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This paper investigates seemingly contradictory findings on whether large vision-language models (LVLMs) can coordinate efficient referring expressions. The authors show that models can achieve efficiency when explicitly prompted, but fail to infer the need for efficiency from implicit prompts, revealing key differences between human and AI communication.