The integration of blockchain and artificial intelligence (AI) in legal contract execution has revolutionized traditional contract enforcement mechanisms. Smart contracts, self-executing contracts with terms encoded into blockchain networks, have emerged as a transformative tool in business transactions, reducing the reliance on intermediaries and enhancing contract security. AI further enhances these contracts by providing predictive analytics, natural language processing (NLP) for contract interpretation, and automated dispute resolution mechanisms. However, despite their potential, the legal recognition and enforcement of smart contracts pose significant challenges. Jurisdictional issues, regulatory compliance, contract validity, and the limitations of blockchain immutability necessitate an in-depth analysis of the legal landscape governing smart contracts. This paper provides a comprehensive review of the legal and regulatory frameworks surrounding AI-driven smart contracts, identifying their advantages, limitations, and future prospects. The study examines case laws, real-time implementations, and the role of AI in streamlining dispute resolution. The findings reveal that while blockchain enhances contract security and AI assists in interpretation, the lack of uniform legal frameworks remains a major hurdle. Through comparative analysis of existing regulatory approaches and real-world applications, this paper outlines potential solutions for effective enforcement and dispute resolution in AI-enhanced smart contracts.
This paper is concerned with a natural variant of the contact process modeling the spread of knowledge on the integer lattice. Each site is characterized by its knowledge, measured by a real number ranging from 0 = ignorant to 1 = omniscient. Neighbors interact at rate $Ī»$, which results in both neighbors attempting to teach each other a fraction $Ī¼$ of their knowledge, and individuals die at rate one, which results in a new individual with no knowledge. Starting with a single omniscient site, our objective is to study whether the total amount of knowledge on the lattice converges to zero (extinction) or remains bounded away from zero (survival). The process dies out when $Ī»\leq Ī»_c$ and/or $Ī¼= 0$, where $Ī»_c$ denotes the critical value of the contact process. In contrast, we prove that, for all $Ī»> Ī»_c$, there is a unique phase transition in the direction of $Ī¼$, and for all $Ī¼> 0$, there is a unique phase transition in the direction of $Ī»$. Our proof of survival relies on block constructions showing more generally convergence of the knowledge to infinity, while our proof of extinction relies on martingale techniques showing more generally an exponential decay of the knowledge.
Purpose Several terms are interchangeably employed by researchers and practitioners to refer to central bank digital currency (CBDC), resulting in potential mistakes in the CBDC description. This study aims to survey the conceptualization of the CBDC and its utilization context to propose a list of CBDC terminologies. Design/methodology/approach The research method used is the multivocal literature review, which covers the state-of-the-art with scientific papers and state-of-the-practice with practitioners' reports of the CBDC terminology. Findings The finding reveals that the terminologies used to mention a digital currency (DC) issued by a central bank are digital money, official DC, DC, centrally banked cryptocurrencies, digital cash, digital central bank money, CBDCs, central bank-issued cryptocurrency, central bank cryptocurrency, digital fiat currency, central bank-issued digital cash and sovereign digital currencies. The authors who proposed CBDC with distributed ledger technology-based infrastructure named it central bank cryptocurrency, and the others who didnāt specify clearly the infrastructure called it CBDC or another synonym of the DC. Originality/value We propose a CBDC concept map to clarify the CBDC understanding, which lists all terminologies found in the literature in a logical structure. The proposed CBDC concept map elucidates the linguistic landscape and clarifies the interpretation nuances across different CBDC terminologies, provides a comprehensive blueprint of the multi-conceptualization nature of CBDCs and contributes with an accessible tool for economists, technologists and lawyer researchers.
Blockchain technology is rapidly evolving, with scalability remaining one of its most significant challenges. While various solutions have been proposed and continue to be developed, it is essential to consider the blockchain trilemma -- balancing scalability, security, and decentralization -- when designing new approaches. One promising solution is the zero-knowledge proof (ZKP)-based rollup, implemented on top of Ethereum. However, the performance of these systems is often limited by the efficiency of the ZKP mechanism. This paper explores the performance of ZKP-based rollups, focusing on a solution built using the Hardhat Ethereum development environment. Through detailed analysis, the paper identifies and examines key bottlenecks within the ZKP system, providing insight into potential areas for optimization to enhance scalability and overall system performance.
