pith. sign in

arxiv: 2601.06943 · v2 · pith:6JRELLUEnew · submitted 2026-01-11 · 💻 cs.CV · cs.AI

Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning

Chengwen Liu , Xiaomin Yu , Zhuoyue Chang , Zhe Huang , Shuo Zhang , Heng Lian , Jisheng Dang , Rui Xu
show 10 more authors
Sen Hu Jianheng Hou Chengwei Qin Xiaobin Hu Kunyi Wang Zhi Yang Hao Peng Hong Peng Ronghao Chen Huacan Wang
This is my paper

Pith reviewed 2026-05-21 16:29 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords video question answeringagentic reasoningopen-web retrievalmultimodal large language modelsbenchmark constructiongoal driftlong-horizon consistency
0
0 comments X

The pith

Video deep research succeeds only when models keep initial visual anchors intact across long web-retrieval chains.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds VideoDR, the first benchmark that forces models to pull localized clues from video frames, iteratively search the open web, and verify answers through multi-hop reasoning over the combined evidence. It tests both structured workflow and agentic paradigms on closed- and open-source multimodal models and finds that agentic methods are not reliably better. Gains appear only when a model can preserve the original video anchors through extended retrieval sequences. The work shows that goal drift and loss of long-horizon consistency are the main obstacles to reliable video deep research agents.

Core claim

VideoDR demonstrates that video-conditioned open-domain question answering requires joint cross-frame visual anchor extraction, interactive web retrieval, and multi-hop reasoning over video-web evidence; under these conditions agentic execution is not consistently superior to workflow execution because performance depends on a model's ability to retain the initial video anchors over long retrieval chains.

What carries the argument

The VideoDR benchmark, a set of human-annotated samples that demand cross-frame visual anchors plus open-web multi-hop verification rather than video-only or superficial lookup answers.

If this is right

  • Agentic video systems will remain unreliable on tasks whose answers lie outside the video unless long-horizon consistency is solved.
  • Structured workflow pipelines may currently be more dependable for maintaining visual grounding across retrieval steps.
  • Goal drift becomes the dominant failure mode once retrieval chains exceed a few hops.
  • Benchmarks that isolate anchor retention can directly measure progress on the core bottleneck.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same anchor-drift problem is likely to appear in other long-horizon agent tasks such as multi-step image or audio reasoning.
  • Adding explicit memory or re-anchoring mechanisms could be tested as a direct fix for the consistency failures identified here.
  • The benchmark could be extended to measure how different retrieval strategies affect anchor preservation.

Load-bearing premise

Human annotation and quality control produce questions that genuinely need both video visual anchors and open-web multi-hop reasoning.

What would settle it

Run the same questions with web access removed and measure whether accuracy collapses; if models still answer correctly from the video alone, the benchmark does not test the claimed joint requirement.

Figures

Figures reproduced from arXiv: 2601.06943 by Chengwei Qin, Chengwen Liu, Hao Peng, Heng Lian, Hong Peng, Huacan Wang, Jianheng Hou, Jisheng Dang, Kunyi Wang, Ronghao Chen, Rui Xu, Sen Hu, Shuo Zhang, Xiaobin Hu, Xiaomin Yu, Zhe Huang, Zhi Yang, Zhuoyue Chang.

Figure 1
Figure 1. Figure 1: Overview of the VideoDR construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An example of the VideoDR task: identifying a museum via video visual cues, then using multi-hop [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Human solvability across benchmark difficulty levels. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Data statistics of VideoDR, including (a) video category, (b) question length, and (c)video duration. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript introduces VideoDR, the first benchmark for video deep research consisting of video-conditioned open-domain QA samples that require cross-frame visual anchor extraction from video, iterative open-web retrieval, and multi-hop reasoning over joint video-web evidence. Samples span six semantic domains and were obtained via rigorous human annotation and quality control. Evaluations of closed- and open-source MLLMs under Workflow versus Agentic paradigms show that Agentic approaches are not consistently superior; performance gains depend on a model's ability to maintain initial video anchors over long retrieval chains. Further analysis identifies goal drift and long-horizon consistency as the core bottlenecks for video agents in open-web settings.

