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This work proposes Vid-PRE (Video Prompt Reasoner and Enhancer), a model-agnostic prompt rewriter that offloads the cognitive burden of reasoning to a dedicated VLM, and introduces VWG-Bench, a comprehensive benchmark spanning 9 reasoning dimensions and 38 fine-grained tasks.
OmniHallu is introduced, a unified hallucination detection framework spanning both comprehension and generation tasks across image, video, and audio modalities, and a preference-optimized trainable verifier that approximates the multi-agent decision boundary, reducing expert calls by 66% with minimal performance loss.
Video-HOCA, a diagnostic benchmark for physical anomaly reasoning in videos, is introduced and it is found that recognition outpaces explanation: Task I scores cluster at 75-88, while Task II macro-F1 stays mostly below 50.
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