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Specifically, in the supervised fine-tuning stage, we design a unified masking mechanism that encourages exploration while preve","title":"VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos","url":"https://arxiv.org/abs/2602.07801","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.07801v4 Announce Type: replace-cross \nAbstract: In long-video understanding, conventional uniform frame sampling often fails to capture key visual evidence, leading to degraded performance and increased hallucinations. To address this, recent agentic thinking-with-videos paradigms have emerged, adopting a localize-clip-answer pipeline in which the model actively identifies relevant video segments, performs dense sampling within those clips, and then produces answers. However, existing methods remain inefficient, suffer from weak localization, and adhere to rigid workflows. 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