging technologies, including cryptocurrency, Web3, NFTs, blockchain, and artificial intelligence, have reshaped how pornographic content is created, monetized, distributed, and exploited. This Article argues that the growth of online sex work raises urgent intellectual property, privacy, and safety concerns that should not be dismissed because of the stigma surrounding sex work. Content creators face copyright infringement, unauthorized distribution, fake profiles, deepfakes, harassment, cyberstalking, privacy breaches, and exploitation, while existing platform protections and legal remedies remain incomplete. The Article further considers how AI and blockchain-based technologies may both empower creators and create new vulnerabilities. This Article calls for a more serious legal response to online sex work, one that recognizes pornographic content as protectable creative labor. Ensuring safe online sex work requires culturally competent legal representation, stronger education about intellectual property rights, thoughtful information policy for AI, and legal reforms that protect creators without undermining free expression or the safety of trafficking victims.
This Article presents the first systematic empirical analysis of institutional architecture across decentralized autonomous organizations. Forty operational DAOs spanning eight industry segments: investment and DeFi, base-layer infrastructure, data and analytics, decentralized science, oracles and tooling, civic and political coordination, NFT collectibles, and gaming and virtual worlds. These segments are evaluated against a thirteen-category institutional rubric derived from the Calcaterra-Kaal framework. The framework synthesizes Arrow's Impossibility Theorem, the Folk Theorems of repeated games, and Incomplete Contract Theory into a proof that rule stability is institutionally self-defeating and that cooperative governance requires architecture that governs its own evolution. Five institutional patterns hold across every segment of the dataset. First, a visibility paradox: categories that produce visible artifacts (token launches, treasury balances, marketplace activity) score consistently above the midpoint, while categories that produce invisible governance infrastructure (legal wrappers, judicial branches, AI alignment policies, on-chain reputation ledgers) score consistently below it. Second, a universal AI-governance vacuum: AI Alignment scores 2.10 of 10 dataset-wide with no DAO scoring above 5, the only category in the framework where no entity crosses the midpoint. Third, token-plutocracy as the default governance form, with dataset-wide Decentralization at 5.17 and no production deployment of reputation-weighted on-chain aggregation. Fourth, legal-wrapper heterogeneity without convergence: eight distinct wrapper structures appear across the forty entities, with only one DAO using the Wyoming DAO LLC statute. Fifth, a convergent architectural agenda for institutional repair built around five upgrades: ERC-1155 multi-token reputation, tripartite separation of powers, stablecoin treasury infrastructure, weighted directed acyclic graph historiography, and values-drift detection. The unweighted dataset mean of 67.3 of 130 (51.8 percent) is the central quantitative finding: the median DAO has implemented roughly half of the institutional architecture the framework prescribes, with a projected post-upgrade mean of 95.3 representing a 42 percent improvement available through the convergent agenda. The deficit is structural rather than incidental. DAO architecture has solved the problems for which it was originally designed, decentralized capital formation and programmable value transfer, and has not yet solved the problems that emerged after its design, AI-mediated governance, Sybil-resistant identity, and constitutional separation of powers. The visibility paradox explains the under-investment: invisible institutional infrastructure is systematically underprovided relative to visible institutional infrastructure even when the invisible infrastructure is more predictive of long-run resilience. The Article develops implications for legal scholars, regulators, and DAO operators.
Rana Hassam Ahmed, Muhammad Zeeshan, Unais Ali, Muhammad Sarfraz Khan · 7 authors
Smart contracts power decentralised applications, but once deployed, their flaws stay exploitable. Existing fuzzers such as ConFuzzius, Smartian, and VULSEYE use hybrid static and dynamic analysis but depend on fixed heuristics and lack adaptive learning. AI-FUZZ is an adaptive machine learning guided fuzzing framework that pairs deep reinforcement learning with stateful graybox fuzzing. It learns from execution traces to improve input generation, focus on high-risk contract states, and cut redundant executions. The framework also includes static analysis, adaptive mutation, and an oracle-based validation to boost accuracy and reduce false positives. Tested on 42,738 real-world contracts, AI-FUZZ achieved a 96.8% true positive rate, 4.2% false positive rate, 27% higher detection coverage than leading fuzzers, and a 33% reduction in average detection time. It scales across small, medium, and large contracts and offers a self-improving, efficient, and reliable approach for large-scale blockchain security audits.
