Transaction Profiling and Address Role Inference in Tokenized U.S. Treasuries
This work addresses the need for structured understanding of functional heterogeneity and participant roles in tokenized Treasuries, providing new empirical evidence for on-chain financialization, but it is incremental as it builds on existing methods for blockchain analysis.
This paper tackles the problem of limited empirical analysis of transaction-level behavior in tokenized U.S. Treasuries by conducting a quantitative dissection of tokens like BUIDL, BENJI, and USDY across multiple blockchains, revealing segmentation between institutional and retail users and introducing a curvature-aware representation learning framework that outperforms baselines in role inference and generalizes to tasks like anomaly detection.
Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically enforced, yield-bearing instruments collateralized by sovereign debt and deployed across multiple blockchain networks. While the market has expanded rapidly, empirical analyses of transaction-level behaviour 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. We analyze decoded contract calls to isolate core functional primitives such as issuance, redemption, transfer, and bridge activity, revealing segmentation in behaviour between institutional actors and retail users. To model address-level economic roles, we introduce a curvature-aware representation learning framework using Poincaré embeddings and liquidity-based graph features. Our method outperforms baseline models on our RWA Treasury dataset in role inference and generalizes to downstream tasks such as anomaly detection and wallet classification in broader blockchain transaction networks. These findings provide a structured understanding of functional heterogeneity and participant roles in tokenized Treasury in a transaction-level perspective, contributing new empirical evidence to the study of on-chain financialization.