Ke Zhang, Xiaoning Zhao, Chaocheng Zheng, Jiahong Ning ¡ 8 authors
This study proposes Tool-RoCo, a novel benchmark for evaluating large language models (LLMs) in long-term multi-agent cooperation based on RoCo, a multi-robot cooperative benchmark. Recent research on LLM-based multi-agent systems has relied on predefined orchestration, while ignoring agent autonomy. Tool-RoCo treats other agents as tools and introduces cooperative tools, leveraging tool usage to evaluate multi-agent cooperation and self-organization. Tool usage means that each agent (LLM) selects a tool from a candidate set based on the current state, receives feedback, and adjusts its selection in subsequent rounds. To evaluate different autonomy levels, we propose four LLM paradigms: (1) centralized cooperation, where a single LLM allocates tools to all agents; (2) centralized self-organization, where a central LLM autonomously activates agents while keeping others inactive; (3) decentralized cooperation, where each agent has its own LLM and calls tools based on local information; and (4) self-organization, where a randomly chosen initial agent can request collaboration, activating additional agents via tool calls. Tool-RoCo includes three multi-robot tasks, SORT, PACK, and CABINET, to measure format and parameter accuracy and agent coordination through tool usage. The results using several LLMs showed that cooperative tools accounted for only 7.09% of all tools, indicating that LLM-based agents rarely invoked others as assistants. Moreover, activation tools accounted for 96.42%, suggesting that current LLMs tend to maintain active agents while seldom deactivating them for adaptive coordination. Tool-RoCo provides a systematic benchmark to evaluate LLM autonomy and cooperation in multi-agent tasks. Code and Demo: https://github.com/ColaZhang22/Tool-Roco
We step outside the P = NP vs. P â NP dichotomy and, following a co-evolutionary, hypothesis-first program, we frame evidence by the accounting constraint P(L, t) + NP(L, t) = 1, where t indexes registered time windows and L indexes structural layers of analysis. The credit assigned to constructive computation P(L, t) versus certificate-based reasoning NP(L, t) may shift across windows and layers, but their sum is conserved by design. Within this multilayer, time-indexed lens, we propose a test object for proof in 3-SAT: a small, auditable branching set K. Our operational hypothesis is that, within controlled experimental windows, there exists K â V(F) with |K| ⤠c¡log n such that, for every partial assignment Îą: K â {0,1}, the restricted formula F ⣠ι terminates in polynomial time and emits a publicly verifiable certificate (a satisfying assignment or a DRAT/DRUP-style unsatisfiability proof). Because 2^|K| = n^O(1), exhaustive branching over K is polynomial inside the window, enabling artifact-backed constructive behavior without asserting a universal algorithm. We (i) define auditable objects and falsifiable hypotheses, (ii) sketch a Ď-rounds normalization pipeline that contracts structure while logging transformations, (iii) posit a finite catalog of local obstructions with radius-2 witnesses, (iv) outline a greedy hitting-set routine to assemble K, and (v) introduce protection mechanisms against recovery of K by an adversary (commitments and zero-knowledge). Evidence will be supplied via reproducible artifacts (DRAT logs, commitments, run ledgers) and transport tests across registered windows and layers, and will be interpreted under the constraint P(L, t) + NP(L, t) = 1, in a manner consistent with kernelization barriers and sparsification limits.
Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy, particularly in untrusted environments. Although parameter-efficient methods like Low-Rank Adaptation (LoRA) significantly reduce resource requirements, ensuring the security and verifiability of fine-tuning under zero-knowledge constraints remains an unresolved challenge. To address this, we introduce VeriLoRA, the first framework to integrate LoRA fine-tuning with zero-knowledge proofs (ZKPs), achieving provable security and correctness. VeriLoRA employs advanced cryptographic techniques -- such as lookup arguments, sumcheck protocols, and polynomial commitments -- to verify both arithmetic and non-arithmetic operations in Transformer-based architectures. The framework provides end-to-end verifiability for forward propagation, backward propagation, and parameter updates during LoRA fine-tuning, while safeguarding the privacy of model parameters and training data. Leveraging GPU-based implementations, VeriLoRA demonstrates practicality and efficiency through experimental validation on open-source LLMs like LLaMA, scaling up to 13 billion parameters. By combining parameter-efficient fine-tuning with ZKPs, VeriLoRA bridges a critical gap, enabling secure and trustworthy deployment of LLMs in sensitive or untrusted environments.
