A new method called Credit-in-Event identifies and addresses temporal credit dilution in learned dynamics models. CREST, a label-free and training-free readout, re-anchors pooled representations by estimating transient event cores and applying event-versus-rest contrast, reducing out-of-distribution error across multiple systems and data types. Ablations confirm the improvement stems from event-core credit re-anchoring, not generic locality or stability priors.