AI RESEARCH
Abstraction as a Memory-Efficient Inductive Bias for Continual Learning
arXiv CS.LG
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ArXi:2603.17198v1 Announce Type: new The real world is non-stationary and infinitely complex, requiring intelligent agents to from scratch. While online continual learning offers a framework for this setting, learning new information often interferes with previously acquired knowledge, causes forgetting and degraded generalization. To address this, we propose Abstraction-Augmented