Domain Generalization via Text-Anchored Information Bottleneck

Visual recognition models often fail when deployed in newenvironments. Domain Generalization (DG) addresses this by learningrepresentations that remain invariant to environment-specific variations.Recent approaches increasingly rely on large vision-language models, as-suming that preserving their expressive visual representations improvesrobustness. However, we show that such visual expressiveness can insteadpropagate spurious cues that tie representations to the training environ-ments, hindering invariant learning. We therefore discard visual guidanceand instead treat the language embedding space as the primary sourceof domain invariance, naturally acting as an information bottleneck thatpreserves core semantics while suppressing domain-specific variations.Extensive experiments across diverse backbones exhibit state-of-the-artperformance and further analyze what makes guidance effective for ro-bust generalization. These findings shift the focus of DG from improvingrepresentations to designing supervision that enforces invariance.