AI RESEARCH
Beyond Perception Errors: Semantic Fixation in Large Vision-Language Models
arXiv CS.LG
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ArXi:2604.12119v1 Announce Type: cross Large vision-language models (VLMs) often rely on familiar semantic priors, but existing evaluations do not cleanly separate perception failures from rule-mapping failures. We study this behavior as semantic fixation: preserving a default interpretation even when the prompt specifies an alternative, equally valid mapping. To isolate this effect, we