In the rapidly evolving landscape of digital marketing and electronic commerce, short-form contentâparticularly on platforms like Twitter (now X)âhas become pivotal for real-time branding, community engagement, and product promotion. The rise of Non-Fungible Tokens (NFTs) and Web3 ecosystems further underscores the need for domain-specific, engagement-oriented social media content. However, automating the generation of such content while balancing linguistic quality, semantic relevance, and audience engagement remains a substantial challenge. To address this, we propose RL-TweetGen, a socio-technical framework that integrates instruction-tuned large language models (LLMs) with reinforcement learning (RL) to generate concise, impactful, and engagement-optimized tweets. The framework incorporates a structured pipeline comprising domain-specific data curation, semantic classification, and intent-aware prompt engineering, and leverages Parameter-Efficient Fine-Tuning (PEFT) with LoRA for scalable model adaptation. We fine-tuned and evaluated three LLMsâLLaMA-3.1-8B, Mistral-7B Instruct, and DeepSeek 7B Chatâguided by a hybrid reward function that blends XGBoost-predicted engagement scores with expert-in-the-loop feedback. To enhance lexical diversity and contextual alignment, we implemented advanced decoding strategies, including Tailored Beam Search, Enhanced Top-p Sampling, and Contextual Temperature Scaling. A case study focused on NFT-related tweet generation demonstrated the practical effectiveness of RL-TweetGen. Experimental results showed that Mistral-7B achieved the highest lexical fluency (BLEU: 0.2285), LLaMA-3.1 exhibited superior semantic precision (BERT-F1: 0.8155), while DeepSeek 7B provided balanced performance. Overall, RL-TweetGen presents a scalable and adaptive solution for marketers, content strategists, and Web3 platforms seeking to automate and optimize social media engagement. The framework advances the role of generative AI in digital commerce by aligning content generation with platform dynamics, user preferences, and marketing goals.
Ana-Maria Istrate, Fausto MilletarÏ, Fabrizio Castrotorres, Jakub M. Tomczak ¡ 7 authors
Abstract Reasoning models are typically trained against verification mechanisms in formally specified systems such as code or symbolic math. In open domains like biology, however, we lack exact rules to enable large-scale formal verification and instead often rely on lab experiments to test predictions. Such experiments are slow, costly, and cannot scale with computation. In this work, we show that world models of biology or other prior knowledge can serve as approximate oracles for soft verification , allowing reasoning systems to be trained without additional experimental data. We present two paradigms of training models with approximate verifiers: RLEMF : reinforcement learning with experimental model feedback and RLPK : reinforcement learning from prior knowledge. Using these paradigms, we introduce rbio1 , a reasoning model for biology post-trained from a pretrained LLM with reinforcement learning, using learned biological models for verification during training. We demonstrate that soft verification can distill biological world models into rbio1 , enabling it to achieve state-of-the-art performance on perturbation prediction in the PerturbQA benchmark. We further show that composing multiple AI-verifiers improves performance and that models trained with soft biological rewards transfer zero-shot to cross-domain tasks such as disease-state prediction. We present rbio1 as a proof of concept that predictions from biological models can train powerful reasoning systems using simulations rather than experimental data, offering a new paradigm for model training.
