Decentralized Autonomous Organizations (DAOs) are a class of Decentralized Applications (DApps) using smart contracts to facilitate governance processes. The design of DAOs is affected by additional complexity compared to other DApps due to the need to specify organizational roles, permissions, and control relations early in the early development stages of the system. In addition, DAOs face scalability challenges. While existing Model-Driven Development (MDD) tools support general smart contract and DApp design, they lack constructs tailored to the organizational and governance features unique to DAOs. To bridge this gap, we develop a code generation approach for DAO-ML, a visual modeling language for DAO design. The translator we implement for this method generates smart contracts of DAOs with suitably configured roles and permissions from visual models. The generated smart contracts particularly optimize the representation of roles and permissions to improve the system scalability and handle complex governance structures, necessary to increase the utility of DAO systems. The approach is evaluated in the context of an in vivo case study on the development of a DAO for the disintermediated management of local tourism. This work advances MDD for decentralized systems by bridging high-level governance modeling with executable, gas-efficient smart contract code generation.
The use of Enterprise Data Warehouse (EDWs) has been experienced as the analytical backbone of risk management, financial reporting and regulatory reporting of the data in very regulated sectors like banking, insurance, and capital markets. They were based on batch-oriented Extract Transform Load (ETL) paradigms, tight coupled schema and monolithic governance models that are better suited to stability than agility. Nevertheless, the increasing regulatory complexity, impacts of the near-real time risk visibility requirements, and increasing cost of infrastructure have emanated inherent weaknesses of the legacy EDW architectures. At the same time, the emergence of hybrid cloud platforms, scalable object storage, distributed query engines, and workflow orchestration system has made it possible to make the paradigm shift toward Extract–Load–Transform (ELT), domain-driven data products, and decentralized ownership models. In spite of these developments, in numerous organizations, the pressure to modernize reporting pipes based on strong backward compatibility criteria, audit limitations and the operational risks of massive data migrations makes this a challenge. This paper gives a detailed blueprint of modernization in the process of moving the old EDW centric ETL architectures to the hybrid cloud ELT platforms to suit the risk, finance, and regulatory reporting. Its proposed solution integrates domain-driven data products and ELT pushdown transformations orchestrating control planes and explicit data contracts that is applied in an incremental fashion with a strangler pattern. The framework focuses on retrogressively compatible schemas, reconcilability determinacy, the rollback safety nets, and regulated cutover plans to provide continuous regulatory compliance. Using a well-organized migration roadmap, cost and performance metrics and an official risk register, the paper will show how organizations can shorten report delivery cycles, enhance service-level agreement (SLA) compliance and minimize the overall cost of ownership without sacrificing auditability and strict governance. The findings have shown that hybrid cloud ELT systems may cut the latency in report by more than 40%, cut compute expenditure by up to 35, and become much more responsive to regulatory cases without infection of information integrity or resilience.
One major issue associated to microservices architectures is aspects related to data consistency and management of data within such a context. To measure the effect on system integrity, this research paper compares different data consistency models which are, eventual consistency, distributed transactions through Two-Phase Commit (2PC), the Saga pattern. Employing historical data analysis, case studies, and examining the recent technological advancements, the study gives a comparative assessment of various approaches in terms of their capabilities to achieve transaction latencies and throughput and possibility to guarantee consistent results. Based on the results, it is evident that although eventual consistency models provide high scale-out and availability characteristics, the models experience transitory data inconsistency. Consistency is high since data is accessed from, and committed to, multiple databases in a distributed transaction but they have performance penalties. The Saga patterning gives a balanced view but at the same time brings together additional design difficulty. New trends like Distributed Ledger Technology (DLT) as well as new consensus algorithms that can be used to solve problematic approaches like the Raft algorithm give new possibilities for improving consistency of the data but bring their own concerns. The study implies that one must choose the right consistency model depending on the need and performance Degree and the future research can consider the use of the combination of several models along with the improvement of technological solutions to have a better deal with data consistency in microservices.
