An Adaptive NFT Fractionalization Framework with Rights Segregation and Dynamic Rights Management

Abstract

The tokenization of real-world assets (RWAs) through non-fungible tokens (NFTs) has introduced new opportunities for liquidity, enabling fractional ownership of traditionally illiquid assets. Yet, current NFT fractionalization models remain static, lacking adaptive governance and real-time responsiveness required for managing the dynamic nature of RWAs. This paper presents an Adaptive NFT Fractionalization Framework with Rights Segregation that integrates modular smart contracts, oracle data, and machine learning (ML) insights to enable dynamic rights management. The framework segregates and defines distinct rights, governed through cross-layer decision-making and adaptive rights management that updates allocations based on market data and predictive analytics. Experimental results demonstrate accurate, real-time adjustments of fractional rights, consistent governance execution, and efficient gas utilization across stress and concurrency tests. The findings validate the framework's scalability, responsiveness, and cost-effectiveness, establishing it as a viable approach for adaptive, data-driven management of fractionalized RWAs.