AdaVDR: Adaptive Tool Use and Reflection for Video Deep Research
Video deep research aims to answer questions by jointly understanding video content and retrieving external knowledge from the open Web. However, these tasks often require diverse and multi-step tool-use trajectories, which may lead to unnecessary video tool calls, incurring additional costs and failure risk. In this paper, we propose AdaVDR, an adaptive video deep research agent with adaptive tool use and reflection, which selects tools based on task and model capabilities while backtracking when needed. To this end, we develop a data construction pipeline that builds adaptive tool-use trajectories according to task types and model capabilities. Specifically, we construct QA pairs that r...
| Authors | Xintong Zhang, Xiaomeng Fan, Shilin Yan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 August 2026 |
| Last updated at source | 14 October 2026 |
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