With the globalization of the software industry, requirements traceability has become increasingly critical in the software development process. However, the development of large-scale, complex software systems by cross-organizational research teams often faces challenges due to diverse organizational backgrounds, multi-site environments, conflicting objectives, and organizational boundaries. These factors can lead to trust issues, complicating the implementation of requirements traceability. To address these challenges, this study proposes a Smart Contract-Based Requirements Traceability (SCRT) framework. Smart contracts, which are executable code deployed on a blockchain, exhibit properties such as enforceability, tamper resistance, and verifiability. These characteristics empower the SCRT framework to enhance collaboration, communication, and trust among stakeholders while potentially improving the efficiency and quality of software development. Within the SCRT framework, a novel Requirements Traceability Information Model (RTIM) is introduced, which categorizes the links between new and existing artifacts. This model serves as a guide for the smart contract module, delineating which software artifacts to trace and the relationships to establish.
In increasingly dynamic organizational and computational environments, workflows controlled by multi-agent systems face often face context changes, blurred task borders, and unexpected behaviors. This research proposes a new model for coping with decision drift in adaptive workflows by modeling latent boundary negotiation. By using concepts from complex systems theory, cognitive modeling, and distributed artificial intelligence, we define agents as autonomous systems which are able to perceive, report, and re-negotiate internal role expectations through localized negotiation protocols. The designed model merges drift detection with multi-agent coordination techniques and correlates them to simulated workflow processes typical for high risk, time constrained settings like crisis management, distributed logistics, and agile development teams. Simulation results show that latent negotiation improves task coherence, decreases agent interference, and stabilizes the performance of workflows under ambiguous and changing boundary conditions much more than negotiation less systems. The benefits of negotiation-aware multi-agent systems in supporting sustained alignment of decisions and flexibility in the system are highlighted through comparison with static and rule-based systems. The results outline latent boundary negotiation as a key feature towards the implementation of intelligent self-organizing work systems in decentralized multi agent ecosystems.
Can you imagine, blockchain transactions can talk! In this paper, we study how they talk and what they talk about. We focus on the input data field of Ethereum transactions, which is designed to allow external callers to interact with smart contracts. In practice, this field also enables users to embed natural language messages into transactions. Users can leverage these Input Data Messages (IDMs) for peer-to-peer communication. This means that, beyond Ethereum's well-known role as a financial infrastructure, it also serves as a decentralized communication medium. We present the first large-scale analysis of Ethereum IDMs from the genesis block to February 2024 (3134 days). We filter IDMs to extract 867,140 transactions with informative IDMs and use LLMs for language detection. We find that English (95.4%) and Chinese (4.4%) dominate the use of natural languages in IDMs. Interestingly, English IDMs center on security and scam warnings (24%) with predominantly negative emotions, while Chinese IDMs emphasize emotional expression and social connection (44%) with a more positive tone. We also observe that longer English IDMs often transfer high ETH values for protocol-level purposes, while longer Chinese IDMs tend to involve symbolic transfer amounts for emotional intent. Moreover, we find that the IDM participants tend to form small, loosely connected communities (59.99%). Our findings highlight culturally and functionally divergent use cases of the IDM channel across user communities. We further examine the security relevance of IDMs in on-chain attacks. Many victims use them to appeal to attackers for fund recovery. IDMs containing negotiations or reward offers are linked to higher reply rates. We also analyze IDMs' regulatory implications. Their misuse for abuse, threats, and sexual solicitation reveals the urgent need for content moderation and regulation in decentralized systems.
