While Ethereum has successfully achieved dynamic availability together with safety, a fundamental delay remains between transaction execution and immutable finality. In Ethereum's current Gasper protocol, this latency is on the order of 15 minutes, exposing the network to ex ante reorganization attacks, enabling MEV extraction, and limiting the efficiency of economic settlement. These limitations have motivated a growing body of work on Speedy Secure Finality (SSF), which aims to minimize confirmation latency without weakening formal security guarantees. This paper surveys the state of the art in fast finality protocol design. We introduce the core theoretical primitives underlying this space, including reorganization resilience and the generalized sleepy model, and trace their development from Goldfish to RLMD-GHOST. We then analyze the communication and aggregation bottlenecks faced by single-slot finality protocols in large validator settings. Finally, we survey the 3-slot finality (3SF) protocol as a practical synthesis that balances fast finality with the engineering constraints of the Ethereum network.
Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and upside potential profit from the first principles of mining: Each hash is a Bernoulli trial with a Bitcoin block difficulty-based success probability. The model yields closed-form expected revenue per hash-rate unit, risk metrics in different scenarios, and upside-profit probabilities for different fleet sizes. Empirical calibration closely matches previously reported observations, yielding a unified, faithful quantification across hardware, pools, and operating conditions. This foundation enables more reliable analysis of mining impacts and behavior.
Rapid advancements in quantum computing and machine learning threaten the long-term security of classical blockchain systems, whose protection mechanisms largely rely on computational difficulties. In this study, we propose a quantum blockchain protocol whose protection mechanism is directly derived from quantum mechanical principles. The protocol combines high-dimensional Bell states, time-entanglement, entanglement switching, and high-dimensional superdense coding. Encoding classical block information into time-delimited qudit states allows block identity and data verification to be implemented through the causal sequencing of quantum measurements instead of cryptographic hash functions. High-dimensional coding increases the information capacity per quantum carrier and improves noise resistance. Time-entanglement provides distributed authentication, non-repudiation, and tamper detection across the blockchain. Each block derives its own public-private key pair directly from the observed quantum correlations by performing high-dimensional Bell state measurements in successive time steps. Because these keys are dependent on the time ordering of measurements, attempts to alter block data or disrupt the protocol's timing structure inevitably affect the reconstructed correlations and are revealed during validation. Recent advances in the creation and detection of high-dimensional time-slice entanglement demonstrate that the necessary quantum resources are compatible with emerging quantum communication platforms. Taken together, these considerations suggest that the proposed framework can be evaluated as a viable and scalable candidate for quantum-secure blockchain architectures in future quantum network environments.
Accurate and interpretable forecasting of multivariate time series is crucial for understanding the complex dynamics of cryptocurrency markets in digital asset systems. Advanced deep learning methodologies, particularly Transformer-based and MLP-based architectures, have achieved competitive predictive performance in cryptocurrency forecasting tasks. However, cryptocurrency data is inherently composed of long-term socio-economic trends and local high-frequency speculative oscillations. Existing deep learning-based 'black-box' models fail to effectively decouple these composite dynamics or provide the interpretability needed for trustworthy financial decision-making. To overcome these limitations, we propose DecoKAN, an interpretable forecasting framework that integrates multi-level Discrete Wavelet Transform (DWT) for decoupling and hierarchical signal decomposition with Kolmogorov-Arnold Network (KAN) mixers for transparent and interpretable nonlinear modeling. The DWT component decomposes complex cryptocurrency time series into distinct frequency components, enabling frequency-specific analysis, while KAN mixers provide intrinsically interpretable spline-based mappings within each decomposed subseries. Furthermore, interpretability is enhanced through a symbolic analysis pipeline involving sparsification, pruning, and symbolization, which produces concise analytical expressions offering symbolic representations of the learned patterns. Extensive experiments demonstrate that DecoKAN achieves the lowest average Mean Squared Error on all tested real-world cryptocurrency datasets (BTC, ETH, XMR), consistently outperforming a comprehensive suite of competitive state-of-the-art baselines. These results validate DecoKAN's potential to bridge the gap between predictive accuracy and model transparency, advancing trustworthy decision support within complex cryptocurrency markets.