Fatou Ndiaye Mbodji, Mame Marieme C. Sougoufara, WendkÝuni A. M. Christian Ouedraogo, Alioune Diallo ¡ 7 authors
Smart contract comment generation has gained traction as a means to improve code comprehension and maintainability in blockchain systems. However, evaluating the quality of generated comments remains a challenge. Traditional metrics such as BLEU and ROUGE fail to capture domain-specific nuances, while human evaluation is costly and unscalable. In this paper, we present evalSmarT, a modular and extensible framework that leverages large language models (LLMs) as evaluators. The system supports over 400 evaluator configurations by combining approximately 40 LLMs with 10 prompting strategies. We demonstrate its application in benchmarking comment generation tools and selecting the most informative outputs. Our results show that prompt design significantly impacts alignment with human judgment, and that LLM-based evaluation offers a scalable and semantically rich alternative to existing methods.ResourcesVideo Demo: https://youtu.be/HXS_Yiszoz4Code and Data: https://anonymous.4open.science/r/SC_code_summarization-4653
Abderahman Rejeb, Karim Rejeb, Heba F. Zaher, Steve Simske
This paper explores the intersection of blockchain technology and smart cities to support the transition toward decentralized, secure, and sustainable urban systems. Drawing on co-word analysis and BERTopic modeling applied to the literature published between 2016 and 2025, this study maps the thematic and technological evolution of blockchain in urban environments. The co-word analysis reveals blockchainâs foundational role in enabling secure and interoperable infrastructures, particularly through its integration with IoT, edge computing, and smart contracts. These systems underpin critical urban services such as transportation, healthcare, energy trading, and waste management by enhancing data privacy, authentication, and system resilience. The application of BERTopic modeling further uncovers a shift from general technological exploration to more specialized and sector-specific applications. These include real-time mobility systems, decentralized healthcare platforms, peer-to-peer energy exchanges, and blockchain-enabled drone coordination. The results demonstrate that blockchain increasingly supports cross-sectoral innovation, enabling transparency, trust, and circular flows in urban systems. Overall, the current study identifies blockchain as both a technological backbone and an ethical infrastructure for smart cities that supports secure, adaptive, and sustainable urban development.
As large language models (LLMs) are used in sensitive fields, accurately verifying their computational provenance without disclosing their training datasets poses a significant challenge, particularly in regulated sectors such as healthcare, which have strict requirements for dataset use. Traditional approaches either incur substantial computational cost to fully verify the entire training process or leak unauthorized information to the verifier. Therefore, we introduce ZKPROV, a novel cryptographic framework allowing users to verify that the LLM's responses to their prompts are trained on datasets certified by the authorities that own them. Additionally, it ensures that the dataset's content is relevant to the users' queries without revealing sensitive information about the datasets or the model parameters. ZKPROV offers a unique balance between privacy and efficiency by binding training datasets, model parameters, and responses, while also attaching zero-knowledge proofs to the responses generated by the LLM to validate these claims. Our experimental results demonstrate sublinear scaling for generating and verifying these proofs, with end-to-end overhead under 3.3 seconds for models up to 8B parameters, presenting a practical solution for real-world applications. We also provide formal security guarantees, proving that our approach preserves dataset confidentiality while ensuring trustworthy dataset provenance.
Purpose Cryptocurrencyâs novelty and volatilityâcombined with the absence of standardized reporting prior to 2023âcreated an opaque information environment. This study explores whether such conditions enabled assertive impression management in corporate reporting. We examine how firms not only varied the volume of cryptocurrency disclosures over time, but also strategically manipulated their readability . Additionally, we use this context to demonstrate the utility of machine learning and natural language processing tools for consistent analysis of complex financial narratives. Study design We analyze full-text annual reports, MD&A sections, and proxy statements from five publicly traded U.S. firms with diverse cryptocurrency involvements. Our methodology includes machine learning-based topic modeling, readability assessment using standardized indices, and visualization tools. Findings (i) Information Demand: Google search trends for target firms are strongly associated with Bitcoin price movements, reflecting external attention cycles. (ii) Impression Management: Firms increase both the frequency and readability of crypto disclosures in favorable markets and reduce or obscure them in downturns, consistent with strategic impression management. (iii) Readability: Crypto-related disclosures are significantly more readable than non-crypto sections from the same reports suggesting deliberate simplification. Contributions This study advances the limited literature on cryptocurrency disclosure by offering a textual and behavioral lens on corporate impression management. A key contribution is the integration of readability metrics, public attention signals, and NLP tools into disclosure analysis. We highlight how firms use both narrative framing and readability engineering as tools to influence perceptionâespecially in periods of regulatory uncertainty. Implications Our findings have direct implications for policy and practice: (i) Policymakers should consider not only disclosure quantity but also its linguistic clarity and comparability, especially for volatile assets. (ii) Investors and analysts can use automated text analysis to detect subtle impression management tactics and to interpret the strategic use of clarity in disclosure narratives.
