Multi-Frequency Associative Memory for Continual Graph Learning through Nested Optimization

Abstract

Graph Neural Networks struggle to learn new tasks without forgetting old ones a problem known as catastrophic forgetting. In graph domains, this is compounded by structural shift, where newly added edges corrupt the learned representations of historical nodes even when model weights remain unchanged. We present CAM-Titans, a continual graph learning framework built around a two-buffer associative memory to address both parametric and structural forgetting. Our architecture operates across three timescales of adaptation: a slow base memory updated via ordinary gradient descent, an intermediate task buffer re-encoded after every task using the delta-rule, and a transient in-context state for rapid within-pass adaptation. To ensure historical class prototypes remain retrievable as the network backbone evolves, memory retrieval is anchored in a dynamically maintained prototype coordinate system. Furthermore, a cosine classifier mitigates magnitude imbalance, preventing older classes from dominating predictions. Empirical evaluations across diverse continual learning benchmarks demonstrate that CAM-Titans effectively mitigates catastrophic forgetting, achieving superior stability and accuracy in both Task-Incremental and Class-Incremental settings.