arXiv subjects

Rischan Mafrur

Publications and source records attributed to Rischan Mafrur.

9 recordsLinked to original sources

arXiv2026

RWA-PoB: A Credential-Based Proof-of-Backing Framework for Tokenized U.S. Treasury Products

Proof of reserves (PoR) can improve transparency for tokenized assets, but aggregate reserve coverage does not establish whether off-chain assets are legally eligible, unencumbered, consistently valued, or sufficiently liquid for redemptions. We propose RWA-PoB, a credential-based proof-of-backing framework for tokenized U.S. Treasury products. Five authorised institutional roles approve a canonical EIP-712 snapshot containing reserve, liability, liquidity, and policy information. The framework evaluates backing adequacy through the Backing Coverage Ratio (BCR) and short-term redemption capacity through the Redemption Liquidity Coverage (RLC). The Solidity prototype couples the policy controller to an ERC-20 token. Successful issuance atomically increases token supply and recorded liabilities by the corresponding USD-denominated liability. A redemption request burns tokens while reclassifying the corresponding obligation as pending. The obligation is reduced only after an authorised settlement-role account confirms payment. We evaluate the framework against a simplified aggregate PoR baseline using USDY-calibrated liabilities and deterministic synthetic reserve scenarios. Both approaches permit issuance in the valid state, but RWA-PoB rejects an encumbered-assets state with a BCR of 96.3408%, below the experimental 105% threshold. Under liquidity stress, it classifies the proposed redemption as queued because the post-request RLC falls to 39.9999%. RWA-PoB authenticates the attribution and integrity of institutional claims but does not independently prove the existence, ownership, or condition of off-chain assets. The prototype, test suite, datasets, and replication scripts are available at https://github.com/rischanlab/PoB.

cs.CE↗

arXiv2026

Toward Decentralized Carbon Trading in Indonesia: A Public-Blockchain Architecture for Tokenized Real-World Assets

Indonesia has established a regulated carbon market supported by national registry infrastructure and the IDXCarbon exchange. Carbon units can be issued, recorded, traded, and retired within this framework. IDXCarbon currently uses a private blockchain for its trading infrastructure. This creates an opportunity to examine how Indonesian carbon credits could also be represented and traded through public blockchain infrastructure. This study proposes an architecture for tokenizing Indonesian carbon credits as real-world assets (RWAs), with particular focus on Sertifikat Pengurangan Emisi Gas Rumah Kaca (SPE-GRK). The proposed architecture retains the Sistem Registri Unit Karbon (SRUK) as the authoritative source of carbon-unit status. It introduces a public-blockchain layer for token representation and programmable transactions. The architecture is designed to support lifecycle management, token-based asset representation, public observability of token activity, interoperability, wallet-based transactions, and programmable settlement. The architecture consists of four layers: the authoritative carbon layer, the registry interoperability and tokenization layer, the public-blockchain RWA layer, and the market and application layer. Access to the tokenized carbon assets remains regulated. Token issuance and transfers are linked to participant eligibility and registry status. Retirement also remains dependent on the authoritative carbon registry. The proposed architecture provides a framework for introducing public-blockchain RWA infrastructure into Indonesia's existing carbon market while maintaining SRUK authority and existing market-integrity controls.

cs.CE↗

arXiv2026

Tokenized but Illiquid? Evidence from Real-World Asset Markets

Real-world asset tokenization is often presented as a mechanism for improving the liquidity of traditionally illiquid assets. However, on-chain representation and secondary-market liquidity are distinct outcomes. This paper examines whether tokenized real-world assets exhibit meaningful observed liquidity and identifies the token characteristics associated with higher market activity. Using token-level data from RWA.xyz and supplemental contract-level observations from Etherscan, the study constructs an Ethereum-based monthly panel of non-stablecoin real-world assets across three prominent categories: U.S. Treasury-backed tokens, gold-backed commodity tokens, and private-credit-related tokens. Liquidity is measured using turnover, active addresses, and an active-month indicator. The empirical design combines descriptive statistics, non-parametric group tests, and exploratory panel regressions suited to short and sparse token histories. The results show substantial heterogeneity across asset categories. Gold-backed tokens exhibit broader holder bases and more persistent on-chain activity than many Treasury and private-credit-related products, while outstanding asset value alone does not reliably predict observed liquidity. The paper contributes to the literature by developing a clearer empirical measurement framework for real-world-asset liquidity and showing that tokenization and liquidity should be analyzed as distinct outcomes.

cs.CE↗

arXiv2026

From Agent Identity to Agent Economy: Measuring the Operational Readiness of ERC-8004 AI Agents

This paper examines whether blockchain-registered AI agents demonstrate operational readiness beyond identity registration. Using a dataset of ERC-8004 agents on Ethereum, we construct an agent-level feature table covering identity status, metadata, service declarations, reputation feedback, transfers, and cross-chain registration. We develop an operational readiness framework based on observable evidence layers and complement it with network analysis of owner-agent, feedback-client, wallet-transfer, and combined evidence relationships. The results show that early ERC-8004 adoption is registration-heavy but operationally shallow. While the identity layer is visible at scale, metadata availability, service exposure, reputation formation, and cross-chain evidence remain limited. Ownership and feedback activity are also highly concentrated, suggesting that early participation is shaped by a small number of high-activity wallets and clients. The network analysis further shows that richer operational evidence clusters around a small subset of agents rather than being broadly distributed across the ecosystem. The findings suggest that ERC-8004 provides an important identity layer for decentralized AI agents, but the transition from agent identity to agent economy remains incomplete.

