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This paper studies the ability of multimodal large language models (MLLMs) to detect when the correct answer is absent in video understanding tasks, finding that models systematically fail by selecting plausible distractors instead of recognizing no valid option exists. The failure worsens in temporal reasoning and dense frame sampling, and chain-of-thought prompting only partially mitigates the issue.