When Better Routing Hides a Worse Prompt Update: Classifier-Conditioned Evaluation in Domain-Incremental Learning

Prompt-based continual-learning models store small sets of prompt parameters learned for different domains. At test time, a router selects a prompt for each input and, in some systems, a classifier as well. A router change can therefore be deployed in two ways: by changing only the prompt assignment while retaining the original classifier assignment, or by changing both together. Accuracy measured with the routed prompt–classifier pair correctly evaluates the joint action but does not reveal whether changing the prompt alone helps, and this distinction can reverse a deployment decision. On DomainNet with S-iPrompts, a Gaussian router improves joint accuracy by $1.28$ percentage points (pp), whereas applying only its prompt assignments under the original classifier assignments lowers accuracy by $0.23\pm 0.03$ pp. Fixed-bank analyses show the same classifier dependence: in S-iPrompts and a DualPrompt bank from an independent code base with added per-domain heads, the same domain-prompt correction changes sign depending on which classifier is retained, whereas in CP-Prompt it helps under both. Changing classifier exposure through matched refits further changes this dependence in all three per-domain-head configurations on DomainNet, without improving routed classification accuracy over matched baselines. We therefore propose intervention-matched evaluation: a prompt-only action is evaluated with the classifier that will use it, a joint action by joint accuracy, and a training change against a baseline fitted under matched conditions, while reporting paired repairs and harms.