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This inflates visual tokens ofter to 97-99% of total tokens, resulting in high compute and latency, even when low-resolution images would suffice. We introduce \\emph{CARES}-a \\textbf{C}ontext-\\textbf{A}ware \\textbf{R}esolution \\textbf{S}elector, a lightweight preprocessing module that, given an image-query pair, predicts the \\emph{minimal} sufficient input resolution. CARES uses a compact VLM (350M) to extract features and predict when a target pretrained VLM's response converges to its peak ability to answer correctly. Though trained as a discrete classifier over a set of optional resolutions, CARES interpolates continuous resolutions at inference for fine-grained control. 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