https://researchpod-share.vercel.app/episode/def8ab9d-82b2-44a3-847d-77135741a278

\n","updatedAt":"2026-01-13T11:28:58.792Z","author":{"_id":"6960eca92f7ad9b043b5cbe0","avatarUrl":"/avatars/e68dcc7fd04f143d849d40414866e633.svg","fullname":"Noah","name":"noahml","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8073317408561707},"editors":["noahml"],"editorAvatarUrls":["/avatars/e68dcc7fd04f143d849d40414866e633.svg"],"reactions":[{"reaction":"👍","users":["cristiano28","Yu2020","Huacan-Wang","POTATO66","potatoto888"],"count":5}],"isReport":false}},{"id":"6966f414851dd5274801f740","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false},"createdAt":"2026-01-14T01:40:36.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Video-BrowseComp: Benchmarking Agentic Video Research on Open Web](https://huggingface.co/papers/2512.23044) (2025)\n* [CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models](https://huggingface.co/papers/2511.12263) (2025)\n* [JointAVBench: A Benchmark for Joint Audio-Visual Reasoning Evaluation](https://huggingface.co/papers/2512.12772) (2025)\n* [LongVideoAgent: Multi-Agent Reasoning with Long Videos](https://huggingface.co/papers/2512.20618) (2025)\n* [A Benchmark and Agentic Framework for Omni-Modal Reasoning and Tool Use in Long Videos](https://huggingface.co/papers/2512.16978) (2025)\n* [Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding](https://huggingface.co/papers/2512.05774) (2025)\n* [Skywork-R1V4: Toward Agentic Multimodal Intelligence through Interleaved Thinking with Images and DeepResearch](https://huggingface.co/papers/2512.02395) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"

This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

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The following papers were recommended by the Semantic Scholar API

\n

\n
• Video-BrowseComp: Benchmarking Agentic Video Research on Open Web (2025)
\n
• CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models (2025)
\n
• JointAVBench: A Benchmark for Joint Audio-Visual Reasoning Evaluation (2025)
\n
• LongVideoAgent: Multi-Agent Reasoning with Long Videos (2025)
\n
• A Benchmark and Agentic Framework for Omni-Modal Reasoning and Tool Use in Long Videos (2025)
\n
• Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding (2025)
\n
• Skywork-R1V4: Toward Agentic Multimodal Intelligence through Interleaved Thinking with Images and DeepResearch (2025)
\n

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Please give a thumbs up to this comment if you found it helpful!

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If you want recommendations for any Paper on Hugging Face checkout this Space

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You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: \n\n@librarian-bot\n\t recommend

\n","updatedAt":"2026-01-14T01:40:36.165Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6941177248954773},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}},{"id":"6968ed2106c6e11b7684999d","author":{"_id":"6966415575a7cc5f08189a9f","avatarUrl":"/avatars/2847456d0cc4d97cf35580da24f6b8f2.svg","fullname":"zero","name":"potatoto888","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false},"createdAt":"2026-01-15T13:35:29.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This paper is both interesting and practically significant, and it can indeed promote further expansion in the field of video understanding. I look forward to your team's next work.","html":"

This paper is both interesting and practically significant, and it can indeed promote further expansion in the field of video understanding. I look forward to your team's next work.

\n","updatedAt":"2026-01-15T13:35:29.129Z","author":{"_id":"6966415575a7cc5f08189a9f","avatarUrl":"/avatars/2847456d0cc4d97cf35580da24f6b8f2.svg","fullname":"zero","name":"potatoto888","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9682532548904419},"editors":["potatoto888"],"editorAvatarUrls":["/avatars/2847456d0cc4d97cf35580da24f6b8f2.svg"],"reactions":[],"isReport":false}},{"id":"696b8aa1e7a76925b936fa45","author":{"_id":"65243980050781c16f234f1f","avatarUrl":"/avatars/743a009681d5d554c27e04300db9f267.svg","fullname":"Avi","name":"avahal","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false},"createdAt":"2026-01-17T13:12:01.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"arXivlens breakdown of this paper 👉 https://arxivlens.com/PaperView/Details/watching-reasoning-and-searching-a-video-deep-research-benchmark-on-open-web-for-agentic-video-reasoning-9505-80c82ba2\n\n- Executive Summary\n- Detailed Breakdown\n- Practical Applications","html":"

arXivlens breakdown of this paper 👉 https://arxivlens.com/PaperView/Details/watching-reasoning-and-searching-a-video-deep-research-benchmark-on-open-web-for-agentic-video-reasoning-9505-80c82ba2

