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This paper investigates how vision-language models resolve conflicts between visual evidence and world knowledge, revealing that visual grounding is the default while prior knowledge depends on a small set of late-layer attention heads. The authors perform causal analysis across three VLM families, demonstrating an asymmetric structure where ablating these heads shifts predictions from knowledge-grounded to visually grounded answers.