dorsal/arxiv
View SchemaWatching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning
| Authors | Chengwen Liu, Xiaomin Yu, Zhuoyue Chang, Zhe Huang, Shuo Zhang, Heng Lian, Kunyi Wang, Rui Xu, Sen Hu, Jianheng Hou, Hao Peng, Chengwei Qin, Xiaobin Hu, Hong Peng, Ronghao Chen, Huacan Wang |
|---|---|
| Categories | |
| ArXiv ID | 2601.06943vv1 |
| URL | https://arxiv.org/abs/2601.06943 |
| License | http://creativecommons.org/licenses/by-nc-sa/4.0/ |
Abstract
In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore need to jointly perform cross-frame clue extraction, iterative retrieval, and multi-hop reasoning-based verification. To bridge this gap, we construct the first video deep research benchmark, VideoDR. VideoDR centers on video-conditioned open-domain video question answering, requiring cross-frame visual anchor extraction, interactive web retrieval, and multi-hop reasoning over joint video-web evidence; through rigorous human annotation and quality control, we obtain high-quality video deep research samples spanning six semantic domains. We evaluate multiple closed-source and open-source multimodal large language models under both the Workflow and Agentic paradigms, and the results show that Agentic is not consistently superior to Workflow: its gains depend on a model's ability to maintain the initial video anchors over long retrieval chains. Further analysis indicates that goal drift and long-horizon consistency are the core bottlenecks. In sum, VideoDR provides a systematic benchmark for studying video agents in open-web settings and reveals the key challenges for next-generation video deep research agents.
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"date_created": "2026-02-17T05:53:08.516000Z",
"date_modified": "2026-02-17T05:53:08.516000Z",
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"abstract": "In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore need to jointly perform cross-frame clue extraction, iterative retrieval, and multi-hop reasoning-based verification. To bridge this gap, we construct the first video deep research benchmark, VideoDR. VideoDR centers on video-conditioned open-domain video question answering, requiring cross-frame visual anchor extraction, interactive web retrieval, and multi-hop reasoning over joint video-web evidence; through rigorous human annotation and quality control, we obtain high-quality video deep research samples spanning six semantic domains. We evaluate multiple closed-source and open-source multimodal large language models under both the Workflow and Agentic paradigms, and the results show that Agentic is not consistently superior to Workflow: its gains depend on a model\u0027s ability to maintain the initial video anchors over long retrieval chains. Further analysis indicates that goal drift and long-horizon consistency are the core bottlenecks. In sum, VideoDR provides a systematic benchmark for studying video agents in open-web settings and reveals the key challenges for next-generation video deep research agents.",
"arxiv_id": "2601.06943",
"authors": [
"Chengwen Liu",
"Xiaomin Yu",
"Zhuoyue Chang",
"Zhe Huang",
"Shuo Zhang",
"Heng Lian",
"Kunyi Wang",
"Rui Xu",
"Sen Hu",
"Jianheng Hou",
"Hao Peng",
"Chengwei Qin",
"Xiaobin Hu",
"Hong Peng",
"Ronghao Chen",
"Huacan Wang"
],
"categories": [
"cs.CV",
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
"title": "Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning",
"url": "https://arxiv.org/abs/2601.06943",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "0da669c7-0cc2-45c7-beab-b5de6980d312",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
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