# AdaVDR: Adaptive Tool Use and Reflection for Video Deep Research

arXiv: https://arxiv.org/abs/2608.25559 · HTML: https://mlsanity.com/paper/2608.25559/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 35 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 30 of 33)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes](https://mlsanity.com/paper/2308.11417/) (2023)
- [Qwen3-Omni Technical Report](https://mlsanity.com/paper/2509.17765/) (2025)
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data](https://mlsanity.com/paper/2111.08897/) (2021)
- [WebSailor: Navigating Super-human Reasoning for Web Agent](https://mlsanity.com/paper/2507.02592/) (2025)
- [MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe](https://mlsanity.com/paper/2509.18154/) (2025)
- [Qwen3.5-Omni Technical Report](https://mlsanity.com/paper/2604.15804/) (2026)
- [DeepEyesV2: Toward Agentic Multimodal Model](https://mlsanity.com/paper/2511.05271/) (2025)
- [MMSearch-R1: Incentivizing LMMs to Search](https://mlsanity.com/paper/2506.20670/) (2025)
- [Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding](https://mlsanity.com/paper/2505.18079/) (2025)
- [EgoLife: Towards Egocentric Life Assistant](https://mlsanity.com/paper/2503.03803/) (2025)
- [Sekai: A Video Dataset towards World Exploration](https://mlsanity.com/paper/2506.15675/) (2025)
- [LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents](https://mlsanity.com/paper/2503.10200/) (2025)
- [VCA: Video Curious Agent for Long Video Understanding](https://mlsanity.com/paper/2412.10471/) (2024)
- [REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents](https://mlsanity.com/paper/2602.14234/) (2026)
- [CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos](https://mlsanity.com/paper/2411.17820/) (2024)
- [VideoAgent2: Enhancing the LLM-Based Agent System for Long-Form Video Understanding by Uncertainty-Aware CoT](https://mlsanity.com/paper/2504.04471/) (2025)
- [Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models](https://mlsanity.com/paper/2601.22060/) (2026)
- [VideoExplorer: Think With Videos For Agentic Long-Video Understanding](https://mlsanity.com/paper/2506.10821/) (2025)
- [OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents](https://mlsanity.com/paper/2605.05185/) (2026)
- [Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning](https://mlsanity.com/paper/2601.06943/) (2026)
- [Video-Browser: Towards Agentic Open-web Video Browsing](https://mlsanity.com/paper/2512.23044/) (2025)
- [HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents](https://mlsanity.com/paper/2605.07177/) (2026)

## Abstract (arXiv)

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 require both video and external information, and organize the information acquisition processes into task-specific tool-use trajectories. We then apply model-conditioned tool necessity filtering to remove tools that can be bypassed using video understanding and internal knowledge of the model. Based on this pipeline, we construct a training dataset with adaptive trajectories and perform SFT on them to initialize these capabilities of AdaVDR. To further improve adaptive capabilities, we propose NASA-GRPO, a necessity-aware segment-guided credit assignment method, which evaluates segment-guided evidence contribution and tool necessity to construct normalized tool-level rewards. We also construct VDR-EE, a benchmark built using the proposed pipeline. Experiments show that AdaVDR outperforms the base model by 15.20 and 29.00 percentage points on VDR-EE and the existing VideoDR, respectively, demonstrating its superiority.