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This work proposes MAEM, a replay-free, capacity-controlled dynamic mixture-of-experts framework for class-incremental graph learning that mitigates recency bias, outperforms strong continual graph learning baselines, and keeps model capacity bounded under strict replay-free learning.
Graph few-shot class-incremental learning (GFSCIL) requires a model to continually recognize emerging classes from only a few labeled nodes while preserving previously acquired knowledge. Beyond the catastrophic forgetting inherited from conventional graph continual learning, GFSCIL presents two distinctive challenges:...
This work introduces the Counterfactual Quotient Model, which treats action-conditioned futures as equivalent when they differ only by a component shared across actions, and establishes the decision sufficiency, identifiability, common-mode invariance, approximation behavior, and regret properties of the resulting repr...
An innovative approach to robustness in continual learning systems is introduced, the Dynamic Siamese Expansion Framework (DSEF) that employs a Siamese backbone architecture, comprising static and dynamic components, to facilitate the learning of both global and local representations over time.
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