Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference

Abstract

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.

Figures (6)

• Figure 1: Log-scaled scatter plot of BUIDL token transactions by function and chain. Each point represents a (function, chain) pair, where the x-axis is the total BUIDL value transacted and the y-axis is the transaction count. Marker shape denotes functional class (e.g., transfer, issuetokens, redeem), and colour indicates a blockchain (e.g., Ethereum, Arbitrum).
• Figure 2: Function-level distribution of USDY transaction volume vs. frequency across chains. Each point represents a specific $(f,c)$ pair (function $f$ on chain $c$), with marker shape encoding functional category (e.g., swap, lending, execution) and colour denoting blockchain. Both axes use logarithmic scales. This figure reveals how USDY’s cross-chain activity exhibits distinct clusters of functional usage, highlighting protocol specialization (e.g., high-frequency swaps on Mantle vs. large-value mint and burn operations on Ethereum).
• Figure 3: Illustrative example of a blockchain transaction graph and its corresponding Poincaré disk embedding. Nodes emulate three‐tier addresses: the red address anchors global liquidity, the green addresses relay funds and manage liquidity pools, and a ring of peripheral trader blue addresses engage sporadically with the core. Embedding the same adjacency structure in the negatively curved Poincaré model separates tiers by geodesic radius.
• Figure 4: Illustration of PoincaVec embedding of addresses colored by labels from our U.S. Treasury dataset. Smaller hyperbolic radius indicates higher hierarchy (closer to the origin). Roles like Treasury and Bot appear near the origin, while Trader and Other are peripheral, near the edge.
• Figure 5: Precision@k curves on all five datasets, evaluated at k ranges from 0.05 to 0.60 in increments of 0.05 of the test set. Our proposed PoincaVec (w/ H, w/ T) variant demonstrates strong top-$k$ ranking performance across all the datasets, demonstrating the capacity to detect anomalous addresses of various blockchain transaction datasets.

Theorems & Definitions (1)

• proof