DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks

TL;DR

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, for measuring and forecasting inter-protocol credit exposure on DeFi networks.

Abstract

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.

Figures (5)

• Figure 1: Multi-layer credit exposure in DeFi. A user stakes ETH with Lido, receiving stETH, which is wrapped into wstETH and deposited into Pendle. Each protocol holds the liability of the preceding one, creating a chain of credit exposures from Pendle to Lido.
• Figure 2: Predictive contagion stress testing (Task II). We compare system loss (%) under identical shocks when running the contagion simulator on the observed network at time $t$ (persistence baseline), the model-predicted network $\hat{G}_{t+h}$, and the realized future network $G_{t+h}$.
• Figure 3: Advantage regimes for predictive stress testing. Bars report $\Delta\mathrm{MAE}=\mathrm{MAE}(\text{baseline})-\mathrm{MAE}(\text{model})$ in system loss (% points). "Overall" is the full test set; "Worst 20%" restricts to the 20% largest baseline errors (tail regime). Positive values indicate DeXposure-FM outperforms persistence, highlighting where a learned forecaster adds value when the persistence assumption breaks.
• Figure 4: Forward-looking risk metric forecasting on predicted exposure graphs. Each point compares a realized metric on $G_{t+h}$ (x-axis) to the same metric computed on the predicted graph $\hat{G}_{t+h}$ (y-axis), across horizons.
• Figure 5: Early warning event studies. For each event window, we compare predicted vs. realized risk concentration (HHI) and predictive stress-test loss (contagion system loss %).

Theorems & Definitions (2)

• Definition 1: binary cross-entropy (BCE)
• Definition 2: smooth L1 loss