VAULT: Trustworthy Finite Memory for Contaminated Continual Learning

In contaminated continual learning, replay can turn transient labeling errors into persistent supervision. A finite memory must therefore determine which observations are reliable enough to influence future tasks. We introduce VAULT, a framework that separates participation in current-task learning from admission to long-term memory. VAULT constructs hierarchical evidence in a bootstrapped semantic space, combining granular support and stacked zentropy to assess candidate reliability. Classifier and prototype agreement then certify candidates for storage, while conflict-aware replay preserves the knowledge supported by the resulting memory. Experiments on CIFAR-100 show that VAULT achieves higher final accuracy than the compared replay baselines at the smallest evaluated memory budget across task partitions, together with compact, high-purity storage. Ablations and sample-level analyses connect this advantage to the complementary roles of evidence-based admission and replay preservation. Together, the results show how evidence-based admission builds reliable historical supervision within finite replay memory.