Transaction Profiling and Address Role Inference in Tokenized U.S. Treasuries

ArXiv ID: 2507.14808 “View on arXiv”

Authors: Junliang Luo, Katrin Tinn, Samuel Ferreira Duran, Di Wu, Xue Liu

Abstract

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.

Keywords: Tokenized Treasuries, Real-World Assets (RWA), Blockchain Analytics, Poincaré Embeddings, On-chain Financialization

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced, curvature-aware representation learning using Poincaré embeddings, a non-Euclidean geometry technique, indicating significant mathematical complexity. Concurrently, it demonstrates high empirical rigor through large-scale, multi-chain data collection (millions of transactions), structured transaction decoding, and validation on both custom and public datasets for downstream tasks.

  flowchart TD
    A["Research Goal: Analyze transaction-level behaviour<br>in Tokenized U.S. Treasuries"] --> B["Data Collection & Decoding<br>Multi-chain RWA tokens: BUIDL, BENJI, USDY"]
    B --> C["Methodology: Functional Dissection<br>Isolate Issuance, Redemption, Transfer, Bridge"]
    C --> D["Computational Process:<br>Poincaré Embeddings & Graph Features"]
    D --> E["Outcome 1: Role Inference<br>Segmentation of Institutional vs Retail Actors"]
    E --> F["Outcome 2: Model Validation<br>Outperforms Baselines in Anomaly Detection"]
    F --> G["Outcome 3: Generalization<br>Applicable to broader blockchain networks"]