Industry 4.0 technologies are accelerating the digital transformation of financial systems, reshaping money, payment infrastructures, and the strategic role of central banks. This study examines the emergence of Central Bank Digital Currencies (CBDCs) within this evolving landscape, exploring the evolution of payment systems, fintech integration, and the implications of distributed ledger technology and private cryptocurrencies. Using qualitative content analysis of secondary data, the paper compares the approaches of the U.S. Federal Reserve, the Bank of England, and the South African Reserve Bank to CBDC design, adoption, and regulation. Findings highlight shared policy concerns including cybersecurity, privacy, regulatory gaps, financial inclusion, and the need for international interoperability while revealing notable differences in institutional priorities and pace of development. The study underscores that central banks stand at a pivotal moment: their responses to Industry 4.0 innovations and digital currency initiatives will shape future monetary stability and the global financial order.
The immutable nature of smart contracts necessitates rigorous auditing, especially for ERC compliance, to prevent significant economic losses. While automated tools, particularly those combining Large Language Models (LLMs) with symbolic execution, have improved detection, they often suffer from false positives, false negatives, and insufficient interpretability. This paper introduces SymExplainer, a novel integrated framework designed to overcome these limitations. SymExplainer features an LLM-Enhanced Rule Semantic Extraction Module that deeply understands ERC specifications and misuse patterns using multi-stage prompting and a domain-specific knowledge base. Its Context-Aware Symbolic Execution Engine then efficiently prioritizes exploration paths based on these LLM insights. Crucially, a Violation Verification and Interpretability Generation Module performs secondary LLM-based cross-validation to significantly reduce false positives and produces comprehensive, natural language reports detailing "why," "where," and "how-to-fix" confirmed violations. Evaluated on a ground-truth dataset of 159 expert-annotated ERC violations, SymExplainer achieved perfect recall with zero false negatives and substantially reduced false positives to only 15, outperforming state-of-the-art methods like SymGPT (which reported 29 false positives and 1 false negative). An ablation study confirmed the critical contribution of each module, and qualitative human evaluation validated the high clarity, accuracy, and actionability of its interpretability reports. Despite a modest increase in computational cost, SymExplainer provides a more precise, reliable, and transparent solution for smart contract auditing through unparalleled accuracy, reduced noise, and actionable insights.
The Local Energy Market (LEM) is a key element in the energy sector's transition toward a decentralized system, enabling the integration of a growing number of small generation sources and energy storage facilities located at end-user locations.By utilizing digital energy trading platforms provided by LEMs, small consumers, producers, and prosumers actively participate in system balancing, which, among others, allows them to increase profits and energy independence.The efficiency of energy exchange in LEM is achieved by means of optimization methods that make use of sensitive participant data, such as energy consumption profiles.Therefore, ensuring privacy while simultaneously ensuring trust in the achieved optimal quantitative and qualitative results is crucial.The classic technology used in decentralized systems, i.e., blockchain, does not provide adequate scalability when transactions result from solving optimization problems.In this article, we analyze the possibilities of verifying optimization results by the use of cryptographic zero-knowledge proofs (ZKP).We explain how ZKP can support privacy and enable verification of computations without the need of repeating them for every participant.We also refer to existing ZKP implementations on LEM, while highlighting the barriers of high computational costs that prevent direct implementation of complex optimization algorithms within ZKP protocols.'To overcome these barriers, we present an approach integrating ZKP with optimality certificates, which has significant potential to increase the efficiency of
Diana Georges Freiha, Márcia Michele Garcia Duarte
A transformação digital criou um cenário no qual as transações comerciais se desenvolvem de forma instantânea, valendo-se de intermediários digitais. Os contratos inteligentes ou smart contracts emergem como uma resposta tecnológica a essa nova realidade que pressupõe respostas muitas das vezes transfonteiriças. Com a promessa de oferecimento da transação comercial com baixo custo operacional e célere, vale-se da linguagem comum, o código, para a execução das relações contratuais por intermédio de cláusulas autoexecutáveis. Esses modelos de contrato tomam papel de destaque a partir da apontada crise suportada pelo Poder Judiciário frente à sobrecarga de processo, passando a ser vistos como uma promissora ferramenta de desjudicialização. Defende-se, no presente trabalho, contudo, que o uso de ferramentas inteligentes deve ser feito com cautela, diante dos inúmeros problemas que podem surgir, pois não se deve obliterar que se trata de um campo novo a ser explorado, em que ainda não há acuidade nos resultados gerados pelos artefatos inteligentes. Isso poderá ocasionar um efeito adverso, ou seja, intensificar ainda mais a procura pelo Poder Judiciário, principalmente diante das hipóteses de incidência de erro de programação ou vício de consentimento. A metodologia a ser empregada está assentada na pesquisa bibliográfica pela análise teórica.
