@inproceedings{fang-etal-2026-video,
title = "Video-{D}eep{R}esearch: Towards the Next-Generation Multimodal Deepresearch Agent",
author = "Fang, Zhen and
Zeng, Yu and
Huang, Wenxuan and
Zhao, Yiming and
Huang, Shiting and
Ren, Tianfei and
Lu, Qi and
Ren, Qingnan and
Su, Qisheng and
Wang, Lionel Z. and
Yin, Qingyu and
Chen, Shuang and
Chen, Zehui and
Chen, Lin and
Yin, Zhenfei and
Hu, Yao and
Lin, Shaohui and
Ouyang, Wanli and
Cao, Shaosheng and
Zhao, Feng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1881/",
pages = "40113--40131",
ISBN = "979-8-89176-462-0",
abstract = "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-DeepResearch, 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 VideoDR-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."
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<abstract>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-DeepResearch, 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 VideoDR-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.</abstract>
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%0 Conference Proceedings
%T Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent
%A Fang, Zhen
%A Zeng, Yu
%A Huang, Wenxuan
%A Zhao, Yiming
%A Huang, Shiting
%A Ren, Tianfei
%A Lu, Qi
%A Ren, Qingnan
%A Su, Qisheng
%A Wang, Lionel Z.
%A Yin, Qingyu
%A Chen, Shuang
%A Chen, Zehui
%A Chen, Lin
%A Yin, Zhenfei
%A Hu, Yao
%A Lin, Shaohui
%A Ouyang, Wanli
%A Cao, Shaosheng
%A Zhao, Feng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F fang-etal-2026-video
%X 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-DeepResearch, 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 VideoDR-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.
%U https://aclanthology.org/2026.findings-emnlp.1881/
%P 40113-40131
Markdown (Informal)
[Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent](https://aclanthology.org/2026.findings-emnlp.1881/) (Fang et al., Findings 2026)
ACL
- 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, and Feng Zhao. 2026. Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 40113–40131, Budapest, Hungary. Association for Computational Linguistics.