Researchers have introduced MASA, a novel approach to wild test-time adaptation that uses a frozen multimodal large language model to provide structured semantic descriptions for object families and nuisance factors. This helps to break the self-referential loop in model adaptation and improves accuracy in limited-batch, mixed-domain, and imbalanced-label-shift settings.