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.

翻译：代币化美国国债已成为现实世界资产（RWA）中的一个重要子类，它们提供基于密码学保护的收益型工具，并在多链Web3基础设施上发行，对透明度、可访问性和金融普惠性具有重要意义。尽管市场迅速扩张，但对交易层面行为的实证分析仍然有限。本文对基于美国国债支持的RWA代币（包括BUIDL、BENJI和USDY）在多条链（主要是以太坊和Layer-2）上进行了定量、功能层面的解构。解码后的合约调用揭示了发行、赎回、转移和跨链桥接等核心金融原语，展示了区分机构参与者与小型或零售用户的模式，揭示了当前RWA采纳的包容性程度与局限性。为推断地址层面的经济角色，我们引入了一种曲率感知表示学习模型。我们的方法在我们收集的美国国债交易数据集上的角色推断任务中优于基线模型，并能泛化到更广泛的公共区块链交易数据集上的地址分类任务。跨链代币化美国国债的解码交易层面模式揭示了零售参与的程度，而角色推断模型能够基于行为模式区分机构金库、套利机器人和零售交易者，从而促进未来更透明、包容和负责任的Web3金融。