Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910147614343168 |
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| author | Luo, Junliang Tinn, Katrin Duran, Samuel Ferreira Wu, Di Liu, Xue |
| author_facet | Luo, Junliang Tinn, Katrin Duran, Samuel Ferreira Wu, Di Liu, Xue |
| contents | Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing instruments issued across multi-chain Web3 infrastructures, with growing significance for transparency, accessibility, and financial inclusion. While the market has expanded rapidly, empirical analyses of transaction-level behaviours remain limited. This paper conducts a quantitative, function-level dissection of U.S. Treasury-backed RWA tokens, including BUIDL, BENJI, and USDY across multi-chain: mostly Ethereum and Layer-2s. Decoded contract calls expose core financial primitives such as issuance, redemption, transfer, and bridging, revealing patterns that distinguish institutional participants from smaller or retail users for the extent and limits of inclusivity in current RWA adoption. To infer address-level economic roles, we introduce a curvature-aware representation learning model. Our method outperforms baseline models in role inference on our collected U.S. Treasury transaction dataset and generalizes to address classification across broader public blockchain transaction datasets. The decoded transaction-level patterns in tokenized U.S. Treasuries across chains surface the degree of retail participation, and the role inference model enables the distinction between institutional treasuries, arbitrage bots, and retail traders based on behavioral patterns, facilitating future more transparent, inclusive, and accountable Web3 finance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14808 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference Luo, Junliang Tinn, Katrin Duran, Samuel Ferreira Wu, Di Liu, Xue Computational Finance Computational Engineering, Finance, and Science Machine Learning Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing instruments issued across multi-chain Web3 infrastructures, with growing significance for transparency, accessibility, and financial inclusion. While the market has expanded rapidly, empirical analyses of transaction-level behaviours remain limited. This paper conducts a quantitative, function-level dissection of U.S. Treasury-backed RWA tokens, including BUIDL, BENJI, and USDY across multi-chain: mostly Ethereum and Layer-2s. Decoded contract calls expose core financial primitives such as issuance, redemption, transfer, and bridging, revealing patterns that distinguish institutional participants from smaller or retail users for the extent and limits of inclusivity in current RWA adoption. To infer address-level economic roles, we introduce a curvature-aware representation learning model. Our method outperforms baseline models in role inference on our collected U.S. Treasury transaction dataset and generalizes to address classification across broader public blockchain transaction datasets. The decoded transaction-level patterns in tokenized U.S. Treasuries across chains surface the degree of retail participation, and the role inference model enables the distinction between institutional treasuries, arbitrage bots, and retail traders based on behavioral patterns, facilitating future more transparent, inclusive, and accountable Web3 finance. |
| title | Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference |
| topic | Computational Finance Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2507.14808 |