Memory-based temporal graph neural networks (TGN) rely on recurrent node memories to capture evolving interaction histories. However, temporal mini-batching introduces a trade-off between computational efficiency and temporal fidelity: when a node appears multiple times within the same batch, later interactions are evaluated using stale pre-batch memory because the intermediate memory updates have not yet been committed. This problem becomes more severe as temporal batch size increases. To overcome this challenge, we propose AnchorRM, an anchor-based residual memory mechanism that reconstructs event-specific intra-batch node states without executing sequential recurrent updates. AnchorRM treats the last committed node memory at the beginning of each batch as a pre-batch fixed anchor and uses causally preceding same-batch interactions to infer the missing state evolution relative to that anchor. The learned residual then calibrates the anchor to reconstruct an event-specific node memory for each interaction. Because each event-specific residual is predicted directly from the fixed anchor and its causal history, reconstructed states do not depend on previously reconstructed states and can therefore be computed in parallel within the batch while preserving temporal causality. Experiments across four temporal graph datasets and three memory-based backbones demonstrate that AnchorRM achieves an average 6.16x training speedup (up to 10.05x) and 28.67x batch scaling (up to 40x). These results demonstrate that AnchorRM substantially improves the scalability and training efficiency of memory-based temporal graph learning while maintaining competitive predictive performance.

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