When Looking Is Not Enough: Visual Attention Structure Reveals Hallucination in MLLMs
Multimodal large language models (MLLMs) have become a key interface for visual reasoning and grounded question answering, yet they remain vulnerable to visual hallucinations, where generated responses contradict image content or mention nonexistent objects. A central challenge is that hallucination is not always caused by a simple lack of visual attention: the model may still assign substantial attention mass to image tokens while internally drifting toward an incorrect answer. In this paper, we show that the high-frequency structure of visual attention, measured by layer-wise Laplacian energy, reveals both the layer where hallucinated preferences emerge and the layer where the ground-tr...
| Authors | Fanpu Cao, Xin Zou, Xuming Hu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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