@inproceedings{gao-etal-2026-videosearcher,
title = "{V}ideo{S}earcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning",
author = "Gao, Zhenkun and
Bao, Yicheng and
Peng, Jinlong and
Li, Xueheng and
Huang, Suyuan and
Liu, Bangwei and
Li, Kunquan and
Gan, Zhenye and
Hu, Tao and
Xie, Chengjun and
He, Xuanhua and
Zhang, Zhizhong and
Tan, Xin and
Wang, Chengjie and
Xie, Yuan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1373/",
pages = "31328--31352",
ISBN = "979-8-89176-459-0",
abstract = "Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimodal search agents primarily target static images, and the current VDR benchmark relies on text-centric retrieval that discards crucial visual information. To address these limitations, we propose VideoSearcher, a closed-loop agentic framework that empowers Vision-Language Models with multi-tool reasoning for VDR. VideoSearcher unifies temporal localization, spatial focusing, and multimodal search within a single reasoning trajectory, enabling agents to progressively ground visual anchors, retrieve relevant evidence, and synthesize answers. To optimize knowledge-intensive reasoning trajectories, we propose Bi-branch Sequence Policy Optimization (BiSPO), a reinforcement learning algorithm that decouples tool-invocation optimization from answer-accuracy optimization. This design provides stable learning signals for both evidence-grounded reasoning and purposeful tool use. Furthermore, we construct VideoSearch-QA, the first benchmark designed to evaluate open-world video information grounding and multimodal search-based reasoning. Extensive experiments demonstrate that VideoSearcher significantly outperforms prior open-source agentic baselines across various search-oriented and multimodal understanding benchmarks. Our code is publicly available at https://github.com/Stephen-gzk/VideoSearcher."
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<abstract>Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimodal search agents primarily target static images, and the current VDR benchmark relies on text-centric retrieval that discards crucial visual information. To address these limitations, we propose VideoSearcher, a closed-loop agentic framework that empowers Vision-Language Models with multi-tool reasoning for VDR. VideoSearcher unifies temporal localization, spatial focusing, and multimodal search within a single reasoning trajectory, enabling agents to progressively ground visual anchors, retrieve relevant evidence, and synthesize answers. To optimize knowledge-intensive reasoning trajectories, we propose Bi-branch Sequence Policy Optimization (BiSPO), a reinforcement learning algorithm that decouples tool-invocation optimization from answer-accuracy optimization. This design provides stable learning signals for both evidence-grounded reasoning and purposeful tool use. Furthermore, we construct VideoSearch-QA, the first benchmark designed to evaluate open-world video information grounding and multimodal search-based reasoning. Extensive experiments demonstrate that VideoSearcher significantly outperforms prior open-source agentic baselines across various search-oriented and multimodal understanding benchmarks. Our code is publicly available at https://github.com/Stephen-gzk/VideoSearcher.</abstract>
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%0 Conference Proceedings
%T VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning
%A Gao, Zhenkun
%A Bao, Yicheng
%A Peng, Jinlong
%A Li, Xueheng
%A Huang, Suyuan
%A Liu, Bangwei
%A Li, Kunquan
%A Gan, Zhenye
%A Hu, Tao
%A Xie, Chengjun
%A He, Xuanhua
%A Zhang, Zhizhong
%A Tan, Xin
%A Wang, Chengjie
%A Xie, Yuan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F gao-etal-2026-videosearcher
%X Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimodal search agents primarily target static images, and the current VDR benchmark relies on text-centric retrieval that discards crucial visual information. To address these limitations, we propose VideoSearcher, a closed-loop agentic framework that empowers Vision-Language Models with multi-tool reasoning for VDR. VideoSearcher unifies temporal localization, spatial focusing, and multimodal search within a single reasoning trajectory, enabling agents to progressively ground visual anchors, retrieve relevant evidence, and synthesize answers. To optimize knowledge-intensive reasoning trajectories, we propose Bi-branch Sequence Policy Optimization (BiSPO), a reinforcement learning algorithm that decouples tool-invocation optimization from answer-accuracy optimization. This design provides stable learning signals for both evidence-grounded reasoning and purposeful tool use. Furthermore, we construct VideoSearch-QA, the first benchmark designed to evaluate open-world video information grounding and multimodal search-based reasoning. Extensive experiments demonstrate that VideoSearcher significantly outperforms prior open-source agentic baselines across various search-oriented and multimodal understanding benchmarks. Our code is publicly available at https://github.com/Stephen-gzk/VideoSearcher.
%U https://aclanthology.org/2026.emnlp-main.1373/
%P 31328-31352
Markdown (Informal)
[VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning](https://aclanthology.org/2026.emnlp-main.1373/) (Gao et al., EMNLP 2026)
ACL
- Zhenkun Gao, Yicheng Bao, Jinlong Peng, Xueheng Li, Suyuan Huang, Bangwei Liu, Kunquan Li, Zhenye Gan, Tao Hu, Chengjun Xie, Xuanhua He, Zhizhong Zhang, Xin Tan, Chengjie Wang, and Yuan Xie. 2026. VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31328–31352, Budapest, Hungary. Association for Computational Linguistics.