ABSTRACT Based on the rationale that returns and volatility are interrelated, we apply a multilayer network framework involving the return layer and volatility layer of cryptocurrencies, NFTs, and DeFi assets over the period January 1, 2018–January 23, 2024. The results show significant connectedness in each of the return and volatility layers, with major cryptocurrencies such as Bitcoin and Ethereum playing a central role. Large spikes in the level of connectedness are noticed around COVID‐19 pandemic and Russia–Ukraine conflict, and Bitcoin and Ethereum emerge as net transmitters of returns and volatility shocks, emphasizing their significant role around these crisis periods. Notably, a strong positive rank correlation exists between the return and volatility layers, highlighting the significant risk–return relationship in the digital asset class. The findings suggest that economic actors should not ignore the interconnectedness between the return and volatility layers in the system of cryptocurrencies, NFTs, and DeFi assets for the sake of a comprehensive analysis of information flow. Otherwise, a share of the information flow concerning the return–volatility nexus across these digital assets would be missed, possibly leading to inferences regarding asset pricing, portfolio allocation, and risk management.
With the rapid proliferation of smart home cameras, wearable vision devices, and user-generated Consumer Internet of Things (CIoT) content, ensuring visual media authenticity, rightful ownership, and tamper detection has become increasingly challenging. We propose Chain-Visage, a blockchain-assisted framework for secure content authentication and tamper tracing in decentralized CIoT multimedia ecosystems. The termChainreflects the consortium blockchain backbone that guarantees immutable provenance, decentralized ownership management, and copyright revocation, whileVisagesymbolizes the unique visual identity of multimedia content achieved through dual-stage visual hash embedding. The proposed framework integrates Zero-Knowledge Proofs (ZKPs) for privacy-preserving ownership verification and employs optimized smart contracts to manage visual rights, provenance records, and ownership transfers efficiently. Evaluations on a large-scale dataset of over 10,000 real-world images and 3,850 video clips from diverse CIoT devices demonstrate Chain-Visage’s superior performance, achieving 97.5% traceability accuracy, 93% tamper detection sensitivity, and low verification latency even under resource-constrained environments. This work addresses a critical research gap in secure, privacy-preserving, and energy-efficient multimedia ownership control and tamper-resilient content authentication for next-generation CIoT ecosystems.
Open access
Blockchain Technology Applications and Security
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
A. Rehash Rushmi Pavitra, R. Radha, R. Satheesh Kumar, Montater MuhsnHasan · 6 authors
The management of resource sharing agreements is being transformed by the introduction of smart contracts, which, alongside decentralized technologies, provide smoother automation, transparency, and trust amongst different parties. This research examines the role that smart contract management systems play in the design, implementation, and control of resource-sharing agreements in the fields of energy, telecommunications, transportation, and digital services. Conventional contract-based practices are plagued by inefficiencies, potential errors, and delays, which smart contracts aim to address by encoding agreement terms into self-executing code stored within blockchain systems. The study examines key architectural building blocks, consensus models, and security elements, focusing on the real-time execution of automated validation, updates, dispute resolution, and contract performance. Practical applications are presented through case studies on decentralized energy markets, bandwidth leasing, and co-utilization of assets. Other concerns are the lack of interconnected systems, enforcement, and private legal structures. The research develops a smart contract lifecycle management model that regulates contracting processes to help organizations develop adequate, compliant, and collaborative resource distribution solutions designed to be scalable. The economic model of spending changes due to the ability of smart contracts, utilizing Blockchain, to share resources, thereby reducing administrative expenses and establishing more resilient mechanisms of dependence in the future.
