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cs.CV2026
MLLMs Get It Right, Then Get It Wrong: Tracing and Correcting Late-Layer Textual Bias
Xingming Li, Ao Cheng, Qiyao Sun +4
When vision contradicts text, multimodal large language models (MLLMs) consistently favor text, even when images provide clear evidence otherwise. This bias poses risks for applica…
cs.CV2026
StemBind: When MLLMs Get Lost Between Rules and Instances in Abstract Visual Reasoning
Xixiang He, Baiqi Wu, Xingming Li +4
Multimodal large language models (MLLMs) often know the rule but pick the wrong answer: on abstract visual reasoning (AVR) tasks, a model can describe what it sees and name the und…