Significance. If the benchmark samples genuinely require the joint cross-frame visual anchoring plus multi-hop web verification described, VideoDR would constitute a valuable new resource for studying agentic video reasoning in realistic open-web scenarios. The explicit finding that Agentic paradigms do not reliably outperform Workflow ones, together with the identification of goal drift as a bottleneck, supplies concrete guidance for future agent design. The use of human annotation and quality control is a positive feature that supports the benchmark's intended difficulty.

major comments (2)
  1. [Abstract and Benchmark Construction] Abstract and Benchmark Construction section: the central claims (Agentic not consistently superior; gains depend on maintaining video anchors; goal drift as bottleneck) rest on the premise that VideoDR questions cannot be solved from video alone, a single web lookup, or superficial keyword search. The manuscript states that rigorous human annotation and quality control were applied but supplies no quantitative breakdown (e.g., accuracy of single-modality or single-hop baselines on the final sample set). Without such statistics the observed paradigm differences and the long-horizon consistency diagnosis cannot be unambiguously attributed to the claimed joint-reasoning requirements.
  2. [Evaluation] Evaluation section: the statement that 'Agentic is not consistently superior to Workflow' is load-bearing for the paper's conclusions, yet the manuscript does not report per-model or per-domain win rates, confidence intervals, or the exact retrieval and verification protocols used in each paradigm. Adding these details (or a table contrasting the two paradigms across the six domains) would allow readers to assess whether the non-superiority result is robust or driven by a subset of models or domains.
minor comments (3)
  1. [Benchmark Construction] The six semantic domains should be enumerated explicitly with one or two example questions each so readers can judge coverage and difficulty.
  2. [Introduction] Define 'Workflow' versus 'Agentic' paradigms with a short illustrative example or diagram in the introduction; the current description is clear only after reading the evaluation section.
  3. Clarify whether the released benchmark includes the full retrieval trajectories or only the final answers; this affects reproducibility of the long-horizon consistency analysis.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have carefully addressed each major comment below, making revisions to strengthen the presentation of the benchmark requirements and evaluation results.

read point-by-point responses

  1. Referee: [Abstract and Benchmark Construction] Abstract and Benchmark Construction section: the central claims (Agentic not consistently superior; gains depend on maintaining video anchors; goal drift as bottleneck) rest on the premise that VideoDR questions cannot be solved from video alone, a single web lookup, or superficial keyword search. The manuscript states that rigorous human annotation and quality control were applied but supplies no quantitative breakdown (e.g., accuracy of single-modality or single-hop baselines on the final sample set). Without such statistics the observed paradigm differences and the long-horizon consistency diagnosis cannot be unambiguously attributed to the claimed joint-reasoning requirements.

    Authors: We agree that quantitative evidence demonstrating the necessity of joint cross-frame and multi-hop reasoning would strengthen the attribution of our findings to the benchmark design. In the revised manuscript, we have added a new analysis in the Benchmark Construction section reporting the accuracy of video-only, web-only, and single-hop baselines on the final curated sample set. These results show substantially lower performance compared to the full setting, supporting that the questions require the described capabilities. We have also expanded the description of the human annotation and quality control process to clarify how annotators ensured the need for iterative retrieval and verification. revision: yes

  2. Referee: [Evaluation] Evaluation section: the statement that 'Agentic is not consistently superior to Workflow' is load-bearing for the paper's conclusions, yet the manuscript does not report per-model or per-domain win rates, confidence intervals, or the exact retrieval and verification protocols used in each paradigm. Adding these details (or a table contrasting the two paradigms across the six domains) would allow readers to assess whether the non-superiority result is robust or driven by a subset of models or domains.