This paper addresses the challenge of creating smart contracts for applications represented using Business Process Management and Notation (BPMN) models. In our prior work we presented a methodology that automates the generation of smart contracts from BPMN models. This approach abstracts the BPMN flow control, making it independent of the underlying blockchain infrastructure, with only the BPMN task elements requiring coding. In subsequent research, we enhanced our approach by adding support for nested transactions and enabling a smart contract repair and/or upgrade. To empower Business Analysts (BAs) to generate smart contracts without relying on software developers, we tackled the challenge of generating smart contracts from BPMN models without assistance of a software developer. We exploit the Decision Model and Notation (DMN) standard to represent the decisions and the business logic of the BPMN task elements and amended our methodology for transformation of BPMN models into smart contracts to support also the generation script to represent the business logic represented by the DMN models. To support such transformation, we describe how the BA documents, using the BPMN elements, the flow of information along with the flow of execution. Thus, if the BA is successful in representing the blockchain application requirements using BPMN and DMN models, our methodology and the tool, called TABS, that we developed as a proof of concept, is used to generate the smart contracts directly from those models without developer assistance.
Julie Saesen, Bastian Kindermann, Darius Abel, Steffen Strese
Decentralized autonomous organizations (DAOs) leverage blockchain technology to facilitate collaboration and decentralize decision-making through rules encoded in smart contracts. DAOs challenge traditional governance theory by combining ownership and management using governance tokens. A governance token’s performance is traceable based on token price changes and subject to influencing factors. Understanding these factors is essential to ensure DAOs’ long-term viability as a new organizational form. However, insights into the drivers of token performance are still limited, leaving both DAO founders and potential token holders in the dark about which on-chain governance design choices to adopt to attract potential token holders. We apply signaling theory to elucidate how on-chain governance design choices influence token performance and use a unique dataset of 204 DAOs. We find that a minimum token quorum positively relates to token performance, while embedding voting incentives, such as non-fungible tokens or additional governance tokens, can negatively relate to token performance. Moreover, we identify founders’ stake as a contingent factor moderating these signals.
The increasing demand for scalable blockchain has driven research into parallel execution models for smart contracts. Crystality is a novel smart contract programming language designed for parallel Ethereum Virtual Machines (EVMs), enabling fine-grained concurrency through Programmable Contract Scopes and Asynchronous Functional Relay. This paper presents the first formal structural operational semantics for Crystality, providing a rigorous framework to reason about its execution. We mechanize the syntax and semantics of Crystality in the theorem-proving assistant Coq, enabling formal verification of correctness properties. As a case study, we verify a simplified token transfer function, demonstrating the applicability of our semantics in ensuring smart contract correctness. Our work lays the foundation for formally verified parallel smart contracts, contributing to the security and scalability of blockchain systems.
Proof-of-Stake (PoS) is a prominent Sybil control mechanism for blockchain-based systems. In "e-PoS: Making PoS Decentralized and Fair," Saad et al. (TPDS'21) introduced a new Proof-of-Stake protocol, e-PoS, to enhance PoS applications' decentralization and fairness. In this comment paper, we address a misunderstanding in the work of Saad et al. The conventional Proof-of-Stake model that causes the fairness problem does not align with the general concept of Proof-of-Stake nor the Proof-of-Stake cryptocurrencies mentioned in their paper.
Edgar Roberto Dulce Villarreal, Giovanni Hernández, Jesús Insuasti, Julio Ariel Hurtado Alegría · 5 authors
The exchange of medical information significantly benefits people's quality of life, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge. Blockchain technology is an alternative to find a balance in the healthcare ecosystem. Smart contracts (SC) are decentralized and self-executing programs that allow the automation of agreements without intermediaries to improve operational efficiency. However, the constant development of new Blockchain technologies and programming languages for smart contracts is a growing problem. This work presents the validation by expert judgment of the MUISCA (Mechanism for UnIversal SmartContrAct) tool, which uses Model Driven Engineering (MDE). MUISCA uses transformations of models and models to text to generate smart contracts in healthcare environments and specific to Blockchain platforms. The validation is conducted by smart contracts development experts, who show positivity in the perceived usefulness.
