Beyond Static Selectivity: Task-Native Access and Adaptation Stability in Promptable Segmentation Unlearning

Current evaluation of selective concept unlearning for segmentation foundation models assesses whether a designated target is suppressed while useful non-target segmentation is preserved at a fixed post-unlearning checkpoint under a canonical inference configuration. Such static selectivity provides a meaningful qualification, but leaves open how broadly it characterizes the same target capability beyond its qualifying route and model state. We study this scope along two controlled axes: task-native access with the model fixed, and target-clean model evolution with canonical access fixed. Across a 336-run State Bank comprising SCU and six standardized forgetting/retention controls over two SAM backbones, a pre-specified criterion identifies 28 qualified model-target states. With model weights fixed, mean route-normalized residual target capability rises from 0.548 under canonical box access to 0.944 under model-ranked multimask output. In SAM2, changing only the output-resolution rule while holding the image, box prompt, and model weights fixed increases the deployed-versus-controlled IoU gap from 0.0013 in the original model to 0.203 across qualified post-unlearning states. With canonical access fixed, five target-clean adaptation updates restore 77.2% of the behavioral gap on average, while monitored utility remains within the pre-specified tolerance in 83 of 84 trajectories at step 50. In one matched fine-grained case, behavioral return from the post-unlearning state exceeds that of an ordinary-reacquisition control, while its provenance remains unresolved. These findings show that static selectivity is a condition-specific qualification rather than an access- or state-invariant characterization of post-unlearning target capability.