In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we come across an unexpected finding. That is, the current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. We propose, analyze, and implement a novel reference incentive model, which is inherently structured as a Directed Acyclic Graph (DAG)-based NFT network. This model aims to maximize connections (or references) between NFTs, enabling each isolated NFT to expand its network and accumulate rewards derived from subsequent or subscribed ones. We conduct both theoretical and practical analyses of the model, demonstrating its optimal utility.

翻译：本文研究了如何优化现有的非同质化代币（NFT）激励机制。在探索大量NFT相关标准和实际项目后，我们得出一个意外发现：当前NFT激励机制通常采用孤立且一次性的组织方式，往往忽视了其在可扩展组织结构方面的潜力。我们提出、分析并实现了一种新型引用激励机制，该机制本质上构建于基于有向无环图（DAG）的NFT网络之上。此模型旨在最大化NFT之间的连接（或引用），使每个孤立的NFT能够扩展其网络并累积来自后续或订阅NFT的奖励。我们对该模型进行了理论与实证分析，证明了其最优效用。