C3L Prevents Explanation Drift in Continual Learning
October 23, 2026
Concept-Consistent Continual Learning (C3L) prevents models from shifting attention from diagnostic features to spurious correlations during task transitions. It anchors regularization to offline-discovered semantic concepts rather than relying on the model's own potentially drifting saliency maps.
HOW THIS AFFECTS YOU
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builderYou can use this to build models that maintain reliable interpretability as they learn new tasks over time.
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researcherThis addresses the structural flaw of self-referential drift in explanation-aware continual learning.