cs.CE↗

arXiv2026

Beyond TVL: An Explainable Risk Scoring Framework for Tokenized Real-World Assets

Tokenized real-world assets (RWAs) are often evaluated through headline indicators such as total value locked (TVL) or on-chain asset value. However, a large asset base does not necessarily imply low risk, since tokenized assets may remain illiquid, weakly traded, or highly concentrated among a small number of holders. Using public data from RWA.xyz, this paper develops an empirical and explainable risk scoring framework for tokenized RWA markets. The framework evaluates three dimensions of risk: liquidity risk $L$, concentration risk $C$, and market-quality risk $M$. These risk dimensions are constructed from observable indicators, including turnover, holder distribution, active-address activity, transfer frequency, and network concentration measured through Herfindahl indices. The analysis shows that several RWA tokens with substantial on-chain value exhibit high empirical risk because they combine limited transfer activity, low turnover, and concentrated ownership structures. In contrast, assets with broader participation and stronger on-chain activity display lower liquidity and concentration risk, even when their headline asset values are smaller. The findings demonstrate that TVL alone can obscure important risks in tokenized asset markets. By providing a transparent and data-driven risk scoring approach, this paper contributes to the empirical assessment of RWA liquidity and offers a practical basis for comparing tokenized assets beyond headline valuation metrics.

cs.CE↗

arXiv2025

Blockchain Data Analytics: Review and Challenges

The integration of blockchain technology with data analytics is essential for extracting insights in the cryptocurrency space. Although academic literature on blockchain data analytics is limited, various industry solutions have emerged to address these needs. This paper provides a comprehensive literature review, drawing from both academic research and industry applications. We classify blockchain analytics tools into categories such as block explorers, on-chain data providers, research platforms, and crypto market data providers. Additionally, we discuss the challenges associated with blockchain data analytics, including data accessibility, scalability, accuracy, and interoperability. Our findings emphasize the importance of bridging academic research and industry innovations to advance blockchain data analytics.

cs.CR↗

arXiv2025

Tokenize Everything, But Can You Sell It? RWA Liquidity Challenges and the Road Ahead

The tokenization of real-world assets (RWAs) promises to transform financial markets by enabling fractional ownership, global accessibility, and programmable settlement of traditionally illiquid assets such as real estate, private credit, and government bonds. While technical progress has been rapid, with over \$25 billion in tokenized RWAs brought on-chain as of 2025, liquidity remains a critical bottleneck. This paper investigates the gap between tokenization and tradability, drawing on recent academic research and market data from platforms such as RWA.xyz. We document that most RWA tokens exhibit low trading volumes, long holding periods, and limited investor participation, despite their potential for 24/7 global markets. Through case studies of tokenized real estate, private credit, and tokenized treasury funds, we present empirical liquidity observations that reveal low transfer activity, limited active address counts, and minimal secondary trading for most tokenized asset classes. Next, we categorize the structural barriers to liquidity, including regulatory gating, custodial concentration, whitelisting, valuation opacity, and lack of decentralized trading venues. Finally, we propose actionable pathways to improve liquidity, ranging from hybrid market structures and collateral-based liquidity to transparency enhancements and compliance innovation. Our findings contribute to the growing discourse on digital asset market microstructure and highlight that realizing the liquidity potential of RWAs requires coordinated progress across legal, technical, and institutional domains.

q-fin.GN↗

arXiv2025

AI-Based Crypto Tokens: The Illusion of Decentralized AI?

The convergence of blockchain and artificial intelligence (AI) has led to the emergence of AI-based tokens, which are cryptographic assets designed to power decentralized AI platforms and services. This paper provides a comprehensive review of leading AI-token projects, examining their technical architectures, token utilities, consensus mechanisms, and underlying business models. We explore how these tokens operate across various blockchain ecosystems and assess the extent to which they offer value beyond traditional centralized AI services. Based on this assessment, our analysis identifies several core limitations. From a technical perspective, many platforms depend extensively on off-chain computation, exhibit limited capabilities for on-chain intelligence, and encounter significant scalability challenges. From a business perspective, many models appear to replicate centralized AI service structures, simply adding token-based payment and governance layers without delivering truly novel value. In light of these challenges, we also examine emerging developments that may shape the next phase of decentralized AI systems. These include approaches for on-chain verification of AI outputs, blockchain-enabled federated learning, and more robust incentive frameworks. Collectively, while emerging innovations offer pathways to strengthen decentralized AI ecosystems, significant gaps remain between the promises and the realities of current AI-token implementations. Our findings contribute to a growing body of research at the intersection of AI and blockchain, highlighting the need for critical evaluation and more grounded approaches as the field continues to evolve.

cs.DC↗

arXiv2023

VizPut: Insight-Aware Imputation of Incomplete Data for Visualization Recommendation

In insight recommendation systems, obtaining timely and high-quality recommended visual analytics over incomplete data is challenging due to the difficulties in cleaning and processing such data. Failing to address data incompleteness results in diminished recommendation quality, compelling users to impute the incomplete data to a cleaned version through a costly imputation strategy. This paper introduces VizPut scheme, an insight-aware selective imputation technique capable of determining which missing values should be imputed in incomplete data to optimize the effectiveness of recommended visualizations within a specified imputation budget. The VizPut scheme determines the optimal allocation of imputation operations with the objective of achieving maximal effectiveness in recommended visual analytics. We evaluate this approach using real-world datasets, and our experimental results demonstrate that VizPut effectively maximizes the efficacy of recommended visualizations within the user-defined imputation budget.

cs.DB↗