A three-legged peacock still gets "two legs": a few in-context examples move attention off the category prior and onto the pixels
Across five vision-centric tasks, adding labeled demonstrations to the context sharply raises accuracy on counterfactual examples that conflict with training priors while leaving prior-aligned accuracy nearly unchanged, indicating the models already have the visual capability and were being overridden by learned regularities.