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Experiments on five dynamic graph benchmarks show that CacheDyG adopts substantially fewer trainable parameters and lower runtime to obtain more competitive predictive performance than baselines, demonstrating that cache-based decoupling provides an effective principle for scalable dynamic graph learning.
