Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He
arXiv 3 Feb 2026 · Machine Learning
arXiv:2602.03981 · PDF · DOI · OpenAlex · Extracted main text
Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure-FM, the first time-series, graph foundation model for measuring and forecasting inter-protocol credit exposure on DeFi networks, to the best of our knowledge. Employing a graph-tabular encoder, with pre-trained weight initialization, and multiple task-specific heads, DeXposure-FM is trained on the DeXposure dataset that has 43.7 million data entries, across 4,300+ protocols on 602 blockchains, covering 24,300+ unique tokens. The training is operationalized for credit-exposure forecasting, predicting the joint dynamics of (1) protocol-level flows, and (2) the topology and weights of credit-exposure links. The DeXposure-FM is empirically validated on two machine learning benchmarks; it consistently outperforms the state-of-the-art approaches, including a graph foundation model and temporal graph neural networks. DeXposure-FM further produces financial economics tools that support macroprudential monitoring and scenario-based DeFi stress testing, by enabling protocol-level systemic-importance scores, sector-level spillover and concentration measures via a forecast-then-measure pipeline. Empirical verification fully supports our financial economics tools. The model and code have been publicly available. Model: https://huggingface.co/EVIEHub/DeXposure-FM. Code: https://github.com/EVIEHub/DeXposure-FM.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jeremy Bertomeu and Xiumin Martin and Ibrahima Sall (2024) Measuring DeFi risk | 0.843 | 3 | 3 | 100% |
| 2 | Dmitry Eremeev and Gleb Bazhenov and Oleg Platonov and Artem Babenko… (2025) Turning Tabular Foundation Models into Graph Foundation Models | 0.843 | 3 | 3 | 100% |
| 3 | Auer, Raphael and Faragò, Marco and Turi, Davide (2025) Towards Verifiability of Total Value Locked (TVL) in Decentralized Finance | 0.737 | 3 | 2 | 100% |
| 4 | European Systemic Risk Board (2025) Crypto-assets and decentralised finance: Report on stablecoins, crypto-investment products and multi-function groups | 0.737 | 3 | 2 | 100% |
| 5 | Krzysztof Gogol and Christian Killer and Malte Schlosser and Thomas… (2024) SoK: Decentralized Finance (DeFi) – Fundamentals, Taxonomy and Risks | 0.737 | 3 | 2 | 100% |
| 6 | Wenbin Wu and Kejiang Qian and Alexis Lui and Christopher Jack and Y… (2025) DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks self | 0.737 | 3 | 2 | 100% |
| 7 | Battiston, Stefano and Puliga, Michelangelo and Kaushik, Rahul and T… (2012) DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk | 0.644 | 2 | 2 | 100% |
| 8 | Eisenberg, Larry and Noe, Thomas H (2001) Systemic Risk in Financial Systems | 0.644 | 2 | 2 | 100% |
| 9 | Kingma, Diederik P. and Ba, Jimmy (2015) Adam: A Method for Stochastic Optimization | 0.644 | 2 | 2 | 100% |
| 10 | Thomas N. Kipf and Max Welling (2017) Semi-Supervised Classification with Graph Convolutional Networks | 0.644 | 2 | 2 | 100% |
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