AnchorRM: Parallel Intra-Batch State Reconstruction with Anchor-Based Residual Memory for Temporal Graph Neural Networks

arXiv:2610.27558v1 Announce Type: cross Abstract: 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

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