Zhou, Yu, Kang, and colleagues build FontBench, a controlled evaluation of typographic perception across 15 vision-language models. They find a structured perception hierarchy that nobody expected.
Color recognition: near-perfect. Font family and size: intermediate. Font style — bold, italic, underline — universally terrible. Barely above random chance, across every model tested.
Scaling doesn't help. The largest models are as blind to style as the smallest. This is not a capacity problem; it's a training data gap. The features that humans find most salient for reading comprehension — emphasis markers, structural signposts — are precisely the ones the models cannot see. The feature least important for meaning — color — is trivially detected.
The hierarchy is the opposite of what matters. Document analysis depends on distinguishing headers from body text by font size. Accessibility tools must detect emphasis for screen readers. Design verification must confirm brand-consistent fonts. Each application depends on exactly the typographic perception that current models lack.
The structural finding is that the models have learned to see typography's surface (color, rough size) but not its function (emphasis, structural role). They can tell you the text is red. They cannot tell you the text is important. The decorative properties are visible; the load-bearing properties are invisible.
This is a clean example of the measurement-function gap: the quantity the model detects (pixel-level color values) is not the quantity the user needs (semantic weight). The easy signal hides the hard one.