The decentralized finance (DeFi) ecosystem continues to evolve, allowing crypto holders greater control over their assets. This research examines key aspects of token accessibility, liquidity provisioning, and holder distribution. The study focuses on evaluating whether holders can check their ranking and percentage ownership, the availability of the token on decentralized exchanges (DEXs), the feasibility of liquidity pool creation, and opportunities for holders to acquire at least 0.1% of the total supply. In present paper, Coredaovip token has been considered as example to evaluate the crypto holder accessibility, liquidity and participation in decentralized ecosystem.
Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and enable self-adjusting governance mechanisms in Decentralized Autonomous Organizations (DAOs). Additionally, AI enhances blockchain consensus mechanisms, fraud detection, and risk assessment in Decentralized Finance (DeFi) applications. Privacy-preserving technologies such as zero-knowledge proofs (ZKPs) and quantum-resistant cryptography strengthen the security and confidentiality of AI-driven smart contracts. This research explores the convergence of AI and blockchain, examining how intelligent smart contracts can automate legal compliance, enforce dynamic contract logic, and optimize transaction fees while maintaining transparency and decentralization. By integrating AI-driven decision-making, automated dispute resolution, and scalable execution models, this study provides a comprehensive framework for secure, efficient, and intelligent decentralized governance. Keywords: AI-powered smart contracts, blockchain automation, decentralized governance, reinforcement learning, fraud detection, decentralized finance (DeFi), zero-knowledge proofs, quantum-resistant cryptography, DAO optimization, legal compliance automation.
Sandro Rodriguez Garzon, Carlo Segat, Axel KĆ¼pper
A Decentralized Identifier (DID) empowers an entity to prove control over a unique and self-issued identifier without relying on any identity provider. The public key material for the proof is encoded into an associated DID document (DDO). This is preferable shared via a distributed ledger because it guarantees algorithmically that everyone has access to the latest state of any tamper-proof DDO but only the entities in control of a DID are able to update theirs. Yet, it is possible to grant deputies the authority to update the DDO on behalf of the DID owner. However, the DID specification leaves largely open on how authorizations over a DDO are managed and enforced among multiple deputies. This article investigates what it means to govern a DID and discusses various forms of how a DID can be controlled by potentially more than one entity. It also presents a prototype of a DID-conform identifier management system where a selected set of governance policies are deployed as Smart Contracts. The article highlights the critical role of governance for the trustworthy and flexible deployment of ledger-anchored DIDs across various domains.
ABSTRACT The emergence of wireless technology brought about enhanced communication across various devices, resulting in the demand for efficient and reliable wireless networks, like wireless mesh networks (WMNs) and mobile Adāhoc Networks (MANETs). MANETs are known for their decentralized nature, rapid deployment, infrastructureāless operation, adaptability, and ease of use in several applications and outdoor events. Despite their flexibility, they often face challenges relating to security vulnerabilities, together with blackhole and grayhole attacks, and tradeāoffs in terms of performance relating to reliability and integrity. This paper proposes an improved, innovative routing protocol for Adāhoc OnāDemand Distance Vector (AODV) by infusion of blockchain's proof of stake (PoS) consensus mechanism named PoSAODV, whose objective is to enhance security, energyāefficiency, and adaptability while reducing packet loss rate, routing overheads, and increasing throughput. Smart contractābased validator selection was utilized to ensure fairness and reduce blackhole and grayhole attacks. The result obtained through simulation demonstrates that PoSAODV outperforms the original AODV by reduced latency of 0.79 ms , average throughput of 45 Mbps , and packet delivery ratio of 80%ā100% in both unsafe and safe environments. This makes PoSAODV suitable for resourceāconstrained adāhoc networks with dynamic topologies.