Md. Nahidul Islam Opu, Md Shahidul Islam, Sara Rouhani, Shaiful Chowdhury
Blockchain-based software systems are increasingly deployed across diverse domains, yet a systematic understanding of their development challenges remains limited. This paper presents a large-scale empirical study of 497,742 issues mined from 1,209 open-source blockchain projects hosted on GitHub. Employing BERTopic, a transformer-based topic modeling technique, we identify 49 distinct issue topics and organize them hierarchically into 11 major subcategories. Our analysis reveals that both general software development issues and blockchain-specific concerns are nearly equally represented, with Wallet Management and UI Enhancement emerging as the most prominent topics. We further examine the temporal evolution of issue categories and resolution times, finding that Wallet issues not only dominate in frequency but also exhibit the longest resolution time. Conversely, Mechanisms issues are resolved significantly faster. Issue frequency surged after 2016 with the rise of Ethereum and decentralized applications, but started declining after 2022. These findings enhance our understanding of blockchain software maintenance, informing the development of specialized tools and practices to improve robustness and maintainability.
Che, Zheng, Taoyu Li, Meng Shen, Hanbiao Du ¡ 5 authors
The untraceability of transactions facilitated by Ethereum mixing services like Tornado Cash poses significant challenges to blockchain security and financial regulation. Existing methods for correlating mixing accounts suffer from limited labeled data and vulnerability to noisy annotations, which restrict their practical applicability. In this paper, we propose StealthLink, a novel framework that addresses these limitations through cross-task domain-invariant feature learning. Our key innovation lies in transferring knowledge from the well-studied domain of blockchain anomaly detection to the data-scarce task of mixing transaction tracing. Specifically, we design a MixFusion module that constructs and encodes mixing subgraphs to capture local transactional patterns, while introducing a knowledge transfer mechanism that aligns discriminative features across domains through adversarial discrepancy minimization. This dual approach enables robust feature learning under label scarcity and distribution shifts. Extensive experiments on real-world mixing transaction datasets demonstrate that StealthLink achieves state-of-the-art performance, with 96.98\% F1-score in 10-shot learning scenarios. Notably, our framework shows superior generalization capability in imbalanced data conditions than conventional supervised methods. This work establishes the first systematic approach for cross-domain knowledge transfer in blockchain forensics, providing a practical solution for combating privacy-enhanced financial crimes in decentralized ecosystems.
This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.
The rapid advancement of Large Language Models (LLMs) has catalyzed the development of multi-agent systems, where multiple LLM-based agents collaborate to solve complex tasks. However, existing systems predominantly rely on centralized coordination, which introduces scalability bottlenecks, limits adaptability, and creates single points of failure. Additionally, concerns over privacy and proprietary knowledge sharing hinder cross-organizational collaboration, leading to siloed expertise. To address these challenges, we propose AgentNet, a decentralized, Retrieval-Augmented Generation (RAG)-based framework that enables LLM-based agents to autonomously evolve their capabilities and collaborate efficiently in a Directed Acyclic Graph (DAG)-structured network. Unlike traditional multi-agent systems that depend on static role assignments or centralized control, AgentNet allows agents to specialize dynamically, adjust their connectivity, and route tasks without relying on predefined workflows. AgentNetâs core design is built upon several key innovations: (1) Fully Decentralized Paradigm: Removing the central orchestrator, allowing agents to coordinate and specialize autonomously, fostering fault tolerance and emergent collective intelligence. (2) Dynamically Evolving Graph Topology: Real-time adaptation of agent connections based on task demands, ensuring scalability and resilience. (3) Adaptive Learning for Expertise Refinement: A retrieval-based memory system that enables agents to continuously update and refine their specialized skills. By eliminating centralized control, AgentNet enhances fault tolerance, promotes scalable specialization, and enables privacy-preserving collaboration across organizations. Through decentralized coordination and minimal data exchange, agents can leverage diverse knowledge sources while safeguarding sensitive information. Experimental results demonstrate that AgentNet outperforms traditional centralized multi-agent systems, significantly improving efficiency, adaptability, and scalability in dynamic environments, making it a promising foundation for next-generation autonomous, privacy-respecting multi-agent ecosystems.
In many-task optimization scenarios, surrogate models are valuable for mitigating the computational burden of repeated fitness evaluations across tasks. This study proposes a novel meta-surrogate framework to assist many-task optimization, by leveraging the knowledge transfer strengths and emergent capabilities of large language models (LLMs). We formulate a unified framework for many-task fitness prediction, by defining a universal model with metadata to fit a group of problems. Fitness prediction is performed on metadata and decision variables, enabling efficient knowledge sharing across tasks and adaptability to new tasks. The LLM-based meta-surrogate treats fitness prediction as conditional probability estimation, employing a unified token sequence representation for task metadata, inputs, and outputs. This approach facilitates efficient inter-task knowledge sharing through shared token embeddings and captures complex task dependencies via multi-task model training. Experimental results demonstrate the model's emergent generalization ability, including zero-shot performance on problems with unseen dimensions. When integrated into evolutionary transfer optimization (ETO), our framework supports dual-level knowledge transfer -- at both the surrogate and individual levels -- enhancing optimization efficiency and robustness. This work establishes a novel foundation for applying LLMs in surrogate modeling, offering a versatile solution for many-task optimization.
