AIBearisharXiv – CS AI · Jun 97/10
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When No Answer Is Correct: Diagnosing Absent Answer Detection for MLLMs in Video Understanding
Researchers have identified a critical reliability flaw in multimodal large language models (MLLMs) used for video understanding: when the correct answer is absent from available options, these models fail to recognize it and instead select plausible incorrect alternatives. Testing across multiple models and benchmarks reveals this limitation is especially severe in temporal reasoning tasks and worsens with increased video frame sampling, with chain-of-thought prompting offering only partial mitigation.