In the digital era, consumers increasingly encounter an illusion of ownership when purchasing copyrighted works such as video games, digital music albums, or e-books. Under dominant licensing models exacerbated by cloud computing and subscription services users acquire mere access rights rather than true property interests, rendering their acquisitions vulnerable to platform shutdowns, account terminations, or service discontinuations. This phenomenon marks the “vanishing ownership” of digital content, eroding the traditional balance struck by the First Sale Doctrine in U.S. copyright law and the Exhaustion Principle in EU law. This article examines the failure of these doctrines to adapt to digital distribution, as evidenced by landmark cases. It further explores emerging challenges and opportunities posed by cloud-based services and Non-Fungible Tokens (NFTs), which promise transferable digital ownership but raise unresolved questions about copyright exhaustion, resale rights, and potential disruptions to rightholders’ licensing revenues. Through comparative legal analysis and doctrinal critique, this study argues for reconstructing the First Sale Doctrine and digital exhaustion to restore consumer property rights. It proposes hybrid legislative and technological solutions, including limited exhaustion for permanently downloaded works, mandatory resale mechanisms, and blockchain-enabled forward-and-delete protocols.
The rise of Internet 3.0, the metaverse, and virtual realities is accelerating the shift from a physical economy to one that is digital, decentralized, and globally accessible. While the benefits and detriments of virtual assets like non-fungible tokens (NFTs) have received attention, individuals’ opinions about them remain polarized. This study investigates how personality traits shape users’ perceived value of NFTs. Using survey data from 805 respondents, we examine how the Big Five traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) are associated with 14 value dimensions spanning technology, art, and product aspects. The findings indicate that perceptions of NFTs vary among users. Of note, individuals high in agreeableness and conscientiousness perceive NFTs more favorably across the spectrum of value dimensions, whereas those high in neuroticism exhibit opposite tendencies. Extraverted individuals are drawn to the subjective norms and financial gains related to NFTs, while those high in openness value their information transparency.
Open access
2 source records
Virtual Reality Applications and Impacts
Consumer Behavior in Brand Consumption and Identification
The rapid expansion of the Non-Fungible Token (NFT) market has underscored significant challenges in copyright protection and ownership authentication. While blockchain technology ensures the immutability and transparency of token transactions, the off-chain storage of metadata and original content remains a critical vulnerability, exposing NFTs to risks such as data loss, manipulation, and copyright disputes. In response to these challenges, this study proposes a blockchain-integrated watermarking framework that embeds resilient copyright information into digital assets via a general frequency-domain approach. The watermark is stored off-chain within the InterPlanetary File System (IPFS), while its associated Content Identifier (CID) is anchored in a smart contract, ensuring traceability of provenance and verification of authenticity. Comparative experiments with the Least Significant Bit (LSB) method demonstrate the superior robustness of the proposed frequency-domain technique against various attacks, including compression, noise, and image manipulation. The proposed framework significantly enhances copyright protection, facilitates transparent NFT provenance, and provides a scalable foundation for secure digital asset management within blockchain-based ecosystems.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Dec 23, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Tejas Kotha, Kushagra Bhatnagar, Leona Chandra Kruse, Matti Rossi
NFTs (non-fungible tokens) promised the interaction of artists/creators directly with their collectors without the need for any intermediaries, but the realisation was quick that such a technology, instead of getting rid of intermediaries, reintroduced new intermediaries in the form of NFT marketplaces. These marketplaces exhibit diverse features and cater to different user groups. A wide array of governance strategies, such as curation and gatekeeping, are used to steer creativity and interactions in the marketplace, informed by the marketplace's strategy. We examined this diversity by identifying the 'ideal types' of marketplaces based on these strategies alongside the motivations of the creators to make sense of the growing NFT market and constructed a typology that distinguishes four kinds of NFT marketplaces: Avant-garde, Canonical, Mass Culture, and Coterie. The article also offers practical implications for creators and collectors looking to make informed choices when deciding to participate in a particular marketplace.
