DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks
DeXposure-FM：预测去中心化金融网络信用敞口的时间序列图基础模型AI 解读 ↓
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DeXposure-FM：预测去中心化金融网络信用敞口的时间序列图基础模型
- 研究问题
- 面对代币连接形成的 DeFi 协议间信用依赖，如何预测协议资金流与敞口网络变化，并支持传染风险和系统重要性监测？
- 方法与思路
- 作者采用预训练初始化的图表格编码器与多个任务头，联合预测协议资金流和敞口边的拓扑、权重，再从预测结果计算风险指标。
- 材料陈述的贡献
- 摘要称，模型在两个机器学习基准中优于比较方法，并产生系统重要性、溢出及集中度指标，支持监测和情景压力测试。
- 阅读关注点
- 应核对代币中介信用敞口的定义、训练测试的时间切分、跨协议信息泄漏风险，以及预测误差怎样传递至压力测试与风险指标。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；基准优势与工具验证均为摘要陈述，未说明经济验证标准及极端情景可靠性。
原文摘要
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.