Lukas Kirchdorfer, Robert Blümel, Timotheus Kampik, Han van der Aa · 5 authors
Abstract Business process simulation (BPS) is a powerful tool for estimating process performance across different scenarios, offering critical support for organizational process redesign and optimization. Traditional BPS approaches predominantly rely on a control-flow-first perspective by enriching a process model with simulation parameters. While these approaches seem suitable for capturing centrally orchestrated processes, such as those managed by workflow systems, they fall short of accurately reflecting real-world processes characterized by decentralized decision-making and distinct resource behaviors. To overcome this limitation, we propose AgentSimulator , a resource-first BPS approach that discovers a multi-agent system from an event log. By modeling the distinct behaviors and interaction patterns of individual resources, AgentSimulator effectively simulates the underlying process. Our approach automatically identifies whether resource behavior is rather orchestrated or autonomous, adapting to the specific decision-making structure of the process. Experimental results reveal that AgentSimulator achieves state-of-the-art simulation accuracy while ensuring high adaptability to various process types.
This article examines the transformative impact of Agentic Process Automation (APA) on modern business workflows, highlighting the evolution from traditional Robotic Process Automation to autonomous intelligent systems. The article establishes APA as a paradigm shift that transcends the limitations of conventional automation approaches through self-governing agent models capable of adaptive decision-making. Through comprehensive analysis spanning architectural foundations, comparative capabilities, multi-agent collaboration frameworks, and real-world implementations, this article demonstrates how APA systems deliver superior performance in dynamic business environments. Key aspects explored include decentralized intelligence, machine learning integration, ethical governance frameworks, and strategic implementation methodologies. Case studies across financial services, healthcare, and manufacturing sectors provide empirical evidence of APA's operational benefits, while also highlighting implementation challenges and mitigation strategies. The article reveals that organizations implementing agentic systems achieve significant improvements in process efficiency, adaptability, and cost optimization compared to traditional automation approaches, particularly for complex workflows requiring judgment and contextual understanding. This article provides valuable insights for organizations navigating the transition toward intelligent automation and offers a structured framework for evaluating APA readiness, implementation priorities, and governance considerations within enterprise environments
This article examines key emerging technologies transforming financial platform engineering. Platform engineering plays a pivotal role in building these systems by leveraging microservices architecture, event-driven systems, and cloud-native technologies. This article explores how modern platform engineering practices ensure low latency, high throughput, security, and regulatory compliance while integrating cutting-edge technologies like machine learning and blockchain. Machine learning has revolutionized fraud detection by enabling the analysis of vast transactional datasets to identify patterns invisible to human observers. Blockchain technology has gained adoption for transaction verification, providing distributed ledger systems that ensure security and immutability while enabling smart contracts that automate complex financial agreements. Real-time analytics capabilities allow financial institutions to process streaming data for immediate insights on market trends, customer behavior, and risk factors, supporting data-driven decision-making at market speed. Finally, API ecosystems have created interconnected networks of services that facilitate innovation through standardized interfaces, transforming how financial services are developed and consumed across core banking, partner integration, and public marketplace contexts.
Decentralized Autonomous Organizations (DAOs) are a type of Decentralized Applications (DApps) that utilize smart contracts to support governance processes. To achieve a high degree of utility of the system, stakeholders need to identify a suitable organizational structure in the early stages of design. While Model-Driven Development (MDD) methods are established for DApp and smart contract design, they lack specialization for modeling the organizational structures of DAOs. To address this gap, we propose a modeling language and a method which support crucial DAO design and development phases. The method is evaluated through an in vivo case study. Unlike existing solutions, comprehensive stepwise guidance is provided by our method for both technical and non-technical stakeholders involved in DAO development from the initial stages of the project.
