Researchers have introduced AdaVDR, an adaptive agent designed to enhance video deep research by intelligently using and reflecting on external tools. This system aims to improve accuracy and reduce latency by selecting appropriate tools based on diverse questions and video content, and only backtracking when intermediate results are unreliable. AdaVDR utilizes a novel data construction pipeline that identifies retrieval-relevant events and entities in videos, grounds them with external knowledge, and tailors tool-use trajectories to the target model's capabilities. The method is trained using supervised fine-tuning and reinforcement learning, demonstrating superior performance on the VDR-EE benchmark and improving upon existing models in VideoDR. AI
IMPACT Improves AI's ability to understand video content and external knowledge for complex research tasks.
RANK_REASON Academic paper introducing a new method and benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
- AdaVDR
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- Connected Papers
- DagsHub
- Gotit.pub
- Hugging Face
- Litmaps
- ScienceCast
- scite Smart Citations
- VDR-EE
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