Video-DeepResearch：下一代多模态深度研究智能体
Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent
Video-DR 让 AI 边看视频边上网核实信息，35B 参数干翻 Claude-4.5-Sonnet，还开源了，值得试试。
论文提出 Video-DeepResearch，将多模态智能体从静态图像扩展到连续视频流，要求密集时空定位与开放网页探索结合。
在 Video-DR-Bench 基准上，Video-DeepResearch-35B-A3B 平均准确率达 64.0%，超过 Claude-4.
5-Sonnet（59.0%）5.
0 个百分点，也优于 GPT-5（52.5%）和 Gemini 2.
5 Pro（57.5%）。
框架采用解耦的感知-探索流程，通过分阶段工具解锁强制跨帧视觉定位后再进行网页检索。训练结合监督微调与 GRPO，30B-A3B 变体达到 59.
3%，与 Claude-4.5-Sonnet 相当。
代码已开源。
Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent
We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video streams, a setting that demands dense spatiotemporal grounding coupled with open-web exploration. Preliminary evaluations reveal two critical bottlenecks in current models: (1) modality bias, where agents bypass visual tools in favor of textual search, and (2) parametric knowledge leakage, where models rely on internal memory rather than genuine tool-augmented execution. To address these challenges, we propose Video-DR, featuring a decoupled perception-exploration pipeline with stage-wise tool unlocking that compels exhaustive cross-frame visual grounding prior to web retrieval. Our framework adopts a two-stage training recipe: supervised fine-tuning followed by Group Relative Policy Optimization (GRPO), enabling autonomous exploration that breaks the imitation-learning ceiling. Furthermore, we curate Video-DR-Bench, a human-AI collaborative benchmark comprising 200 complex, multi-hop VQA instances. Empirical results demonstrate that our Video-DeepResearch-35B-A3B establishes a new state-of-the-art of 64.0% average accuracy, surpassing proprietary Claude-4.5-Sonnet (59.0%) by 5.0 points and significantly outperforming GPT-5 (52.5%) and Gemini 2.5 Pro (57.5%). The 30B-A3B variant achieves 59.3%, competitive with Claude-4.5-Sonnet and demonstrating the effectiveness of our training paradigm even at compact scale. Code: https://github.com/Osilly/Vision-DeepResearch.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include Video-DeepResearch, 多模态, 智能体, GRPO.
Source attribution: curated from arXiv cs.AI (@Zhen Fang, Yu Zeng, Wenxuan Huang, Yiming Zhao, Shiting Huang, Tianfei Ren, Qi Lu, Qingnan Ren, Qisheng Su, Lionel Z. Wang, Qingyu Yin, Shuang Chen, Zehui Chen, Lin Chen, Zhenfei Yin, Yao Hu, Shaohui Lin, Wanli Ouyang, Shaosheng Cao, Feng Zhao).