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VisER: Visual Evidence and Reliance for Object Hallucination Detection in LVLMs

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Object hallucination remains a persistent reliability issue in large vision-language models, where generated object mentions may sound plausible but lack visual grounding. Recent training-free detectors use internal signals such as token likelihood, attention, visual confidence, or image-text similarity to identify hallucinated objects. These signals are useful, but they are often source-confounded. They measure how strongly an object is supported inside the model without distinguishing whether that support comes from object-specific visual evidence or the generated text prefix. In difficult c

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