
Seeing Isn't Believing: Uncovering Blind Spots in Evaluator Vision-Language Models
Researchers systematically tested whether Vision-Language Models used to evaluate other AI systems can reliably catch common errors like object hallucinations and spatial reasoning failures. A benchmark of 4,000+ perturbed instances across 40 perturbation types reveals significant blind spots in how VLMs assess image-to-text and text-to-image outputs.62




