    Authors: We appreciate this recommendation for enhancing the transparency of our results. In the revised Evaluation section, we have added a table that reports per-model and per-domain win rates between the Agentic and Workflow paradigms, including 95% confidence intervals. We have also provided explicit descriptions of the retrieval and verification protocols for each paradigm directly in the main text, with further implementation details included in the appendix. These additions allow readers to evaluate the robustness of the non-superiority observation across the six domains and models. revision: yes

Circularity Check

0 steps flagged

No significant circularity; new benchmark via human annotation with empirical evaluation

full rationale

The paper introduces VideoDR as a new benchmark constructed through human annotation and quality control for video-conditioned open-web QA tasks. It then reports empirical model evaluations under Workflow and Agentic paradigms, identifying observations such as Agentic not being consistently superior and goal drift as a bottleneck. No equations, parameter fits, or derivations are present that reduce claims to inputs by construction. Central results rest on fresh data collection rather than self-referential definitions, fitted predictions, or load-bearing self-citations. This is a standard benchmark paper with independent empirical content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The benchmark rests on the domain assumption that human annotators can reliably identify questions whose answers truly require both video frames and open-web evidence. No free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Human annotation with quality control produces samples that genuinely require cross-frame visual anchors plus open-web multi-hop reasoning.
    Invoked in the description of sample collection and the claim that the benchmark tests the intended capabilities.

pith-pipeline@v0.9.0 · 5783 in / 1311 out tokens · 43737 ms · 2026-05-21T16:29:28.366916+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Omni-DeepSearch: A Benchmark for Audio-Driven Omni-Modal Deep Search

    cs.SD 2026-05 unverdicted novelty 8.0

    Omni-DeepSearch is a 640-sample benchmark for audio-driven omni-modal search where the best model reaches only 43.44% accuracy, exposing bottlenecks in audio inference, tool use, and cross-modal reasoning.

  2. From Where Things Are to What They Are For: Benchmarking Spatial-Functional Intelligence in Multimodal LLMs

    cs.CV 2026-05 unverdicted novelty 5.0

    SFI-Bench shows current multimodal LLMs struggle to integrate spatial memory with functional reasoning and external knowledge in video tasks.

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · cited by 2 Pith papers · 9 internal anchors

  1. [1]

    Boyu Chen, Zhengrong Yue, Siran Chen, Zikang Wang, Yang Liu, Peng Li, and Yali Wang. 2025a. Lvagent: Long video understanding by multi-round dynamical collaboration of mllm agents.arXiv preprint arXiv:2503.10200. Mingyang Chen, Linzhuang Sun, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang, Jeff Z Pan, Wen Zhang, Huajun Chen, and 1 others. ...

    work page arXiv

  2. [2]

    Retrieval-Augmented Generation for Large Language Models: A Survey

    Retrieval-augmented generation for large language models: A survey.arXiv preprint arXiv:2312.10997, 2(1). Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    GPT-4o System Card

    Gpt-4o system card.arXiv preprint arXiv:2410.21276. Lawrence Jang, Yinheng Li, Dan Zhao, Charles Ding, Justin Lin, Paul Pu Liang, Rogerio Bonatti, and Kazuhito Koishida

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    arXiv preprint arXiv:2410.19100

    Videowebarena: Evaluating long context multimodal agents with video understanding web tasks. arXiv preprint arXiv:2410.19100. Dongzhi Jiang, Renrui Zhang, Ziyu Guo, Yanmin Wu, Jiayi Lei, Pengshuo Qiu, Pan Lu, Zehui Chen, Chaoyou Fu, Guanglu Song, and 1 others

    work page arXiv

  5. [5]

    Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han

    Mmsearch: Benchmarking the potential of large models as multi-modal search engines.arXiv preprint arXiv:2409.12959. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han

    work page arXiv

  6. [6]

    Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

    Search-r1: Training llms to reason and leverage search engines with reinforcement learning.arXiv preprint arXiv:2503.09516. Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, and 1 others. 2024a. Mvbench: A comprehensive multi-modal video understanding benchmark. InProceedings of the IEEE/CVF Conference o...