Aaron Chan, Alex Ding, Frank Sicong Chen, Alan Wu · 6 authors
The rapid integration of Large Language Models (LLMs) into decentralized physical infrastructure networks (DePIN) is currently bottlenecked by the Verifiability Trilemma, which posits that a decentralized inference system cannot simultaneously achieve high computational integrity, low latency, and low cost. Existing cryptographic solutions, such as Zero-Knowledge Machine Learning (ZKML), suffer from superlinear proving overheads (O(k NlogN)) that render them infeasible for billionparameter models. Conversely, optimistic approaches (opML) impose prohibitive dispute windows, preventing real-time interactivity, while recent "Proof of Quality" (PoQ) paradigms sacrifice cryptographic integrity for subjective semantic evaluation, leaving networks vulnerable to model downgrade attacks and reward hacking. In this paper, we introduce Optimistic TEE-Rollups (OTR), a hybrid verification protocol that harmonizes these constraints. OTR leverages NVIDIA H100 Confidential Computing Trusted Execution Environments (TEEs) to provide sub-second Provisional Finality, underpinned by an optimistic fraud-proof mechanism and stochastic Zero-Knowledge spot-checks to mitigate hardware side-channel risks. We formally define Proof of Efficient Attribution (PoEA), a consensus mechanism that cryptographically binds execution traces to hardware attestations, thereby guaranteeing model authenticity. Extensive simulations demonstrate that OTR achieves 99% of the throughput of centralized baselines with a marginal cost overhead of $0.07 per query, maintaining Byzantine fault tolerance against rational adversaries even in the presence of transient hardware vulnerabilities.
Niccolò Scatena, Pericle Perazzo, Giovanni Nardini
This paper proposes iblock, a comprehensive C++ library for Bitcoin simulation, designed for OMNeT++. iblock offers superior efficiency and scalability with respect to state-of-the-art simulators, which are typically written in high-level languages. Moreover, the possible integration with other OMNeT++ libraries allows highly detailed simulations. We measure iblock's performance against a state-of-the-art blockchain simulator, proving that it is more efficient at the same level of simulation detail. We also validate iblock by using it to simulate different scenarios such as the normal Bitcoin operation and the selfish mine attack, showing that simulation results are coherent with theoretical expectations.
Blockchain consensus mechanisms are fundamental to the security and decentralization of distributed ledgers. In Proof-of-Stake (PoS) systems, which are lauded for their energy efficiency, the fair and unpredictable selection of block proposers is paramount and relies heavily on secure random number generation. The RANDAO random number generation mechanism in the Gasper protocol is susceptible to hash collision attack, which can introduce adversarial bias in the block proposer selection process. From the perspective of resisting adversarial bias attacks, this paper examines the optimization of the Gasper consensus protocol, focusing on security issues such as vulnerabilities to hash collisions in RANDAO and high latency in asynchronous network environments. By analyzing the spatial–temporal distribution of historical block hashes, we propose a dual-round random number verification mechanism that enhances reliability through multiple validation models. We develop a dynamic game-theoretic model under incomplete information to analyze node strategy selection and interaction dynamics. Our experimental results demonstrate that the improved protocol (RABA-Gasper) offers superior resistance to attacks, fairness, and efficiency compared to conventional protocols. RABA-Gasper outperforms conventional ones, achieving a 6.8% attack success rate (vs. 32.7% for RANDAO and 18.2% for Two Look-Back) with 94.3% hash collision detection, a proposer Gini coefficient below 0.23, 2.3x higher throughput retention than RANDAO in asynchronous networks, and a slightly increased random number generation latency of 125 ms. Supported by a game-theoretic model, it guarantees security when honest nodes account for ≥2/3 of the total.
Dec 23, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Oliver Alexy, Oliver Baumann, Ying-Ying Hsieh, Giorgia Sampó
Decentralized Autonomous Organizations (DAOs) represent a radical form of socio-technical systems, where rules are enforced by code and governance is conducted by a distributed network of stakeholders. A critical challenge in designing these systems is achieving consensus without centralized authority, yet how consensus ensures effective governance remains underexplored. This study investigates the design of DAO governance systems, utilizing data from 70 DAOs and applying Fuzzy Set Qualitative Comparative Analysis (fsQCA) to explore which consensus configurations lead to positive organizational outcomes. Our analysis challenges the notion of a single consensus model. Instead, we uncover 13 distinct configurations that characterize successful DAOs. Our key finding reveals a fundamental “ideation-legitimation trade-off”: successful DAOs optimize for broad participation in either the proposal (ideation) stage or the voting (legitimation) stage, but rarely both. These insights provide a nuanced framework for understanding and designing effective governance systems for DAOs.