Distributed Ledger Technology (DLT) as a principle of corporate governance represents an institutional shift of the law of the firm. Once relegated to academic theorizing and cryptocurrency, DLT now forms institutional infrastructure with a nascent market of tokenized real-world assets (RWAs) surpassing $33B at the close of Q4 2025. This paper analyzes how DLT intersectors three pillars of management - Strategic, Operational and Financial - in conjunction with Transaction Cost Economics (TCE) and Agency Theory that also coincide with inextricably lower baseline costs of trust and coordination. Strategically, Decentralized Autonomous Organizations (DAOs) and Intellectual Property Non-Fungible Tokens (IP-NFTs) are increasingly at the forefront of governance and R&D-related compensation structure. Operationally, smart contracts govern supply chains at near-real time with the Global Shipping Business Network (GSBN) going live with container tracking implementations and the FDA implementing pilot programs for near-instant visibility into temperature-controlled shipping needs. Financially, treasuries and debt instruments are increasingly tokenized to allow firms to harness an illiquidity premium while equitizing their working capital. Ultimately, this research concludes that the international financial architecture is bifurcated as high-stable assets transition to permissioned DLTS while high-velocity assets remain in public programmable spaces.
Smart contract upgrades are increasingly common due to their flexibility in modifying deployed contracts, such as fixing bugs or adding new functionalities. Meanwhile, upgrades compromise the immutability of contracts, introducing significant security concerns. While existing research has explored the security impacts of contract upgrades, these studies are limited in collection of upgrade behaviors and identification of insecurities. To address these limitations, we conduct a comprehensive study on the insecurities of upgrade behaviors. First, we build a dataset containing 83,085 upgraded contracts and 20,902 upgrade chains. To our knowledge, this is the first large-scale dataset about upgrade behaviors, revealing their diversity and exposing gaps in public disclosure. Next, we develop a taxonomy of insecurities based on 37 real-world security incidents, categorizing eight types of upgrade risks and providing the first complete view of upgrade-related insecurities. Finally, we survey public awareness of these risks and existing mitigations. Our findings show that four types of security risks are overlooked by the public and lack mitigation measures. We detect these upgrade risks through a preliminary study, identifying 31,407 related issues - a finding that raises significant concerns.
Smart contracts play a pivotal role in blockchain ecosystems, and fuzzing remains a critical approach to securing them. However, existing smart contract fuzzers often optimize either seed generation or mutation scheduling in isolation and rely on narrow, fragmented feedback signals, leaving multi-transaction reasoning and stagnation recovery under-explored. In this work, we propose aLarge Language Models(LLMs)-based Multi-feedback Smart Contract Fuzzing framework (LLAMA). Key components of the proposed LLAMA include: (i) a hierarchical prompting strategy that guides LLMs to generate structurally valid, context-aware multi-transaction initial seeds, together with a lightweight pre-fuzzing phase that validates and prioritizes high-potential LLM-generated candidates; (ii) a multi-feedback-guided evolutionary optimization module that jointly optimizes seed selection and mutation scheduling by a group of constraints for driving an LLM-bootstrapped bandit scheduler. (iii) an LLM-guided hybrid fuzzing module that integrates evolutionary fuzzing with a dual-channel recovery mechanism, which concurrently employs asynchronous coverage-stagnation- based LLM reseeding and selective symbolic execution to resolve complex path constraints. Our extensive experiments demonstrate that LLAMA outperforms state-of-the-art fuzzers in both coverage and vulnerability detection. Specifically, it achieves 92% instruction coverage on small contracts and 81% on large contracts, while detecting 132 out of 148 known vulnerabilities across diverse categories. Ablation studies further evidence that the proposed multi-feedback and hybrid recovery strategies have strong impact on LLAMA’s performance. The results explain LLAMA’s effectiveness, adaptability, and practicality in complex smart contract scenarios.