Distributed ledger technology (DLT) enable the immutable, transparent, and tamper-proof storage of transaction records in a shared electronic register. Due to these intrinsic properties, DLT has the potential to be highly disruptive to businesses. However, the heterogeneity and intricacy of the underlying technologies impede their more widespread adoption. A primary challenge lies in comprehending the interrelationships between the organizational, economical, and technical dimensions of DLT systems. An integrated perspective on the fundamental concepts of DLT within each dimension can be accomplished through the implementation of domain-specific conceptual modeling languages that explicitly account for intrinsic properties. Conceptual models adhere to well-defined elements and rules for the utilization of these elements. This enables the algorithmic processing of models to generate derivations and new insights.The focus of prior research on modeling support for DLT has been on the technical dimension. However, research on conceptual modeling approaches enabling the joint consideration of organizational, economical, and technical factors is lacking. This situation is undesirable both from a scientific standpoint and from a practical one. To address this gap, this thesis advances the current state-of-the-art in the design of DLT applications by introducing several domain-specific approaches addressing concerns across dimensions. This includes support for the decision-making in organizations, for the design of their business model, its alignment with the underlying DLT systems, and for the development of smart contracts and subsequent generation of functional code. The artifacts are consolidated into a domain-specific conceptual modeling method, facilitating the design of DLT applications and business cases.
This paper presents a comprehensive Model-Driven Engineering (MDE) methodology for automatically transforming Business Process Model and Notation (BPMN) diagrams into executable blockchain-based smart contracts. The proposed approach defines a set of Atlas Transformation Language (ATL) rules that systematically map BPMN elements to Solidity con-structs, ensuring semantic consistency and traceability through-out the transformation process. The framework integrates several stages, including process modeling, model validation, code generation, and deployment, supported by tools such as Camunda, Eclipse ATL, Remix IDE, and MetaMask. Experimental vali-dation on the Ethereum Sepolia test network demonstrates the approach’s ability to enhance automation, reduce manual coding errors, and improve synchronization between business work-flows and their on-chain implementations. Compared to existing BPMN-to-blockchain frameworks, the proposed solution offers a unified and reusable transformation pipeline that bridges the gap between business process modeling and blockchain execution. The study concludes that MDE provides a scalable, traceable, and standardized foundation for developing decentralized business process applications.
Background: Ontologies and knowledge graphs have become critical for structuring data into machine-interpretable knowledge, especially in dynamic domains like IT service management (ITSM). Traditional ontology engineering relies heavily on domain experts, making it costly and slow. This study investigates whether a domain-specific ontology can be extended from a top-level ontology without expert involvement, using the IT service management ontology (ITSMO) and the descriptive ontology for linguistic and cognitive engineering (DOLCE-lite) as a test case used in this study. Methodology: We propose an automated mapping approach integrating lexical approaches, embeddings, graph neural networks (GNN), and large language models (LLMs). Two primary mapping methods were developed: (1) embedding-based matching, computing cosine similarity between class embeddings from DOLCE and ITSMO; and (2) LLM-based matching, prompting a language model (GPT-4o) to evaluate class compatibility on a numeric scale. We also experiment with GraphSAGE GNN to enrich embeddings with ontology structure. Z-score clustering is applied to similarity scores to select top candidate mappings while filtering out outliers from the top cluster. The methodology operates with no annotated data and was validated using three-steps approach: GPT-4o as a surrogate expert for baseline class matching evaluation, expert spot-check, and OWL reasoner (Pellet and HermiT) to prove logical consistency (Glimm et al., 2014; Sirin et al., 2007). Results: The automated method successfully mapped ITSMO classes under DOLCE, yielding an integrated ontology (80 classes) that extends DOLCE into the ITIL domain with minimal expert intervention (expert consolidated suggestions into a result ontology). The LLM-based approach (GPT-4o) achieved the best performance with 73.5% accuracy for top-1 mappings and 82.4% for top-3 (cluster) inclusion. Transformer-based embeddings (e.g., DeBERTa) also performed well (up to 39.3% top-1, outperform random matching with 27.6% accuracy), but classical graph embeddings (RDF2Vec/Node2Vec) failed due to the small ontology size. Incorporating a GNN provided smoother embedding distributions and increased correct mappings within top-3 clusters, but it slightly reduced top-1 precision in this small-graph setting. These findings underscore the effectiveness of LLMs in zero-shot ontology alignment and the limitations of purely structural methods on limited data. Conclusions: This work demonstrates, as a proof-of-concept, that an upper-level ontology can be extended to a domain ontology automatically, with no or minimal expert involvement, by leveraging AI-based mapping techniques. The resulting new ontology integrates ITSMO into DOLCE, providing a consistent semantic foundation for IT domain knowledge graphs. The approach is immediately applicable to ITSM and suggests a generalizable framework for ontology expansion in other domains. Future work will focus on scaling the method to larger ontologies, automatically discovering new classes/relations from text, and evaluating the approach’s practical impact on IT service management processes.