This paper explores how Decentralized Autonomous Organizations (DAOs) could inform and shape participatory procedures in democratic governance. We apply DAO decision-making, such as rule-based input aggregation, transparent participation, and programmable decision-making, to a real-world case: the legislative development of the Swiss E-ID law, a proposal to establish a digital identity system for secure online authentication for Swiss residents. Using data from the official legislative consultation, we simulate how DAO-inspired mechanisms could have altered the aggregation of input and policy outcomes. Our analysis contributes conceptually and empirically to debates on digital democratic innovations, showing how programmable governance can be used not only to design new institutional forms, but also to critically assess the procedural dynamics of existing ones.
Dec 23, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Decentralized Autonomous Organizations (DAOs) integrate blockchain-based automation with novel forms of collective governance, yet research on them remains fragmented across technical and organizational silos, hindering a comprehensive understanding of DAOs as socio-technical systems. To bridge these gaps, we first conduct a systematic umbrella review of 12 prior surveys to map research themes and persistent gaps. Based on this analysis, we propose a novel, three-layer framework that explicitly links (i) technical artefacts (the infrastructure, e.g., tokens, smart contracts), (ii) governance logics (the rules, e.g., incentives, consensus mechanisms), and (iii) organizational manifestations (the outcomes, e.g., proposals, votes). By making cross-layer dependencies explicit, the framework enables more holistic theorizing, supports comparative empirical work, and provides a diagnostic tool for practitioners dealing with design trade-offs between decentralization, efficiency, and participation.
This paper measures price differences between Hegic option quotes on Arbitrum and a model-based benchmark built on Black--Scholes model with regime-sensitive volatility estimated via a two-regime MS-AR-(GJR)-GARCH model. Using option-level feasible GLS, we find benchmark prices exceed Hegic quotes on average, especially for call options. The price spread rises with order size, strike, maturity, and estimated volatility, and falls with trading volume. By underlying, wrapped Bitcoin options show larger and more persistent spreads, while Ethereum options are closer to the benchmark. The framework offers a data-driven analysis for monitoring and calibrating on-chain option pricing logic.
Ali Farahbakhsh, Giuliano Losa, Youer Pu, Lorenzo Alvisi · 5 authors
Permissionless blockchains achieve consensus while allowing unknown nodes to join and leave the system at any time. They typically come in two flavors: proof of work (PoW) and proof of stake (PoS), and both are vulnerable to attacks. PoS protocols suffer from long-range attacks, wherein attackers alter execution history at little cost, and PoW protocols are vulnerable to attackers with enough computational power to subvert execution history. PoS protocols respond by relying on external mechanisms like social consensus; PoW protocols either fall back to probabilistic guarantees, or are slow. We present Sieve-MMR, the first fully-permissionless protocol with deterministic security and constant expected latency that does not rely on external mechanisms. We obtain Sieve-MMR by porting a PoS protocol (MMR) to the PoW setting. From MMR we inherit constant expected latency and deterministic security, and proof-of-work gives us resilience against long-range attacks. The main challenge to porting MMR to the PoW setting is what we call time-travel attacks, where attackers use PoWs generated in the distant past to increase their perceived PoW power in the present. We respond by proposing Sieve, a novel algorithm that implements a new broadcast primitive we dub time-travel-resilient broadcast (TTRB). Sieve relies on a black-box, deterministic PoW primitive to implement TTRB, which we use as the messaging layer for MMR.
Decentralized Finance (DeFi) enables financial services to operate without centralized intermediaries, using smart contracts and blockchain consensus to ensure transparency and trust minimization. While DeFi protocols like Aave and MakerDAO use overcollateralization to mitigate credit risk, this approach creates capital inefficiencies and limits access to borrowers lacking on-chain assets. This paper introduces Inverum, a novel DeFi lending protocol designed to support undercollateralized loans for Web3 businesses and Decentralized Autonomous Organizations (DAOs). Inverum integrates on-chain credit scoring via soulbound tokens, decentralized liquidity pools, and governance-driven incentives to enable trustless, reputation-based lending. The protocol offers a fully composable framework for exploring undercollateralized lending without relying on traditional identity or off-chain reputation systems, contributing a research-ready model for future experimentation and protocol design.