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This paper reveals that hallucination in large vision-language models is caused by a dynamic structural misalignment where certain attention heads act as risky mediators, decoupling from visual evidence to lock onto language priors. The authors propose Fox, a training-free causal intervention framework that diagnoses and physically severs these pathological shortcuts, achieving state-of-the-art performance in faithful decoding.
This paper introduces Positive-and-Negative Decoding (PND), a training-free inference framework that reduces object hallucination in Vision-Language Models by contrasting positive visual evidence with negative counterfactuals during decoding.