\n

\n
• Executive Summary
\n
• Detailed Breakdown
\n
• Practical Applications
\n

\n","updatedAt":"2026-01-17T13:12:01.790Z","author":{"_id":"65243980050781c16f234f1f","avatarUrl":"/avatars/743a009681d5d554c27e04300db9f267.svg","fullname":"Avi","name":"avahal","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.5872972011566162},"editors":["avahal"],"editorAvatarUrls":["/avatars/743a009681d5d554c27e04300db9f267.svg"],"reactions":[{"reaction":"👍","users":["HJH2CMD","potatoto888"],"count":2}],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2601.06943","authors":[{"_id":"6965babdfc8c4ecc02c7f8f5","user":{"_id":"6965e8d162405ba787fc50b2","avatarUrl":"/avatars/52858daa454e710712c8a29307e0fe30.svg","isPro":false,"fullname":"Chengwen Liu","user":"POTATO66","type":"user"},"name":"Chengwen Liu","status":"admin_assigned","statusLastChangedAt":"2026-01-13T15:46:54.096Z","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8f6","user":{"_id":"64084fa192033c150738e4f2","avatarUrl":"/avatars/dfff2216eb235c635e5abe6fda3084f0.svg","isPro":false,"fullname":"Yu_xm","user":"Yu2020","type":"user"},"name":"Xiaomin Yu","status":"admin_assigned","statusLastChangedAt":"2026-01-13T15:46:34.064Z","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8f7","name":"Zhuoyue Chang","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8f8","name":"Zhe Huang","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8f9","user":{"_id":"65562edfb7bad186e877c724","avatarUrl":"/avatars/bb91f42b102e113208bbe3238916a015.svg","isPro":false,"fullname":"zhangshuo","user":"mcflurryshuoz","type":"user"},"name":"Shuo Zhang","status":"claimed_verified","statusLastChangedAt":"2026-01-15T15:06:11.587Z","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8fa","name":"Heng Lian","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8fb","name":"Kunyi Wang","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8fc","name":"Rui Xu","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8fd","name":"Sen Hu","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8fe","user":{"_id":"65e459ef400c626ca0968db7","avatarUrl":"/avatars/23177b73ba6e4a9db1165d0b7036a4b7.svg","isPro":false,"fullname":"Jaden (Jianheng) Hou","user":"HJH2CMD","type":"user"},"name":"Jianheng Hou","status":"claimed_verified","statusLastChangedAt":"2026-01-13T15:45:36.919Z","hidden":false},{"_id":"6965babdfc8c4ecc02c7f8ff","name":"Hao Peng","hidden":false},{"_id":"6965babdfc8c4ecc02c7f900","name":"Chengwei Qin","hidden":false},{"_id":"6965babdfc8c4ecc02c7f901","name":"Xiaobin Hu","hidden":false},{"_id":"6965babdfc8c4ecc02c7f902","name":"Hong Peng","hidden":false},{"_id":"6965babdfc8c4ecc02c7f903","name":"Ronghao Chen","hidden":false},{"_id":"6965babdfc8c4ecc02c7f904","user":{"_id":"6603d56ab4344a2b07cd6d21","avatarUrl":"/avatars/1569bb60166532317c85e80da722ba1c.svg","isPro":false,"fullname":"Huacan Wang","user":"Huacan-Wang","type":"user"},"name":"Huacan Wang","status":"claimed_verified","statusLastChangedAt":"2026-01-15T15:06:15.770Z","hidden":false}],"publishedAt":"2026-01-11T15:07:37.000Z","submittedOnDailyAt":"2026-01-13T01:12:08.706Z","title":"Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning","submittedOnDailyBy":{"_id":"64084fa192033c150738e4f2","avatarUrl":"/avatars/dfff2216eb235c635e5abe6fda3084f0.svg","isPro":false,"fullname":"Yu_xm","user":"Yu2020","type":"user"},"summary":"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. 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