Abstract Decentralized autonomous organizations (DAOs), like blockchains, were expected to revolutionize the economy by solving the principal–agent problem in the corporate form. Yet, the very first DAO (“The DAO”) suffered a catastrophic hack that led to the Ethereum blockchain’s forking. Despite this failure, and the 2022 crypto markets collapse, enthusiasm for DAOs as corporate alternatives persists in crypto circles. Nine years on, however, DAOs remain a niche structure with little traction outside crypto projects. This chapter posits that this is due to fundamental flaws in the case for DAOs as corporate replacements. Crypto proponents often dismiss centralization and opacity as wholly undesirable, overlooking their functional value in business organizations. They also fail to recognize that blockchain’s security, transparency, and predictability are narrowly bound and come at a cost, rendering DAOs poorly equipped to handle the risks organizations face in the real world. These trade-offs explain why DAOs are less a revolution, more naïve wishful thinking.
Ajibola Oluwafemi Oyeleye, Onyeka Franca Asuzu, Adaobi Vivian Ibeh
This paper presents a conceptual model for raising Accounts Payable (AP) accuracy in research institutions by embedding process intelligence across the procure-to-pay lifecycle. The model integrates process mining, rule-based controls, and machine-learning anomaly detection with grant compliance logic to reduce mismatches, duplicate payments, and breaches. It addresses the context of universities and research hospitals, where varied funding sources, sponsor terms, and decentralized purchasing create transaction patterns and compliance risk. The model positions AP as a data-driven assurance hub connecting principal investigators, central finance, and suppliers. The architecture has four layers: first, data acquisition that unifies ERP, e-procurement, and grant management logs via standardized event schemas; second, conformance engines encoding sponsor allowability, period of performance, three-way match, and delegation rules; third, analytics and prediction that combine process discovery, first-pass-yield forecasting, vendor normalization, and exception clustering; and fourth, workflow orchestration that returns prescriptive alerts to case managers and routes exceptions to approvers for timely resolution. Methodologically, the model adopts a design-science and DMAIC hybrid. Teams baseline cycle time, touchpoints, and first-pass accuracy; mine event logs to map as-is variants; prioritize failure modes through FMEA; implement targeted controls; and measure effects with interrupted time series and segmented regression. Data quality is elevated through master-data maintenance, vendor deduplication, and invoice OCR confidence thresholds with human-in-the-loop review. Expected outcomes include higher first-pass yield, fewer late-payment penalties, improved sponsor billing, and cleaner audit trails. Leading indicators exception rate, conformance score, and rework loops feed a control chart to sustain gains, while lagging indicators write-offs, questioned costs, and audit findings confirm risk reduction. The model also incorporates equity and accessibility by simplifying small-supplier onboarding and enabling transparent status notifications to reduce inquiry volume and payment anxiety. A change-management plan aligns incentives across finance, research administration, and procurement, with skills uplift delivered through training and playbooks. This conceptualization offers a scalable blueprint aligning AP accuracy with research integrity, stewardship of public funds, and overall operational resilience, enabling institutions to realize predictable, compliant payables operations and stronger supplier relationships.
