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Vision-Language Models (VLMs) often produce self-reflective statements like “let me check the figure again” during reasoning. Do such state- ments trigger genuine visual re-examination, or are they merely learned textual patterns? We in- vestigate this via VISUALSWAP, an image-swap probing framework: after a model reasons over an image, we replace it with a visually similar but semantically different one and test whether the model notices. We introduce VS-BENCH, 800 image pairs curated from MathVista, Math- Verse, MathVision, and MMMU-Pro. Exper- iments on Qwen3-VL, Kimi-VL, and ERNIE- VL reveal a striking failure: models overwhelm- ingly miss the swap, with accuracy dropping by up to 60%. Counterintuitively, thinking mod- els are nearly 3x more vulnerable than their in- structed counterparts, and scaling offers no mit- igation. Multi-turn user instructions restore vi- sual grounding, but self-generated reflective state- ments during continuous generation do not. At- tention analysis explains why: user instructions substantially elevate attention to visual tokens, whereas self-reflection does not. Current VLMs tend to say rather than actually see when claiming to perform visual re-examination. Our code and dataset are available at the project page: https://visualswap.github.io/