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The paper challenges the assumption that cosine alignment between supervised latents and visual targets improves accuracy in vision-language models, finding a strong negative correlation. It introduces PRISM diagnostics revealing that answers are decoded downstream from latents, not within them, and that the auxiliary loss reshapes the language model via shared parameters.