提出三项新指标，区分模型适应与数据变难，更真实反映时间分布偏移下的性能变化。
Tracking Adaptation Time: Metrics for Temporal Distribution Shift
- 设计三类互补指标，捕捉模型随时间的适应能力。
- 揭示传统评估忽略的动态适应模式，提升对模型鲁棒性的理解。
- 适合关注时序数据中模型长期表现的研究者使用。
在时间分布偏移下的鲁棒性评估仍是开放挑战。现有指标仅量化性能平均下降，却无法捕捉模型对演化数据的适应过程。因此，当准确率下降时，难以判断是模型未能适应，还是数据本身变得更难学习。本文提出三个互补的度量指标，以区分模型适应与数据内在难度。这些指标共同提供了模型在时间分布偏移下的动态、可解释行为视图。实验表明，我们的指标揭示了传统分析中隐藏的适应模式，为理解动态环境中的时间鲁棒性提供了更丰富的视角。
原文摘要 · Abstract (English)
Evaluating robustness under temporal distribution shift remains an open challenge. Existing metrics quantify the average decline in performance, but fail to capture how models adapt to evolving data. As a result, temporal degradation is often misinterpreted: when accuracy declines, it is unclear whether the model is failing to adapt or whether the data itself has become inherently more challenging to learn. In this work, we propose three complementary metrics to distinguish adaptation from intrinsic difficulty in the data. Together, these metrics provide a dynamic and interpretable view of model behavior under temporal distribution shift. Results show that our metrics uncover adaptation patterns hidden by existing analysis, offering a richer understanding of temporal robustness in evolving environments.
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