Pre-trained vision–language models such as CLIP provide strong priors for class-incremental learning (CIL), yet they degrade sharply under long-tailed class streams. We show that this degradation is not merely a byproduct of forgetting: in frozen-CLIP long-tailed CIL, newly introduced tail classes are disproportionately absorbed into previously learned old classes, a boundary-level failure we call new-tail collapse. We diagnose this failure with an old-new margin and analyze it through a boundary-risk view, where CLIP semantic overlap, sparse tail supervision, and old-class expansion jointly increase old-dominant violations. Motivated by this view, we propose Smooth Boundary-Risk Loss (SBRL), which estimates effective old rivals from CLIP’s vision-language geometry and suppresses their smooth old-side pressure against new-tail samples. MC-SMERG further consolidates task-wise adapters into a single efficient model. Across ImageNetSubset-LT, CIFAR100-LT, and Food101-LT, our method consistently outperforms strong CLIP-based CIL baselines and substantially reduces boundary violations in high-similarity, low-shot regimes.