Zeta Avarikioti, Eleftherios Kokoris Kogias, Ray Neiheiser, Christos Stefo
The security of many Proof-of-Stake (PoS) payment systems relies on quorum-based State Machine Replication (SMR) protocols. While classical analyses assume purely Byzantine faults, real-world systems must tolerate both arbitrary failures and strategic, profit-driven validators. We therefore study quorum-based SMR under a hybrid model with honest, Byzantine, and rational participants. We first establish the fundamental limitations of traditional consensus mechanisms, proving two impossibility results: (1) in partially synchronous networks, no quorum-based protocol can achieve SMR when rational and Byzantine validators collectively exceed $1/3$ of the participants; and (2) even under synchronous network assumptions, SMR remains unattainable if this coalition comprises more than $2/3$ of the validator set. Assuming a synchrony bound $Ī$, we show how to extend any quorum-based SMR protocol to tolerate up to $1/3$ Byzantine and $1/3$ rational validators by modifying only its finalization rule. Our approach enforces a necessary bound on the total transaction volume finalized within any time window $Ī$ and introduces the \emph{strongest chain rule}, which enables efficient finalization of transactions when a supermajority of honest participants provably supports execution. Empirical analysis of Ethereum and Cosmos demonstrates validator participation exceeding the required $5/6$ threshold in over $99%$ of blocks, supporting the practicality of our design. Finally, we present a recovery mechanism that restores safety and liveness after consistency violations, even with up to $5/9$ Byzantine stake and $1/9$ rational stake, guaranteeing full reimbursement of provable client losses.
AI agents integrated with Web3 offer autonomy and openness but raise security concerns as they interact with financial protocols and immutable smart contracts. This paper investigates the vulnerabilities of AI agents within blockchain-based financial ecosystems when exposed to adversarial threats in real-world scenarios. We introduce the concept of context manipulation -- a comprehensive attack vector that exploits unprotected context surfaces, including input channels, memory modules, and external data feeds. It expands on traditional prompt injection and reveals a more stealthy and persistent threat: memory injection. Using ElizaOS, a representative decentralized AI agent framework for automated Web3 operations, we showcase that malicious injections into prompts or historical records can trigger unauthorized asset transfers and protocol violations which could be financially devastating in reality. To quantify these risks, we introduce CrAIBench, a Web3-focused benchmark covering 150+ realistic blockchain tasks. such as token transfers, trading, bridges, and cross-chain interactions, and 500+ attack test cases using context manipulation. Our evaluation results confirm that AI models are significantly more vulnerable to memory injection compared to prompt injection. Finally, we evaluate a comprehensive defense roadmap, finding that prompt-injection defenses and detectors only provide limited protection when stored context is corrupted, whereas fine-tuning-based defenses substantially reduce attack success rates while preserving performance on single-step tasks. These results underscore the urgent need for AI agents that are both secure and fiduciarily responsible in blockchain environments.
Giuseppe Galante, Christiancarmine Esposito, Pietro Catalano, Salvatore Moscariello Ā· 8 authors
The financial sustainability of a generic supply chain is a complex problem, which can be addressed through detailed monitoring of financial operations deriving from stakeholder interrelationships and consequent analysis of these financial data to compute the relative economic indicators. This allows the identification of specific fintech tools that can be selected to mitigate financial risks. The intention is to retrieve the financial transactions and private information of stakeholders involved in the supply chain to construct a knowledge base and a digital twin representation that can be used to visualize, analyze, and mitigate the issues associated with the financial sustainability of the chain. We propose a software platform that employs key enabling technologies, including AI, blockchain, knowledge graph, and others, opportunely coordinated to address the financial sustainability problem affecting single stakeholders and the entire supply chain. This platform allows for the involvement of external entities that can help stakeholders or the whole supply chain to solve financial sustainability problems through economic interventions. Moreover, introducing these entities enables stakeholders less well-positioned in the market to access financial services offered by credit institutions, utilising the supply chain's internal information as evidence of its reliability. To validate the proposed idea, a case study will be presented analyzing the financial instrument of securitization.
In the rapidly evolving landscape of digital assets and blockchain technologies, the necessity for robust, scalable, and secure data management platforms has never been more critical. This paper introduces a novel software architecture designed to meet these demands by leveraging the inherent strengths of cloud-native technologies and modular micro-service based architectures, to facilitate efficient data management, storage and access, across different stakeholders. We detail the architectural design, including its components and interactions, and discuss how it addresses common challenges in managing blockchain data and digital assets, such as scalability, data siloing, and security vulnerabilities. We demonstrate the capabilities of the platform by employing it into multiple real-life scenarios, namely providing data in near real-time to scientists in help with their research. Our results indicate that the proposed architecture not only enhances the efficiency and scalability of distributed data management but also opens new avenues for innovation in the research reproducibility area. This work lays the groundwork for future research and development in machine learning operations systems, offering a scalable and secure framework for the burgeoning digital economy.
