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Although performance of language-based models is improved by scaling, whether the gap to a structure-aware architecture can eventually be eliminated remains untested.
Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evidence, so a fully black-box framework that models hallucination as a structured uncertainty pattern is proposed.
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