发表机构
University of South Carolina（南卡罗来纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用源侧训练动态，通过逻辑回归预测器等方法，证明其可作为未来OOD故障的早期预警信号，为OOD退化的前瞻性预测提供了原理性支撑。
AI 中文摘要
我们研究是否仅利用源侧训练动态，就能在持续分布外（OOD）退化被直接观测到之前对其进行预测。在受控的捷径学习设置中，简单的逻辑回归预测器会产生清晰的前瞻性信号，而仅靠训练时间则无法做到这一点。源侧量的时间摘要比其当前值更具信息性。当从CNN迁移到MLP且不进行额外训练时，置信度和熵动态仍保留大量预测信息。这些结果为源侧训练动态可包含未来OOD故障的早期预警信号这一原理提供了证明。
英文摘要
We study whether persistent out-of-distribution (OOD) degradation can be predicted before it is directly observed using only source-side training dynamics. In a controlled shortcut-learning setting, a simple logistic regression predictor develops a clear prospective signal, while training time alone does not. Temporal summaries of the source-side quantities are substantially more informative than their current values. When transferred without additional training from a CNN to an MLP, confidence and entropy dynamics retain substantial predictive information. These results provide a proof of principle that source-side training dynamics can contain an early warning signal for future OOD failure.