Marta Rinaldi, Mario Caterino, Stefano Riemma, Roberto Macchiaroli Ā· 5 authors
Background: Emergency scenarios present unprecedented challenges for supply chains worldwide, particularly in the management and distribution of critical supplies, where timely delivery and maintaining integrity are crucial. Methods: This article explores an innovative approach to enhance the emergency management of supply chains using blockchain technology and simulation-based modelling. The proposed methodology aims to tackle issues such as transparency, efficiency, and security, which are vital for managing logistics during crises. A case study involving a vaccine rollout is used to demonstrate how blockchain can optimise supply chain operations, reduce bottlenecks, and ensure better traceability and accountability throughout the process. The case study is specifically developed based on the distribution of COVID-19 vaccines in Italy. Results: The integration of blockchain technology not only enhances data integrity and security but also facilitates real-time monitoring and decision-making. Conslusions: The findings suggest that the proposed blockchain-based model can significantly improve supply chain resilience in emergency situations compared to traditional methods, thereby offering valuable insights for policymakers and supply chain managers facing future crises.
The article is dedicated to analyzing the potential for the implementation of blockchain solutions in the activities of large agro-industrial holdings. Based on a review of current research and the generalization of pilot project experiences, key areas for applying distributed ledger technology in the agro-industrial complex have been identified: supply chain management, product quality monitoring, logistics optimization, and automation of financial transactions. An assessment of the economic impact of integrating blockchain into the business processes of agro-holdings has been conducted. The results obtained indicate significant potential for increasing companiesā efficiency and sustainability through enhanced transparency, security, and speed of operations. Barriers hindering the widespread adoption of blockchain in the agro-industrial complex have been highlighted, and measures to overcome them have been proposed. The conclusions drawn are valuable for strategic planning of digital transformation in the agricultural sector. (127 words).
Open access
Blockchain Technology Applications and Security
Digitalization and Economic Development in Agriculture
The rapid expansion of blockchain technology has led to increased security challenges, particularly in detecting fraudulent transactions and malicious activities within decentralized networks. Traditional anomaly detection techniques, including rule-based heuristics and supervised learning models, struggle to adapt to the dynamic and complex nature of blockchain transactions. This paper introduces a graph neural network (GNN)-based anomaly detection framework designed to improve blockchain security by leveraging the inherent graph structure of transaction networks. The proposed approach models blockchain transactions as a directed graph, where nodes represent wallet addresses and edges correspond to transaction flows. By applying spatial and temporal graph learning techniques, the framework captures both network topology and transaction evolution over time, allowing for the identification of anomalous activities such as money laundering, phishing scams, and Ponzi schemes. The GNN model incorporates graph convolutional networks (GCN), graph attention networks (GAT), and gated recurrent units (GRU) to learn both spatial dependencies and sequential patterns within blockchain transactions. Experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the GNN-based framework outperforms conventional fraud detection methods in terms of precision, recall, and false positive reduction. The model successfully detects fraudulent transactions with an F1-score of 0.92, showing its effectiveness in identifying emerging threats in blockchain networks. These results highlight the potential of deep learning-based anomaly detection in enhancing blockchain security, providing a scalable and adaptive solution for detecting fraud in decentralized financial ecosystems.
