通过动态系统建模预测模型参数随时间变化，实现无需重新训练的自适应推理。
Koopman Operator Identification of Model Parameter Trajectories for Temporal Domain Generalization (KOMET)
- 将模型参数序列视为非线性动力系统轨迹，用EDMD识别其线性控制算子。
- 在6个数据集上对100步未来参数轨迹预测准确率达0.981~1.000。
- 适合长期部署中面临分布漂移的场景，如工业监测、环境感知等。
在非平稳环境中部署的参数化模型会因数据分布随时间演化而性能下降（即时间域漂移）。本文提出KOMET（Koopman算子识别时间漂移下模型参数演化），一种模型无关、数据驱动的框架，将训练得到的参数向量序列视为非线性动力系统的轨迹，利用扩展动态模态分解（EDMD）识别其主导线性算子。通过暖启动序列训练协议确保参数轨迹平滑，并采用傅里叶增强的可观测函数字典捕捉许多真实世界分布漂移中的周期结构。一旦识别出Kooppman算子，即可自主预测未来参数轨迹，无需未来标注数据，实现部署时的零重训练自适应。在涵盖旋转、振荡和扩张分布几何的6个数据集上，KOMET在100个保留时间步上的平均自主回放准确率达到0.981至1.000。谱分析与耦合分析进一步揭示了与漂移决策边界几何一致的可解释动力学结构。
原文摘要 · Abstract (English)
Parametric models deployed in non-stationary environments degrade as the underlying data distribution evolves over time (a phenomenon known as temporal domain drift). In the current work, we present KOMET (Koopman Operator identification of Model parameter Evolution under Temporal drift), a model-agnostic, data-driven framework that treats the sequence of trained parameter vectors as the trajectory of a nonlinear dynamical system and identifies its governing linear operator via Extended Dynamic Mode Decomposition (EDMD). A warm-start sequential training protocol enforces parameter-trajectory smoothness, and a Fourier-augmented observable dictionary exploits the periodic structure inherent in many real-world distribution drifts. Once identified, KOMET's Koopman operator predicts future parameter trajectories autonomously, without access to future labeled data, enabling zero-retraining adaptation at deployment. Evaluated on six datasets spanning rotating, oscillating, and expanding distribution geometries, KOMET achieves mean autonomous-rollout accuracies between 0.981 and 1.000 over 100 held-out time steps. Spectral and coupling analyses further reveal interpretable dynamical structure consistent with the geometry of the drifting decision boundary.
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