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How reliably can a model turn the same visual evidence into the action required by the current context? To answer this question, we introduce \\textsc{ROSE} (\\textbf{R}eference-conditioned \\textbf{O}ddity and \\textbf{S}ymbolic \\textbf{E}xecution), a controlled benchmark that holds the visual scene fixed while varying region constraints and required symbolic outputs. Through coupled counting and coordinate-action tasks, \\textsc{ROSE} tests whether models can infer an implicit majority reference and act on the resulting fine-grained visual evidence under changing contexts. Across nine recent MLLMs, performance drops by as much as 44.5 percentage points from counting-oriented tasks to region-conditioned action, despite 98.8\\% human performance. 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Across nine recent MLLMs, performance drops by as much as 44.5 percentage points from counting-oriented tasks to region-conditioned action, despite 98.8\\% human performance. 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