ERC5521: rNFT

Referable Non-Fungible Tokens

ERC 721 defines the Ethereum standard API to mint NFTs (Non-Fungible Tokens). While an NFT minted with ERC 721 represents something in the world standalone (e.g., an artwork, a copyright, or equipment in the metaverse), it does not reflect the reality/fact that many things in the world are related to or depend on each other, such as: (a) a film is based on a novel; (b) a song is from composing lyrics based on a piece of music, vice versa. Based on this observation, we proposed an extension of ERC 721, called Referable NFTs, by enabling a new NFT to refer 1-to-n the existing NFTs, according to their natural relationship. The proposal was submitted to the Ethereum Foundation in August 2022 and has been finalised in June 2024 and become an Ethereum standard now, i.e. ERC-5521. For details, please visit: https://eips.ethereum.org/EIPS/eip-5521

We believe a lot of real-world applications can leverage ERC-5521 to define/bind the things and their relationships. Like other ERC standards, ERC-5521’s application space would be only limited by our imagination.

Publications

2024
Maximizing NFT Incentives: References Make You Rich
Guangsheng Yu, Qin Wang, Caijun Sun, Lam Duc Nguyen, H. M. N. Dilum Bandara, Shiping Chen
CoRR abs/2402.06459
2024
Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage
Yilin Sai, Qin Wang, Guangsheng Yu, H. M. N. Dilum Bandara, Shiping Chen
CoRR abs/2404.06077
2024
Empowering Visual Artists with Tokenized Digital Assets with NFTs
Ruiqiang Li, Brian Yecies, Qin Wang, Shiping Chen, Jun Shen
CoRR abs/2409.11790
2023
A Referable NFT Scheme
Qin Wang, Guangsheng Yu, Shange Fu, Shiping Chen, Jiangshan Yu, Xiwei Xu
ICBC 2023: 1-6
2023
Predicting NFT Classification with GNN: A Recommender System for Web3 Assets
Guangsheng Yu, Qin Wang, Tanzeela Altaf, Xu Wang, Xiwei Xu, Shiping Chen
ICBC 2023: 1-5

Use Cases

AI Model vs. Training Data
AI Model vs. Training Data
High-quality and licensed training data is essential to an AI model’s effectiveness, accuracy, and ethical compliance, shaping everything from performance to legal standards. Using ERC-5521, we can envision an AI model as an NFT that “references” its training datasets, similar to how a Referable NFT can link to others. These NFTs can well represent the AI model's dependency on specific datasets, which fundamentally impact its quality, reliability, and compliance. The AI model's integrity is thus transparent and trustworthy with the provenance and quality of its underlying data sources.
Referable AI Models
Referable AI Models
ERC-5521’s *Referable NFT* model offers a sharp analogy for AI development, where each AI model is a “referable” layer, drawing value from preceding models. Like an NFT referencing others for contextual depth, each AI model builds on foundational models, creating a lineage that enriches its capabilities and scope. This referenced chain ensures a clear line of provenance, highlighting the importance of high-quality data and inheritance at each step. The AI’s transparency and functionality are strengthened, with each reference adding depth, compliance, and reliability to the evolving model.
Artworks Remix
Artworks Remix
Using ERC-5521 for artwork remix allows NFTs to reference original pieces, creating a structured lineage for each derivative work. This standard enables remix artists to acknowledge and link back to the original NFT sources, preserving provenance and establishing transparent connections between new and existing art. This setup benefits artists and collectors by highlighting the evolution of a piece through its remixes, supporting collaborative creativity while safeguarding the original work’s attribution and artistic integrity.
Referable Education Contents
Referable Education Contents
Using ERC-5521 for lecture course development allows lecturers to reference the materials they’ve drawn upon—such as existing lecture notes, tutorials, and lab exercises—by creating a traceable lineage of educational resources. With each course module structured as a *Referable NFT*, educators can transparently showcase the origins of their content, giving due credit and fostering collaborative academic environments. This approach supports resource sharing while maintaining clear attribution, which benefits educators, students, and creators of original content alike.

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