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others

    Video-browsecomp: Benchmarking agentic video research on open web.arXiv preprint arXiv:2512.23044. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others

    work page arXiv

  8. [8]

    DeepSeek-V3 Technical Report

    Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437. Bang Liu, Xinfeng Li, Jiayi Zhang, Jinlin Wang, Tanjin He, Sirui Hong, Hongzhang Liu, Shaokun Zhang, Kaitao Song, Kunlun Zhu, and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

    Advances and challenges in foundation agents: From brain-inspired intelligence to evolutionary, collaborative, and safe systems.arXiv preprint arXiv:2504.01990. Arsha Nagrani, Mingda Zhang, Ramin Mehran, Rachel Hornung, Nitesh Bharadwaj Gundavarapu, Nilpa Jha, Austin Myers, Xingyi Zhou, Boqing Gong, Cordelia Schmid, and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  10. [10]

    Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, and 1 others

    Neptune: The long orbit to benchmarking long video understanding.arXiv preprint arXiv:2412.09582. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, and 1 others

    work page arXiv

  11. [11]

    Gemini: A Family of Highly Capable Multimodal Models

    Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805. Weihan Wang, Zehai He, Wenyi Hong, Yean Cheng, Xiaohan Zhang, Ji Qi, Ming Ding, Xiaotao Gu, Shiyu Huang, Bin Xu, and 1 others. 2025a. Lvbench: An extreme long video understanding benchmark. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages ...

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

    Browsecomp: A simple yet challenging benchmark for browsing agents.arXiv preprint arXiv:2504.12516. 11 Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    Webwalker: Benchmarking llms in web traversal.arXiv preprint arXiv:2501.07572. Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, Ting He, Xinfa Zhu, Yuanjun Lv, Yongqi Wang, Dake Guo, He Wang, Linhan Ma, Pei Zhang, Xinyu Zhang, Hongkun Hao, Zishan Guo, and 19 others

    work page arXiv

  14. [14]

    Qwen3-Omni Technical Report

    Qwen3-omni technical report.arXiv preprint arXiv:2509.17765. Zhenghai Xue, Longtao Zheng, Qian Liu, Yingru Li, Xiaosen Zheng, Zejun Ma, and Bo An

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    SimpleTIR: End-to-end reinforcementlearningformulti-turntool-integratedreasoning.arXivpreprintarXiv:2509.02479,2025

    Simpletir: End-to-end reinforcement learning for multi-turn tool-integrated reasoning.arXiv preprint arXiv:2509.02479. Zhenyu Yang, Yuhang Hu, Zemin Du, Dizhan Xue, Shengsheng Qian, Jiahong Wu, Fan Yang, Weiming Dong, and Changsheng Xu. 2025a. Svbench: A benchmark with temporal multi-turn dialogues for streaming video understanding.arXiv preprint arXiv:25...

    work page arXiv

  16. [16]

    Vrbench: A benchmark for multi-step reasoning in long nar- rative videos.arXiv preprint arXiv:2506.10857, 2025

    React: Synergizing reasoning and acting in language models. InThe eleventh international conference on learning representations. Jiashuo Yu, Yue Wu, Meng Chu, Zhifei Ren, Zizheng Huang, Pei Chu, Ruijie Zhang, Yinan He, Qirui Li, Songze Li, and 1 others. 2025a. Vrbench: A benchmark for multi-step reasoning in long narrative videos.arXiv preprint arXiv:2506...

    work page arXiv

  17. [17]

    Deep video discovery: Agentic search with tool use for long-form video understanding.arXiv preprint arXiv:2505.18079, 2025

    Deep video discovery: Agentic search with tool use for long-form video understanding.arXiv preprint arXiv:2505.18079. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, and 1 others

    work page arXiv

  18. [18]

    DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

    Deepresearcher: Scaling deep research via reinforcement learning in real-world environments.arXiv preprint arXiv:2504.03160. Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv