Multilingual vision-language models (VLMs) consistently underperform on non-English visual queries, yet the internal mechanism behind this disparity remains unknown. As a focused case study on LLaVA-1.5-7B, we apply logit-lens analysis and causal activation patching to show that non-English visual queries are routed through an English-biased representational bottleneck in layers 5–17, extending the English-pivot phenomenon of Wendler et al. (2024) to the multimodal setting. Peak causal influence occurs at layer 8 ( ̅AIE = 0.49, averaged across languages), with all measurable pivot signal running through text-token positions. Without meaningful visual content (blank-image condition), language-specific representations do not emerge at any layer, showing that the pivot is image-content-dependent rather than triggered by any visual input. Building on these findings, we derive training-free language-steering vectors at the mechanistically identified pivot layers, improving Russian VQA by +6.5 pp and Portuguese by +4.0 pp on MMMB without any fine-tuning — the latter surpassing the English baseline. Within this case study, our results are consistent with the English pivot being a structural property of the LLM backbone that multimodal pre-training does not mitigate; extending this mechanistic methodology to other VLMs and language families remains an important direction for future work.