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Prior work mainly studies per-sample or training-time acquisition while test-time, cohort-level acquisition is less explored. We propose Cohort-based Active Modality Acquisition (CAMA), a novel test-time cohort-level modality acquisition setting, and introduce imputation-based acquisition strategies that estimate the expected utility of acquiring a missing modality, along with upper-bound heuristics for benchmarking. Experiments on datasets with up to 15 modalities demonstrate that our proposed imputation-based strategies can more effectively guide the acquisition of an additional modality for selected samples compared with methods relying solely on pre-acquisition information, entropy-based guidance, or random selection. 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