Block reorganization (reorg) may occur when a blockchain fork is deliberately instigated and remains poorly solved on Ethereum. We utilize an agent-based modeling approach to simulate the block generation and decision-making processes of reorg attackers and ordinary validators. We analyze the impact of six validator network structures (i.e., random, regular, small-world, scale-free, hierarchical, and community networks) and four fork selection rules (i.e., LMD GHOST, HLMD GHOST, Goldfish, and RLMD GHOST) on the success rate of reorg attacks. The results show that the community network is more vulnerable to reorg attacks, while the hierarchical network is more resilient to such attacks. In addition, the Goldfish fork-choice rule can significantly reduce the success rate of reorg attacks.
The UpOn platform leverages cutting-edge modular smart contract architecture to redefine Real World Asset (RWA) management by creating a legally compliant, decentralized ecosystem for virtual office investment, launching, and rental. Built with a focus on transparency, security, and compliance, UpOn allows users to seamlessly participate in real estate investment through Non-Fungible Tokens (NFTs) that represent virtual office spaces. By integrating law firms for legal oversight, UpOn ensures that all transactions align with global legal standards, providing users and investors with trust and security. The platform comprises four main components-Invest, Launch, Rent and Legal-each of which serves a unique function within the ecosystem. The Invest section enables investors to fund new office projects through NFT minting, while Launch allows owners of established offices to tokenize their assets. In the Rent section, users can securely lease office NFTs with contract-backed agreements tailored to local regulations, and the dashboard provides realtime data retrieval from smart contracts, ensuring a transparent user experience. Finally, the Legal component, backed by law firms, safeguards user rights and enforces compliance in all jurisdictions. With its modular design and elimination of centralized databases, UpOn showcases the full potential of Web3 by simplifying the traditional complexities of RWA projects, lowering costs, and offering unprecedented accessibility to global markets.
The rapid evolution of Ethereum’s infrastructure calls for innovative mechanisms to enhance scalability, security, and performance. This paper introduces BeamSNARKS, a cutting-edge framework designed to address critical challenges in zero-knowledge proof systems. BeamSNARKS encompasses two groundbreaking innovations: the Dynamic zkSNARKS Generation Optimization Mechanism and the Dynamic SNARKification Technology. The former revolutionizes computational efficiency by dynamically retrieving state data relevant to proof generation, minimizing bandwidth and storage requirements while maintaining validation accuracy. The latter introduces adaptive circuit design and hierarchical proof aggregation to optimize transaction throughput and reduce the computational and financial overhead of Layer 1 submissions. Together, these innovations establish BeamSNARKS as a pivotal advancement in scalable, efficient, and resource-optimized zero-knowledge proof systems. Through comprehensive analysis and targeted experiments, this paper evaluates the performance of BeamSNARKS’s innovations, demonstrating their potential to transform Ethereum’s decentralized ecosystem and lay the groundwork for future high-throughput applications.
Decentralized Autonomous Organizations (DAOs), based on block-chain systems such as Ethereum, are emerging governance protocols that enable decentralized community management without a central authority. For instance, UniswapDAO allows members to vote on policy changes for the Uniswap exchange. However, DAOs face challenges regarding scalability, governance, and compliance. Hybrid-DAOs, which combine the decentralized nature of DAOs with traditional legal frameworks, provide solutions to these issues. This research explores various aspects of DAOs, including their voting mechanisms, which, while ensuring fairness, are susceptible to Sybil attacks, where a user can create multiple accounts to exploit the system. Hybrid-DAOs offer robust solutions to these attacks, enabling more equitable voting methods. Moreover, decentralization can be understood through four properties: anonymity, transparency, accountability, and fairness, each with distinct implications for DAOs. Lastly, this work discusses legal challenges Hybrid-DAOs face and their promising applications across sectors such as nonprofit management, corporate governance, and startup funding. Overall, we argue that Hybrid-DAOs are the future of DAOs: the additional legal structure enhances the feasibility of many applications, and they offer innovative solutions to technical problems that plague DAOs.