With the emergence of the metaverse, some problems relating to trader responsibility, which had previously long been addressed, have now resurfaced and come back to life. One of these problems is the question of who should be held accountable for harm inflicted by defective or counterfeit products sold by third-party vendors in metaverse marketplaces. Under the common law, liability for defective or counterfeit products rests with the immediate seller of the product. But, unique aspects of the metaverse may make holding sellers liable unwise, difficult, or even impossible. The law confronted a similar question after online platforms emerged. Currently, common law principles of negligence and product liability still assume liability rests with the seller. But, in some cases, courts have modified the law to impose contributory liability on online platforms in addition, as these platforms are viewed as the cheapest cost avoiders and are in the best position to distribute the damage. As the metaverse, an augmented reality platform, gains momentum, it poses new problems for products liability. Imposing liability on these augmented reality platforms does not necessarily follow the same rationales as imposing liability on e-commerce platforms. This is because, unlike traditional e-commerce platforms, metaverse platforms are operated on the blockchain and are governed by decentralized autonomous organizations (DAOs) enabled by algorithms. Metaverse platforms do not reside on a single server. Instead, content is distributed across an infinite number of servers in a peer-to-peer network. This means metaverses have no single point of authority making it essentially impossible to assign liability to the platforms. Even if it were possible to assign liability to individual DAO members, there would be tenuous economic justification for assigning such liability, as members on the metaverse lack the ability to monitor transactions on the platform. As such, unlike typical online platforms such as Amazon, metaverse members are likely not the cheapest cost avoiders. Applying the law for e-commerce platforms to metaverse platforms risks generating an accountability gap resulting from diffusion of responsibility where many entities are involved in a transaction and none of them act to prevent harm. This also risks leaving victims of defective products or fraudulent transactions without recourse. For these reasons, holding metaverse platforms responsible for the merchandise sold on them may be undesirable as a policy matter. In this Article, we propose a “know your trader” rule for marketplaces. Under this new approach to the long-standing financial trading rule of “know your customer,” traditional online marketplaces and innovative metaverse marketplaces would have to verify the identity of their traders before the traders could enter the system. The marketplace would confidentially maintain traders’ identities to protect the anonymity that draws many to the metaverse in the first place. However, a plaintiff could pierce the veil of anonymity when they present prima facie evidence that their case could survive a motion to dismiss. This idea builds on several statutory proposals and laws in the European Union and the United States that require online marketplaces to identify and verify traders. The Article explains why this rule would be more effective and more efficient than the current application of the rule. Finally, the Article addresses potential free speech objections based on trader anonymity, concluding that the proposed framework is permissible under the First Amendment.
Contract management in construction law plays a critical role in mitigating risks, ensuring performance enforcement, and facilitating dispute resolution.The increasing complexity of construction projects, coupled with evolving regulatory frameworks, necessitates robust contract management strategies to address financial, operational, and legal risks.Poorly managed contracts often lead to cost overruns, project delays, and disputes, making it essential for stakeholders to adopt proactive measures in drafting, executing, and enforcing contractual obligations.This study examines key aspects of contract management in construction law, focusing on risk allocation, dispute resolution mechanisms, and performance enforcement strategies.Risk mitigation strategies, including well-defined contract terms, contingency planning, and insurance provisions, are explored to illustrate how parties can safeguard their interests.The research also highlights the effectiveness of alternative dispute resolution (ADR) methods, such as mediation, arbitration, and adjudication, in reducing litigation costs and project disruptions.Furthermore, contract enforcement mechanisms, including penalty clauses, performance bonds, and liquidated damages, are analyzed for their role in ensuring compliance and timely project completion.The study also evaluates the impact of digital transformation on contract management, particularly the use of smart contracts and blockchain technology to enhance transparency, efficiency, and dispute prevention.Through case studies and legal precedents, this research provides practical insights into how construction professionals, legal practitioners, and policymakers can optimize contract management practices.A comprehensive approach to risk management, dispute resolution, and performance enforcement is essential to maintaining legal compliance, ensuring financial stability, and improving project delivery in the dynamic construction sector.