AI RESEARCH
Pruned Adaptation Modules: A Simple yet Strong Baseline for Continual Foundation Models
arXiv CS.LG
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ArXi:2603.21170v1 Announce Type: new The continual learning literature has rapidly shifted from traditional class incremental learning (CIL) techniques to foundation model (FM)-based CIL methods without a clear understanding of how these newer approaches compare to strong, lightweight convolutional baselines. This abrupt transition has created a substantial methodological gap, making it difficult to assess whether recent FM-based CIL progress reflects genuine advances or merely the absence of rigorous baselines. To address this gap, we.