The Shakti series of 100M, 250M, and 500M models offers compact, resource-efficient language models designed for edge AI deployment. Unlike large models like GPT-3 and LLaMA that demand cloud-based infrastructure, Shakti models operate seamlessly on low-resource devices, including smartphones, smart TVs, IoT systems, drones, and low-end GPUs. They ensure minimal energy consumption, privacy-preserving computation, and real-time performance without internet dependency. Optimized for efficiency, Shakti models come in quantized versions (int8, int5, int4) for even faster, lighter execution on edge devices. The 2.5B Shakti model has demonstrated strong performance while maintaining low latency, paving the way for the smaller, highly efficient 100M, 250M, and 500M models. Built on Responsible AI principles, Shakti prioritizes fairness, transparency, and trust while mitigating risks such as bias, privacy concerns, and high carbon footprints. These models are ideal for sensitive domains like finance, healthcare, and legal services, providing cost-effective, sustainable, and scalable AI solutions with on-device data security. Each model is tailored for specific applications. Shakti-100M excels in text generation, summarization, and chatbots for IoT and mobile apps. Shakti-250M specializes in domain-specific tasks such as contract analysis and personalized financial or healthcare advice. Shakti-500M, a versatile model, enhances customer support, content creation, and virtual assistants with multilingual capabilities and long-context understanding. By decentralizing AI, the Shakti series democratizes access to intelligent, ethical, and impactful AI solutions across industries.
Blockchain technologies are increasingly recognized as a transformative force across industries, offering potential solutions for information management, data security, and operational efficiency improvement. However, integration into the healthcare sector faces significant barriers, ranging from technical challenges to organizational resistance. In this study, a methodology is proposed that examines these critical barriers through a comprehensive analysis of 3265 academic papers and derives actionable solutions from 1566 patents published between 2016 and 2023. This approach bridges the gap between identifying challenges and implementing solutions. Using the âStepwise Weight Assessment Ratio Analysis (SWARA)â, twelve critical adoption challenges are analyzed, while Top2Vec-based topic modeling identifies innovations that best address the ranked barriers. In addition to this, the proposed âhealthcare ecosphereâ knowledge map serves as a comprehensive tool to analyze key stakeholders, their interactions, and the alignment of adoption barriers in solution spaces. The findings show that innovations in blockchain technologies are heavily concentrated in areas such as data security and application functionalities, whereas other critical domainsâsuch as consensus mechanisms, governance, and regulatory frameworksâremain underexplored, pointing to opportunities for growth and development. The mapping of barriers to solutions provides practical guidance for healthcare providers, policymakers, and technologists seeking to implement the blockchain technologies effectively. ⢠Identified and ranked twelve key adoption barriers from technological and social contexts using SWARA methodology. ⢠Analyzed trends in blockchain patent grants to strategically address the barriers. ⢠Patent documents are clustered and mapped to address barriers using machine learning approach through Top2Vec. ⢠Noting patent grants are concentrated in specific areas of applications. ⢠Highlighted opportunities for innovation in under-explored blockchain technology layers.
The growing power of large language models (LLMs) has revolutionized how people access and utilize information. Notably, the LLMs excel at performing fine-grained data representation, which facilitates precise retrieval of information. They also generate high-quality answers based on external references, enabling the production of useful knowledge. The recent introduction of reasoning models, like OpenAI O1 and DeepSeek R1, marks another leap forward, highlighting LLMs' ability to think progressively before delivering final answers. This breakthrough significantly improves the ability to address complex tasks, e.g., coding and math proofs. Inspired by this progress, we aim to develop similar capabilities for retrieval models, which hold great promise for tackling critical challenges in the field, including multi-task retrieval, zero-shot retrieval, and tasks requiring intensive reasoning of complex relationships. With this motivation, we propose a novel approach called O1 Embedder, which generates useful thoughts for the input query before making retrieval for the target documents. To realize this objective, we conquer two technical difficulties. First, we design a data synthesis workflow, creating training signals for O1 Embedder by generating initial thoughts from an LLM-expert and subsequently refining them using a retrieval committee. Second, we optimize the training process, enabling a pre-trained model to be jointly fine-tuned to generate retrieval thoughts via behavior cloning and perform dense retrieval through contrastive learning. Our approach is evaluated by comprehensive experiments, where substantial improvements are achieved across 12 popular datasets, spanning both in-domain and out-of-domain scenarios. These results highlight O1 Embedder's remarkable accuracy and generalizability, paving the way for the development of next-generation IR foundation models.
