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MLLMs can outperform humans in target detection and acquisition but fail to mimic the nuanced temporal dynamics of human visual search.
A compact model achieves state-of-the-art spatial reasoning in 3D CT analysis, proving that less can be more in medical imaging AI.
VLMs reveal that people can be looking at different places but still "seeing" the same thing, adding a crucial layer of semantic understanding to traditional eye-tracking analysis.
