Measurement-Driven Isometric Shared-Prompt Transport for General Continual Learning

General continual learning (GCL) requires single-pass learning from non-stationary streams without explicit task boundaries. Prompt tuning enables parameter-efficient adaptation of pretrained models, but recurring classes and changing class mixtures demand both knowledge reuse and distribution-specific adjustment. Selecting or combining independently learned prompts does not explicitly separate shared adaptation structure from distribution-specific variation, potentially dispersing reusable knowledge across prompt states under limited supervision. Meanwhile, imbalanced class observations can bias classifiers toward recent data, further limiting the effective use of historical knowledge. We propose MIST (Measurement-conditioned Isometric Shared-prompt Transport), a replay-free framework that coordinates structured prompt adaptation and adaptive classification through continuous historical associations. MIST uses frozen-feature statistics to associate inputs with historical distributions and condition isometric transformations of shared latent prompts, preserving token geometry across conditions for a given shared latent representation. The same associations weight temporal classifier ensembles, while statistical novelty and confidence advantage regulate their fusion with an online plastic classifier to balance knowledge retention and adaptation. Experiments demonstrate absolute gains of up to 12.10%, 9.14%, and 24.25% in average anytime accuracy over the strongest evaluated baselines on CIFAR-100, ImageNet-R, and CUB-200, respectively.