visual-comparison

VDiff-Bench is a challenging benchmark for evaluating multimodal language models on fine-grained image difference identification, revealing significant weaknesses in detecting subtle low-level visual changes.

This paper introduces a stateful visual encoder for vision-language models that conditions visual representations on prior features, enabling better visual comparison in multi-image and agentic settings. The method shows consistent improvements across tasks such as cross-image spatial aggregation and longitudinal radiology.