Learned dynamics models often answer global physical questions, such as fault severity or impact stiffness, by pooling a per-step feature sequence into one readout vector. This sequence-to-global interface creates an under-studied temporal credit problem: with only trajectory-level supervision, a model can predict accurately in training conditions while reading from abundant smooth correlates rather than the brief physical events that determine the target. We call this failure temporal credit dilution. It is not exposed by the training loss and is not removed by standard physics-informed residuals, because the error lies in where the global readout assigns functional credit. We introduce Credit-in-Event, an interface-level probe for measuring how much pooled credit lands on event steps, and prove in closed form that a pooled linear reader routes credit to a spurious background channel as the event fraction shrinks. We then propose CREST, a training-free and label-free readout that estimates a transient event core from learned features and re-anchors the pooled representation through event-versus-rest contrast. Across simulated gear and impact systems, recurrent and attention encoders, and public bearing vibration data, CREST reduces out-of-distribution error while restoring event credit. Ablations show that stable-step selection and receptive-field shrinking fail, confirming that the gain comes from event-core credit re-anchoring rather than a generic locality or stability prior.

翻译：学习到的动力学模型通常通过将每步特征序列池化为一个读出向量，来回答全局物理问题（如故障严重程度或冲击刚度）。这种序列到全局的接口产生了一个尚未被充分研究的时间信用问题：仅依靠轨迹级监督，模型可能在训练条件下精确预测，但此时其读出的依据是丰富的平滑相关性而非决定目标的短暂物理事件。我们将这种失败称为时间信用稀释。该问题不会被训练损失暴露，也无法通过标准物理信息残差消除，因为错误在于全局读出分配功能信用的位置。我们提出Credit-in-Event——一种用于衡量池化信用落于事件步比例的接口级探针，并闭式证明了当事件占比缩小时，池化线性读出器会将信用导向虚假背景通道。继而提出CREST——一种免训练、无标签的读出方法，该方法从学习到的特征中估计瞬态事件核心，并通过事件与背景的对比来重新锚定池化表征。在模拟齿轮与冲击系统、循环与注意力编码器以及公开轴承振动数据上，CREST在恢复事件信用的同时减少了分布外误差。消融实验表明，稳定步选择与感受野缩小均无效，从而证实性能提升源自事件核心信用再锚定，而非通用的局部性或稳定性先验。