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On the Faithfulness of Visual Thinking: Measurement and Enhancement

arXiv — cs.CV•Tuesday, October 28, 2025 at 4:00:00 AM
NeutralArtificial Intelligence
A recent study highlights the challenges faced by large vision-language models (LVLMs) in generating accurate visual information during multimodal reasoning processes. While these models can produce correct answers, the visual data they rely on often lacks faithfulness, raising concerns about the reliability of their reasoning. This matters because it points to the need for improvements in how these models are trained, particularly in reinforcement fine-tuning, to ensure that they not only perform well but also provide trustworthy visual insights.
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