Olanrewaju Oluwaseun Ajayi, Chisom Elizabeth Alozie, Olumese Anthony Abieba, Joshua Idowu Akerele ¡ 5 authors
Blockchain technology has emerged as a transformative force within the financial technology (Fintech) sector, offering unprecedented opportunities for efficiency, transparency, and security. However, its adoption also brings forth new challenges and vulnerabilities, particularly in the realm of cybersecurity. This review explores the dynamic landscape of Blockchain Technology and Cybersecurity in Fintech, highlighting both the opportunities it presents and the vulnerabilities it introduces. Blockchain technology, most notably recognized as the underlying framework for cryptocurrencies like Bitcoin and Ethereum, operates on a decentralized ledger system, enabling secure and immutable transactions. In Fintech, this technology promises enhanced transactional speed, reduced costs, and increased transparency, revolutionizing traditional banking and payment systems. Nevertheless, the decentralized nature of blockchain networks, while offering resilience against single points of failure, also poses unique cybersecurity risks. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, introduce vulnerabilities such as code bugs and exploits. Moreover, the anonymity associated with blockchain transactions has raised concerns regarding illicit activities, money laundering, and terrorist financing. In response to these challenges, the intersection of Blockchain Technology and Cybersecurity in Fintech offers opportunities for innovation. Advanced cryptographic techniques, such as multi-signature authentication and zero-knowledge proofs, are being leveraged to enhance security and privacy in blockchain-based systems. Additionally, regulatory frameworks are evolving to address the emerging risks associated with Fintech innovations, ensuring compliance and consumer protection. While Blockchain Technology presents promising opportunities for revolutionizing Fintech, its integration must be accompanied by robust cybersecurity measures to mitigate vulnerabilities and safeguard against potential threats. Collaborative efforts between industry stakeholders, regulators, and cybersecurity experts are imperative to foster a secure and resilient ecosystem for blockchain-based financial services.
Play-to-earn (P2E) games targeting users unfamiliar with cryptocurrencies are playing a key role within the industries known as blockchain, crypto or Web3. P2E gaming guilds (Elliott, 2021) are emerging as essential intermediaries bridging Web2 and Web3 ecosystems. Drawing from social network theoryâs study of brokerage motivations, this paper examines the structure and communication practices of a P2E guild, Yield Guild Games (YGG) on Twitter. Through a computational analysis of YGGâs presence on Twitter, the paper compares two mention networks corresponding to a period of optimism, and a period of crisis. We used network analysis to examine the structure of YGG communication on Twitter (Rathnayake, 2023), analysed tweets using BERTopic topic modeling (Grootendorst, 2022), and extracted links to determine what information is shared within the network (Hoyng, 2023). The results demonstrate YGGâs role as a âcultural brokerâ (Foster & Ocejo, 2015) promoting the adoption of blockchain technologies, such as non-fungible tokens (NFTs) and ideologies (Swartz, 2017), ascribing legitimacy and value to particular actors and products in the Web3 ecosystem. The topic lists highlight prominent communication practices related to community building, such as AMA (ask me anything) sessions and airdrops.
Abstract As blockchain technology advances, non-fungible tokens (NFTs) are emerging as unconventional assets in the commercial market. However, it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailing public concerns. This study aimed to bridge this gap by analyzing user-generated content on prominent social media platforms such as Twitter, Weibo, and Reddit. Employing text clustering and topic modeling techniques, such as Latent Dirichlet Allocation, we constructed an analytical framework to delve into the intricacies of the NFT ecosystem. Our investigation revealed seven distinct topics from Twitter and Reddit data and eight topics from Weibo data. Weibo users predominantly engaged in reviews and critiques, whereas Twitter and Reddit users emphasized personal experiences and perceptions. The NFT ecosystem encompasses several crucial elements, including transactions, customers, infrastructure, products, environments, and perceptions. By identifying the prevailing trends and common issues, this study offers valuable guidance for the development of NFT ecosystems.