The deployment of Internet of Things (IoT) devices and edge computing has grown exponentially and has reinvented the world of data processing and making it possible to deliver low-latency applications in real-time settings. Notwithstanding, with this shift towards the use of distributed systems, we are faced with new challenges of ensuring there is effective management of energy consumption. The main aim of the proposed study was to design, deploy, and test a new decentralized edge computing framework that combines blockchain technology and artificial intelligence to achieve optimized energy efficiency. To be more precise, we intended to create AI models that are able to recognize and forecast energy usage patterns at the edge in real-time. The system of 250 edge devices on a network in this study simulated the environment of the smart infrastructure, which portrays a medium-sized U.S. urban grid. All of these devices were able to record important performance and system data on an ongoing basis over more than 30 days at a resolution of 10 seconds, and provide more than 60 million data points. Prominent variables that are recorded are CPU usage (%)/memory load (MB) and energy level (Watts), which is a reflection of the device in terms of operation strain and efficiency. So that edge workloads can be classified according to their energy consumption rates and usage trends to facilitate energy-efficient scheduling. Three supervised machine learning models were chosen: Logistic Regression, Random Forest Classifier, and Support Vector Classifier (SVC). The preprocessed dataset was divided into 80:20 train and test sets to ensure that there was no data leakage, and all three models were trained on the datasets and evaluated on the test set. Based on the measurement, Random Forest had the most accurate predictions, meaning that it tended to slightly outdo the other models in this comparison. The next two models, notably logistic Regression and SVM, respectively, had the lowest accuracy of the three models. The encountered blockchain mechanism, i.e., lightweight transaction ledgers including Hyperledger Sawtooth, offered informative transparency and traceability of energy behavior in edge networks. Introducing blockchain-based green edge computing is about to change the energy management approach in smart cities and intelligent energy grids in the U.S. The introduction of IoT-powered networks in metropolitan areas such as New York City, San Francisco, and Chicago, including traffic sensors and adaptive lighting, autonomous transportation, and Wi-Fi hotspots, has also meant that the energy requirements of distributed edge networks are being placed at a serious burden. Green edge computing with blockchain has an important role in defense and the safety of the population by assuring safe, energy-saving decision-making in the field. The DOD (U.S Department of Defense) mainly depends on mobile and distributed sensor networks to perform surveillance of the theaters of operation, environmental tracking, and real-time information. The findings of the current research add value to the potential of AI-powered methods in increasing energy efficiency in edge computing solutions, especially when combined with blockchain frameworks.
Embedded finance represents a transformative shift in how financial services integrate within non-financial platforms, creating seamless user experiences that eliminate traditional friction points. This comprehensive article explores how companies have leveraged embedded payment infrastructures to create extensive ecosystems that transcend their original business models. The technical infrastructure powering these innovationsāincluding API-first banking, regulatory technology, and microservices architectureāenables real-time processing at scale while maintaining security and compliance. The evolution toward Super Apps demonstrates how financial transactions can become invisible utilities within broader digital experiences, while artificial intelligence enhances these platforms through predictive analytics and conversational interfaces. Despite technical challenges related to data security, scalability, and cross-border complexity, emerging trends including decentralized finance integration, context-aware services, and embedded insurance promise continued innovation in this rapidly developing field
The article examines key trends in the development of digital currencies in the context of the transformation of the global financial system. Based on a comparative analysis, central bank-controlled central digital currencies (CBDCs) and decentralized cryptocurrencies, primarily Bitcoin, are considered as alternative models of digital money. The fundamental differences between them are identified in terms of issuance mechanisms, level of regulatory support, degree of transparency and application of blockchain technologies.
Open access
Security, Politics, and Digital Transformation
Blockchain Technology Applications and Security
Economic, Social, and Public Health Issues in Russia and Globally
Artificial Intelligence (AI) is profoundly transforming cryptography by significantly enhancing cryptanalysis techniques and informing innovative cryptographic design approaches. This survey reviews recent advancements in applying deep learning methods to side-channel and differential fault analyses, demonstrating substantial improvements over traditional methods in attack efficiency, accuracy, and resilience. Additionally, it highlights breakthroughs such as neural differential cryptanalysis, which expand classical cryptanalytic boundaries. In cryptographic design, Generative Adversarial Networks (GANs) have successfully automated the creation of high-quality cryptographic primitives, particularly S-boxes. Furthermore, AI shows promise in post-quantum cryptography (PQC) by uncovering potential vulnerabilities and optimizing cryptographic parameters. Despite these advancements, challenges persist regarding data dependency, model generalization, and interpretability. Future research directions emphasize enhancing AI model explainability, creating standardized benchmarks, and integrating AI with emerging technologies such as quantum computing and zero-knowledge proofs.
