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.
Large language models (LLMs) have advanced code generation, yet code repair remains difficult due to complex multi-step reasoning, strict correctness requirements, limited data, and real-time constraints. Smart contracts, as immutable blockchain programs, are an important case for repair. This paper presents FlowMemRep, a memory-based workflow for smart contract vulnerability repair. It extracts repair logic from a few-shot prompts, retrieves knowledge with gas cost profiles, version constraints, and deployment rules. In addition, outputs are stored in short-term memory, and then verified information is transferred to long-term memory for accurate reuse. Experiments on 205 real-world vulnerabilities show that FlowMemRep reaches 75 percent remediation accuracy, surpassing prior methods by 20 percent.
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.
Ermanno Francesco Sannini, Lucia Simeone, Corrado Aaron Visaggio, Andrea Di Sorbo
In Ethereum's immutable environment, high-quality documentation is essential for users and auditors to fully understand smart contract behavior and build trust. However, an empirical, manually conducted, and comprehensive investigation that analyzes and identifies undocumented implementation details, implicit assumptions, and comment-code inconsistencies in operational smart contracts is still missing. To address this gap, this paper examines the commenting practices occurring in the source code of smart contracts through a systematic manual review of 100 up-to-date Solidity smart contract projects mined from Etherscan, divided into high-usage and low-usage groups based on the number of transactions they received. By combining quantitative analysis, validated with Fisher's exact test, and a multidimensional qualitative checklist, we identify a systemic deficiency in documentation, especially concerning smart contract-specific facets, such as critical security patterns and gas optimization strategies. Our findings show that documentation quality is generally insufficient regardless of a contract's popularity. Smart contract developers tend to prioritize functionality over verifiability, highlighting an urgent need for domain-specific documentation standards and best practices that better support the entire development lifecycle of blockchain applications.
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.
Pothavarjula Phani Prasad, G. Srishailam, T. Srikanth, B. Ranjith Kumar
The primary requirement of any networking application is to maintain a high level of security. In this context, blockchain ecosystems have been rapidly evolving to address such challenges, leading to a growing demand for the development and implementation of Decentralized Applications (DApps). In recent years, several tools and platforms have emerged to support the deployment of various cybersecurity applications using blockchain technology. However, selecting the most suitable blockchain framework for a specific application has become a critical challenge. In this regard, we present this paper to perform a comparative study of smart contract deployment on Ethereum, Polygon, and Fantom blockchains. It includes architecture overviews, performance analysis, and step-by-step deployment methodology using Hardhat and MetaMask. The results help identify trade-offs in scalability, fees, and ease of development across these leading EVM-compatible platforms.
Abstract This chapter critiques decentralized autonomous organization (DAO)-specific organizational statutes and entity types in view of several propositions about the theory and structure of organizational law. First, it introduces and defends the proposition that organizational forms should, in the absence of an overriding reason, be neutral to private parties’ choices about technology. That said, while technological neutrality is a useful principle in evaluating organizational statutes, its application requires subtlety because one software technology may emulate another. Second, the chapter observes that existing general organizational forms are already capable of accommodating DAOs and other new technological structures for organizations. Using existing forms promotes harmony and simplicity in organizational law, a field that is already too fragmented and in which too many distinct types of entities already exist. The chapter also discusses the relationship between registered and unregistered organizational forms and how bespoke organizational statutes interact with the Corporate Transparency Act in the US.
Abstract This chapter explores how digital infrastructure—particularly distributed ledger technologies (DLT)—is reshaping the legal architecture of organizational forms. Using decentralized autonomous organizations (DAOs) as a focal case, the chapter argues that legal and technical affordances must be treated as composable building blocks in the design of digital companies. Far from displacing law, DLT exposes its continued role in enabling, constraining, and legitimating organizational activity. Five legal affordances are identified—property, agency, fiduciary law, liability, and contract—as essential to institutional coherence, and the chapter examines how these interact with digital affordances in emerging organizational forms. Mapping a spectrum from traditional entities to “pure” DAOs, the chapter highlights the rise of techno-legal hybrids and proposes two future-facing models: an “internalization” model embedding legal structure in code, and an “externalization” model layering code onto legal shells. It concludes that digital companies are not just technical innovations but jurisprudential provocations—challenges to rethink law’s role in the design of institutional life.
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:
The rapid growth of blockchain technology has driven the widespread adoption of smart contracts; however, their inherent vulnerabilities have led to significant financial losses. Traditional auditing methods, while essential, struggle to keep pace with the increasing complexity and scale of smart contracts. Large language models (LLMs) offer promising capabilities for automating vulnerability detection, but their adoption is often limited by high computational costs. Although prior work has explored leveraging large models through agents or workflows, relatively little attention has been given to improving the performance of smaller, fine-tuned models—a critical factor for achieving both efficiency and data privacy. In this paper, we introduce HKT-SmartAudit, a framework for developing lightweight models optimized for smart contract auditing. It features a multi-stage knowledge distillation pipeline that integrates classical distillation, external domain knowledge, and reward-guided learning to transfer high-quality insights from large teacher models. A single-task learning strategy is employed to train compact student models that maintain high accuracy and robustness while significantly reducing computational overhead. Experimental results show that our distilled models outperform both commercial tools and larger models in detecting complex vulnerabilities and logical flaws, offering a practical, secure, and scalable solution for smart contract auditing. The source code is available in the GitHub repository1.
Smart contracts are immutable once deployed, making security auditing crucial before deployment. Existing automated tools such as Slither are limited to pattern-matching based detection and cannot reason about the contract’s intended behaviour. Manual expert reviews help address this gap but do not scale to the volume of contracts requiring analysis. Large Language Models (LLMs) offer contextual reasoning capabilities that static analysers lack, but single-pass LLM outputs suffer from high false positive rates and unreliable vulnerability detection. This project proposes and evaluates a multi-agent LLM workflow for automated Solidity smart contract vulnerability detection. The system combines Slither static analysis with a staged reasoning pipeline comprising a context agent, a Retrieval Augmented Generation (RAG)-powered research agent, a two-round specialist debate, a judge synthesis agent, and an automated Foundry test generation and verification loop. Slither is retained to cover syntactically identifiable patterns, while the LLM agents focus on contextual and semantic reasoning that static analysis cannot capture. The workflow was evaluated against a labelled dataset of 360 Solidity contracts across twelve vulnerability categories, using a local model (DeepSeek-Coder-V2:16B) and a frontier model (Claude Sonnet 4.6) as baselines. Results show that the workflow substantially improved the local model's detection capability, nearly quintupling recall and doubling F1 over its one-shot baseline, demonstrating that structured multi-agent reasoning can partially compensate for model scale. However, precision remained low across all configurations and certain vulnerability categories remained difficult to detect. The automated test generation stage gives auditors a working starting point rather than requiring tests to be written from scratch. Overall, the results suggest that LLMs, when structured through a multi-agent workflow, show potential as a triage and scaffolding layer in the auditing process, improving coverage and reducing manual effort. However, they are still insufficient to entirely replace expert review.
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.