The nexus of green finance and digital banking is transforming the world financial system on the twin pillars of environmental sustainability and technological innovation. Topic modeling is utilized in this study to examine nascent trends on the basis of a corpus of around 481 records of the Web of Science database. Six leading topics are: (1) Digital Financial Inclusion and Sustainable Development, (2) Green Finance and Digital Innovation, (3) Fintech and Sustainable Financial Services, (4) Climate and Environmental Sustainability Digital Banking, (5) Blockchain and Transparency in Sustainable Finance, and (6) AI and Big Data in Sustainable Financial DecisionMaking. Digital banking is enabling financial inclusion, especially in rural villages, and supporting the United National Sustainable Development Goals (SDGs). Fintech technologies such as mobile banking, blockchain, and AI are propelling access to green financial products, transparency, and climate risk analysis. Blockchain is providing traceability of green bond issuance, while AI-based tools are offering real-time analysis of sustainability risk. Fintech innovation such as ESG-driven robo-advisors are giving access to sustainable financial services to everyone and facilitating decentralized investment in clean energy projects. Yet, issues like digital literacy deficits, cyber-attacks, and the environmental cost of blockchain mining persist. Regulatory schemes must continue to change and meet these to facilitate the promotion of inclusive access to sustainable financial services. This essay points out the necessity of harmonized ESG reporting mechanisms, AI transparency in governance, and inclusive regulation for facilitating the incorporation of sustainability in electronic banking. The findings point out the transformative potential of digital technologies in remoulding sustainable finance with significant implications for financial institutions, regulators, and academics. Subsequent work must take note of developing technology like quantum computing and decentralized finance (DeFi) to continue advancing sustainable financial innovation.
Purpose This study aims to analyze public discourse on decentralized finance (DeFi) and central bank digital currencies (CBDC) using advanced natural language processing (NLP) techniques to uncover key insights that can guide financial policy and innovation. This research seeks to fill the gap in the existing literature by applying state-of-the-art NLP models like BERT and RoBERTa to understand the evolving online discourse around DeFi and CBDC. Design/methodology/approach This study uses a multilabel classification using BERT and RoBERTa models alongside BERTopic for topic modeling. Data is collected from social media platforms, including Twitter and LinkedIn, as well as relevant documents, to analyze public sentiment and discourse. Model performance is evaluated based on accuracy, precision, recall and F1-scores. Findings RoBERTa outperforms BERT in classification accuracy and precision across all metrics, making it more effective in categorizing public discourse on DeFi and CBDC. BERTopic identifies five key topics frequently discussed, such as financial inclusion, competition and growth in DeFi, with important implications for policymakers. Practical implications The insights derived from this study provide valuable information for financial regulators and policymakers to develop more informed, data-driven strategies for implementing and regulating DeFi and CBDC. Public discourse analysis enables policymakers to understand emerging concerns and trends critical for crafting effective financial policies. Originality/value This study is among the first to use advanced NLP models, including RoBERTa and BERTopic, to analyze public discourse on DeFi and CBDC. It offers novel insights into the potential challenges and opportunities these innovations present. It contributes to the growing body of research on the intersection of digital financial technologies and public sentiment.
Jan 1, 2025¡Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Andrea PeĂąa CalvĂn, David Duenas-Cid, Junaid Ahmed
This study analyzes voting dynamics and proposal outcomes within Decentraland, a prominent Decentralized Autonomous Organization (DAO), by examining its voting behaviors and decision outcomes. We offer insights into how a DAO is employed to facilitate decision-making and discern the nature of the issues about which decisions are made. DAOs promise horizontal and democratic decision-making. However, our research reveals a high concentration of voting power among a few members despite them not utilizing it to a great extent. Additionally, we identify the prevailing themes in decision-making processes within the organization through topic modeling. The primary topics identified are the effective management and governance of the platform and community and the platformâs strategic growth, with a particular emphasis on wearable technology. This research addresses fundamental questions regarding the democratic integrity of DAOs and their ability to achieve equitable representation and decision-making.