Open access
Cryptographic Implementations and Security
Chaos-based Image/Signal Encryption
Physical Unclonable Functions (PUFs) and Hardware Security
In recent years, blockchains have been attracting attention because they are decentralized networks with transparency and trustworthiness. Generally, transactions on blockchain networks with higher transaction fees are processed preferentially compared to others. The processing fee varies significantly depending on other transactions; it is difficult to predict the fee, and it may be significantly high. These are major barriers to blockchain utilization. Although several consensus algorithms have been proposed to solve these problems, their performance has not been fully evaluated. In this study, we model a blockchain system with a base fee, such as in Ethereum, via a priority queueing model. To assess the modelās performance, we derive the stability condition, stationary probability, average number of customers, and average waiting time for each type of customer. In deriving the stability conditions, we propose a method that uses the theoretical values of the partial models. These theoretical values match well with those obtained from Monte Carlo simulations, confirming the validity of the analysis.
In recent years, the integration of smart contracts into supply chain management has garnered significant attention due to their potential to enhance efficiency, transparency, and reliability. This study explores the application of smart contracts within supply chains, focusing on their impact on inventory management, process optimization, and overall operational effectiveness. Through a comprehensive analysis of existing literature and case studies, we identify key benefits and challenges associated with implementing smart contracts in supply chain contexts. Our findings suggest that while smart contracts offer substantial improvements in automating processes and reducing errors, considerations regarding technological infrastructure and stakeholder readiness remain critical for successful adoption.ā
Scott Shackelford, Michael Mattioli, Jeffrey P. Prince, JoĆ£o Marinotti
Abstract The chapter explores the economic implications of the Metaverse, focusing on its underlying economic mechanisms, consumption patterns, supply and demand dynamics, and the potential coexistence with the physical world. It discusses the concept of scarcity in the digital realm, where some goods and services may exhibit scarcity due to physical constraints or costs, while others may not be scarce, due to digital replication, non-fungible tokens (NFTs), etc. The chapter also delves into the supply and demand of the Metaverse, distinguishing between infrastructure and virtual goods/services within it, and considers the potential emergence of one or multiple Metaverses. Potentially impactful factors include technological challenges, economies of scale, barriers to entry, and regulatory considerations. Additionally, it examines how the Metaverse and physical world may interact as complements, substitutes, or independently in terms of products and services.
Hina Binte Haq, Syed Taha Ali, A. G. Sal'Man, Patrick McCorry Ā· 5 authors
The Bitcoin mempool plays an integral role in transaction processing and propagation through the network. Frequent transaction congestion events, as well as spam and dust attacks can clog the mempool, leading to dropped transactions, processing delays, and increased transaction fees. Moreover, increasing transaction loads on the network result in higher resource costs to operate full nodes, thereby restricting Bitcoin's network footprint and negatively impacting its overall health and performance. In this paper, we present Carbyne, a novel mempool optimization scheme, which uses counting bloom filter constructions to adapt to increased transaction flows, thereby making nodes resilient to congestion and spam and dust attacks. We implement Carbyne in C++ and benchmark its performance using a novel data set of Bitcoin mempool activity over a 90-day period. We dramatically reduced the mempool's memory consumption by up to two orders of magnitude (from 300 MB to 3 MB) while verifying and forwarding transactions with 99.9% fidelity and a slight increase in computational load. We simulate extensive spam attacks on Carbyne and demonstrate that mempool loads of 1 GB can be accommodated in as little as 10 MB. Carbyne does not necessitate a hard fork, it will help deploy high-functioning nodes on resource-constrained platforms, and it may also be adapted to other cryptocurrencies.
Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationally intensive due to the large number of modular operations required. The lookup-table-based (LUT-based) approach, a ``space-for-time'' technique, reduces computational load by segmenting the input number into smaller bit groups, pre-computing modular reduction results for each segment, and storing these results in LUTs. While effective, this method incurs significant hardware overhead due to extensive LUT usage. In this paper, we introduce ALLMod, a novel approach that improves the area efficiency of LUT-based large-number modular reduction by employing hybrid workloads. Inspired by the iterative method, ALLMod splits the bit groups into two distinct workloads, achieving lower area costs without compromising throughput. We first develop a template to facilitate workload splitting and ensure balanced distribution. Then, we conduct design space exploration to evaluate the optimal timing for fusing workload results, enabling us to identify the most efficient design under specific constraints. Extensive evaluations show that ALLMod achieves up to $1.65\times$ and $3\times$ improvements in area efficiency over conventional LUT-based methods for bit-widths of $128$ and $8,192$, respectively.