The Flow has not summarised this story yet — read it at arXiv Machine Learning.
HERO is a heterogeneous continual graph learning framework that preserves historical knowledge at both structural and semantic levels while adapting to new tasks. It uses DiSCo to select representative target nodes and reconstruct compact heterogeneous contexts, and applies multi-level knowledge distillation to align task-conditioned predictions with semantic responses. Experiments on four datasets with three HGNN backbones show HERO achieves best or tied-best average performance while maintaining competitive forgetting.
The paper introduces Router Drift Control (RDC), a method for mitigating off‑policy degradation in Mixture‑of‑Experts reinforcement learning. RDC uses the disagreement between rollout and training routers to detect stale routing decisions and selectively rejects high‑drift responses, thereby avoiding the gradient bias introduced by Rollout Routing Replay (R3). Experiments show that RDC improves validation accuracy, especially in asynchronous training settings, outperforming both R3 and Truncated Importance Sampling (TIS).
arXiv:2609.25781v1 Announce Type: new Abstract: Graph Incremental Learning has garnered increasing attention as dynamic graph data continues to emerge across diverse fields. Conventional approaches p...
arXiv:2608. 01252v1 Announce Type: new Abstract: Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks.
HCPN-GCN is a graph-aware extension of Hierarchical Prototype Networks that replaces linear feature extractors with Graph Convolutional Networks and introduces cone-based prototypes with a diversity regularization objective. This design yields richer graph-aware representations, reduces prototype proliferation, and maintains near-zero forgetting. Experiments on six continual graph learning benchmarks show consistent accuracy improvements over the original HPN and other baselines while using roughly 30× fewer atomic prototypes.
arXiv:2512. 18295v2 Announce Type: replace-cross Abstract: Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge.