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REVIEW 6 major objections 6 minor 6 cited by

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

T0 review · 6 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read ReAgent-V claims a critic agent's live reward signal improves video understanding and reasoning.

desk verdict A credibly engineered agentic video framework whose self-critique reward is plausible but under-validated; worth a serious referee, but the empirical claims outrun the evidence as written. read the letter →

arxiv 2506.01300 v1 pith:4OLKBSK2 submitted 2025-06-02 cs.CV

Yiyang Zhou , Yangfan He , Yaofeng Su , Siwei Han , Joel Jang , Gedas Bertasius , Mohit Bansal , Huaxiu Yao This is my paper · ORCID
classification cs.CV
keywords videounderstandingmulti-agentreasoningreal-timerewardgenerationframeselectionself-reflectionpreferenceoptimizationvision-language-actionalignment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that video language models can be improved without extra human annotation by letting a critic agent score the model's own answers in real time during inference. In ReAgent-V, the target agent first picks informative frames, calls tools, and produces an answer; the critic then rejects weak answers, generates sub-questions, and returns a structured evaluation report with a scalar reward. The target agent revises its answer from conservative, neutral, and aggressive perspectives, and the same scores are used to filter high-quality samples for SFT, DPO, and GRPO. If the claims hold, a single self-reward loop yields up to 6.9% higher video understanding, 2.1% higher video reasoning after fine-tuning, and 9.8% higher robot policy alignment.

What carries the argument

The load-bearing mechanism is the critic-generated process reward, produced on demand during inference and fed back into the target agent. It is paired with the Entropy-Calibrated Relevance Score (ECRS), where each frame is scored as $ECRS_i = s_i H_i / \sum_k H_k$, with $s_i$ the CLIP cosine similarity between frame and question and $H_i$ the average RGB-channel entropy; an iterative threshold selects frames that are both relevant and informative. The reward signal takes the form of an evaluation report with a scalar score and structured feedback, after the critic generates sub-questions and optionally calls additional tools. Three reflection prompts (conservative, neutral, aggressive) convert that report into revised answers, and a meta-agent merges them when all confidences pass a threshold, otherwise choosing the highest-confidence revision.

What would settle it

Take a set of questions with known ground-truth answers, run ReAgent-V, and compare the critic's scalar reward with actual correctness of the initial and revised answers. If revisions chosen because the reward rises are wrong more often than the initial answers, or if GRPO trained on the highest-scored samples performs no better than GRPO on a random subset of the same size, then the reward signal is not carrying the reported gains.

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Extended reading notes

Core claim

The central discovery is that a reward signal produced at inference time by a critic agent built from the same base model as the target can carry the whole improvement loop. ReAgent-V uses entropy-calibrated frame selection to choose question-relevant frames, lets the target agent call tools such as OCR, ASR, and object detection, and then has the critic evaluate the initial answer, generating sub-questions and a report with scalar reward and structured feedback. The target agent then revises the answer from three perspectives—conservative (change only the final answer), neutral (correct scene entities), and aggressive (rebuild reasoning)—and the framework stores high-scoring samples for supervised fine-tuning, direct preference optimization, and group relative policy optimization. On the paper's own experiments this produces improvements up to 6.9% on video understanding benchmarks, 2.1% over vanilla GRPO on video reasoning using only 45% of the training data, and 9.8% over the template-reward baseline on robot policy alignment.

Load-bearing premise

The load-bearing premise is that the critic agent's self-generated scores and feedback are reliable enough to decide which revised answers are correct and which training samples are valuable; if those scores are systematically biased, the reflection step can lock in wrong answers and the data filter can select misleading samples.

Editorial extensions

If this is right

  • Wrapping open-source video LVLMs such as LLaVA-Video and Qwen2.5-VL in ReAgent-V improves their accuracy on six video benchmarks, including gains of 3.2% and 6.9% on the 72B models.
  • Using ReAgent-V scores to filter the Video-R1-260k dataset and then training with GRPO beats vanilla GRPO by a relative 2.1% while using 45% of the data, and beats SFT on the full dataset by 4.3%.
  • Replacing a template-based reward with the ReAgent-V evaluation report in trajectory-wise preference optimization improves robot manipulation success by 9.8% over the GRAPE reward baseline on SIMPLER.
  • The ECRS frame selection cuts per-sample inference time on all tested models while maintaining or improving accuracy, and the reflection module adds consistent gains across model scales.
  • ReAgent-V reaches GPT-4o-level performance on part of the video benchmarks despite using far fewer frames than proprietary pipelines.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if the critic's self-scores remain reliable, the same loop could replace human preference annotation for RL alignment in other long-form modalities such as audio or embodied trajectories.
  • Editorial inference: the paper's reflection analysis shows the aggressive strategy corrects with the lowest accuracy and conservative with the highest, so practical deployments might default to answer-only revision and reserve aggressive revision for cases with high-confidence external evidence.
  • Editorial inference: because the critic and target share a base model, systematic blind spots could be shared; using a differently initialized or separately trained critic would test whether the gains come from true signal or from model-specific bias.
  • Editorial inference: ECRS is a generic relevance-plus-information score, so it could be transferred to other retrieval or keyframe tasks, but the threshold schedule likely needs re-calibration for each new base model and video domain.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

6 major / 6 minor

Summary. The paper proposes ReAgent-V, an agentic framework for video understanding that combines entropy-calibrated frame selection, tool-augmented reasoning, and a critic-based reflection mechanism. During inference, a critic agent generates scalar reward signals and structured feedback; these guide multi-perspective answer refinement (conservative, neutral, aggressive) and are also used to filter training data for SFT, DPO, and GRPO, and to serve as a reward for VLA alignment via TPO. Experiments span 12 datasets across three applications, reporting improvements up to 6.9% in video understanding, 2.1% in video reasoning, and 9.8% in VLA alignment over baselines.

Significance. The central idea—using a model's own critic to produce real-time rewards for both inference-time refinement and training-data selection—is appealing and, if valid, would offer a general, annotation-light way to improve video LVLMs. The modular design and planned code release are strengths. However, the manuscript currently does not establish that the critic's reward is the causal driver of the reported gains, because the reward is never validated against ground truth and no ablation isolates it from the tool use and reflection prompts. With additional evidence, the contribution could be significant for the video-understanding community.

major comments (6)
  1. [Section 3.2, Table 1] The headline '6.9% average performance gain' for LLaVA-Video-72B is not traceable to the table: per-benchmark gains over the reported columns are roughly +4.2 (LongBench), +7.8 (NextQA), +0.4 (EgoSchema), +2.0 (LVBench), +0.5 (MLVU), and about +6.4 (VideoMME overall), which average to about 3.5%, not 6.9%. Please state the exact set of benchmarks and the absolute-vs-relative computation, and correct the text or the table accordingly.
  2. [Section 3.1, Appendix A.1.2] The GRPO reward is unspecified. The text says ReAgent-V scores are used as sample-importance filters that retain samples with scores below 5, but the abstract and Figure 1 describe the mechanism as 'automatic filtering of high-quality data' and the paper's framing implies the self-generated rewards are used in the training objective. If GRPO uses a separate rule-based reward, the contribution is data selection, not reward-driven training; if the critic score is the GRPO reward, the loop is circular because the same model provides both the training signal and the policy. Please clarify the reward source and its role in each training stage.
  3. [Sections 2.3 and 3.3, Tables 4 and 5] The causal role of the critic reward is not isolated. Table 5 shows visual tools alone yield gains up to +7.0 on LongBench, and the reflection ablation in Table 4 removes the critic report, the sub-question generation, and the multi-perspective prompts together. No experiment replaces the critic score with a random or fixed reward, and no correlation between critic scores and ground-truth accuracy is reported. Please provide an ablation that isolates the reward signal and a calibration analysis of the critic scores on a sample of the benchmarks.
  4. [Section 2.1, Eq. (4)] The ECRS hyperparameters τ, k, and α are never specified, and the iterative selection procedure is defined only informally. Without these values, the frame-selection module cannot be reimplemented. Please report the hyperparameter values used in all experiments and, ideally, a sensitivity study over them.
  5. [Section 3.1, Video LLM Reasoning] The data-selection description is internally inconsistent: the text says samples with importance scores lower than 5 are retained because they are more challenging and informative, while the abstract and Figure 1 describe the process as 'high-quality data' filtering. This conflates difficulty with quality. Please reconcile the terminology and discuss how a difficulty heuristic relates to the claimed 'high-quality' data selection.
  6. [Section 3.1, Appendix A.1.3, Figure 3] The VLA alignment experiment is based on 80 rollouts from a single run, and no error bars or multiple seeds are reported; the 9.8% gain over GRAPE may therefore not be statistically reliable. Please report variance across seeds and, if possible, the critic's agreement with the SIMPLER environment's success signal on a held-out subset of rollouts.
minor comments (6)
  1. [Appendix B.2.4, Step 1] The aggregation prompt says 'Combine the three answers {answer_aggressive}, {answer_aggressive}, {answer_aggressive}' and should instead reference the conservative, neutral, and aggressive answers individually.
  2. [Figure 4] The caption does not define 'Corr. Rate' and 'Corr. Acc'; please define these terms in the caption or in the surrounding text.
  3. [Section 3.1 and Table 1 caption] The text states that all baselines except proprietary models were re-evaluated with a similar number of frames, while the Table 1 caption says baseline results are copied from other papers; please clarify which baselines were re-evaluated and under what frame counts.
  4. [Table 3] The 'Selection' column uses checkmarks for the two agent baselines and for ReAgent-V rows, but the meaning of a missing checkmark for the static-model baselines is ambiguous; clarify whether those models used no frame selection.
  5. [Equation (2)] Please specify that p^{(i)}_{j,c} is the normalized histogram probability of pixel value j in channel c, and describe how the histogram is computed for frames of varying resolution.
  6. [Appendix A.1.1] The reference for LLaVA-Video appears to be [56], not [54] as written; please correct the citation.

Circularity Check

1 steps flagged · score 4.0 of 10

No definitional circularity in the reward loop; one load-bearing VLA comparison rests on self-cited GRAPE baselines.

  1. self citation load bearing [Section 3.1 'VLA Alignment' and Section 3.2 'VLA Alignment']
    "Following [57], we aim to enhance the alignment of VLA models by using ReAgent-V as the reward model, replacing the template-based reward function originally proposed in [57]. ... For all baseline models—OpenVLA-SFT, OpenVLA-DPO, and OpenVLA-TPO with GRAPE—we use results reported by [57]. ... ReAgent-V achieves a 9.8% overall improvement over the second-best baseline, GRAPE (which uses a template reward), on SIMPLER under the same setting."

    The VLA application's headline claim—that ReAgent-V's reward is more accurate than GRAPE's template reward—is supported solely by baseline numbers 'reported by [57]' rather than by an independent rerun in this paper. Reference [57] shares three authors with the present paper (Joel Jang, Siwei Han, Huaxiu Yao) and is also the source of both the TPO algorithm and the template reward being replaced. The VLA comparison therefore reduces to a self-citation chain: the framework is validated against a baseline whose method, reward function, and reported results all originate from the same group's prior work, with no external check provided. This is load-bearing for the 9.8% VLA claim, though not a definitional reduction because SIMPLER success is an external metric.

full rationale

The paper's central inference-time reward loop is not definitionally circular. The critic is the same base LVLM as the target, but the reported gains are measured against external ground-truth benchmarks (VideoMME, LongBench, EgoSchema, SIMPLER success rates), so the final accuracy is not equal to the critic's self-score by construction. The ECRS frame-selection rule (Eqs. 1-4) is a new heuristic metric, and the reflection ablations in Table 4 and tool ablations in Table 5 are direct comparisons, not fitted predictions. The GRPO data-selection experiment uses external benchmarks and an independent vanilla-GRPO baseline; retaining low-scoring samples as 'challenging' is a labeling choice, not a circular reduction. The only circularity-adjacent step is the VLA alignment application, where the decisive comparison against GRAPE imports baseline numbers from [57], a paper with overlapping authorship that also supplies the TPO algorithm and the template reward being replaced. This makes the VLA superiority claim self-citation-load-bearing. The paper's own limitation statement—that the reflection and evaluation mechanisms rely on heuristic rules or templates rather than learned, end-to-end reward models—reinforces that the endogenous-reward concern is a validation gap, not a definitional equivalence. Overall, the framework has independent empirical content in the other two applications, so the appropriate score is moderate rather than high.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The framework rests on several hand-chosen thresholds (tau, k, alpha, confidence 0.6, importance cutoff 5) and on heuristic assumptions that CLIP similarity and color entropy capture question-relevant content. The most consequential assumption is that self-generated critic scores are reliable enough to select training data and guide refinement. No new physical or conceptual entities are introduced.

free parameters (5)
  • ECRS base threshold tau = not reported
    Algorithm 1 uses the rule ECRS_i > k * alpha^m * tau, but tau is never specified, so the frame-selection behavior is not reproducible.
  • ECRS scaling constants k and alpha = not reported
    The iterative threshold in Equation (4) depends on k and alpha, but their values are never given and no sensitivity analysis is provided.
  • Confidence threshold for merging reflections = 0.6
    Algorithm 1 merges the three revised answers only if min(p(t)) > 0.6, a hand-chosen threshold without sensitivity analysis.
  • Data-filtering importance cutoff = 5/10
    In Section 3.1, samples with critic importance scores below 5 are retained as hard examples; no held-out validation for this cutoff is described.
  • Maximum number of input frames = inconsistent (32 in Table 1, 64 in Appendix A)
    Algorithm 1 says the selected set is filled to 32 frames if below 32, but Appendix A states 'using 64 frames per video' while Table 1 reports 32 to 38 frames for ReAgent-V runs.
assumptions (4)
  • domain assumption CLIP similarity is a valid measure of frame relevance to the question
    ECRS multiplies CLIP cosine similarity by entropy in Equation (3); the paper itself notes CLIP alone fails on questions where many frames contain a person, so this assumption is known to be imperfect.
  • ad hoc to paper Color histogram entropy measures the amount of question-relevant information in a frame
    Equation (2) defines entropy over RGB pixel values, but no evidence is given that high color entropy correlates with useful semantic content for video QA.
  • ad hoc to paper The same LVLM serving as critic produces a reliable correctness reward
    Section 3.1 states both target and critic models are set to the ReAgent-V-enhanced model itself, and the critic's scores drive reflection and data filtering.
  • domain assumption Baseline numbers copied from other papers are accurate
    Table 1 states baseline results are copied from [59, 9, 40, 2, 30], so any errors in those sources propagate into the comparisons.

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Cite this review

Pith. "Pith review of ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding." pith.science (2026). https://pith.science/paper/4OLKBSK2

@misc{pith2026250601300,
  author       = {Pith},
  title        = {Pith review of: ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4OLKBSK2}},
  note         = {Machine review of arXiv:2506.01300}
}
read the original abstract

Video understanding is fundamental to tasks such as action recognition, video reasoning, and robotic control. Early video understanding methods based on large vision-language models (LVLMs) typically adopt a single-pass reasoning paradigm without dynamic feedback, limiting the model's capacity to self-correct and adapt in complex scenarios. Recent efforts have attempted to address this limitation by incorporating reward models and reinforcement learning to enhance reasoning, or by employing tool-agent frameworks. However, these approaches face several challenges, including high annotation costs, reward signals that fail to capture real-time reasoning states, and low inference efficiency. To overcome these issues, we propose ReAgent-V, a novel agentic video understanding framework that integrates efficient frame selection with real-time reward generation during inference. These reward signals not only guide iterative answer refinement through a multi-perspective reflection mechanism-adjusting predictions from conservative, neutral, and aggressive viewpoints-but also enable automatic filtering of high-quality data for supervised fine-tuning (SFT), direct preference optimization (DPO), and group relative policy optimization (GRPO). ReAgent-V is lightweight, modular, and extensible, supporting flexible tool integration tailored to diverse tasks. Extensive experiments on 12 datasets across three core applications-video understanding, video reasoning enhancement, and vision-language-action model alignment-demonstrate significant gains in generalization and reasoning, with improvements of up to 6.9%, 2.1%, and 9.8%, respectively, highlighting the effectiveness and versatility of the proposed framework.

Figures

Figures reproduced from arXiv: 2506.01300 by the authors.

Figure 1
Figure 1. Overview of the ReAgent-V framework: The system first selects relevant video frames [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Frame selection analysis (VideoMME VideoID: 24i4ncHuf6A, QuestionID:005-2) shows entropy, CLIP score, and ECRS across frames; red lines highlight the most relevant frames. In this subsection, we introduce the keyframe selection strategy adopted in ReAgent-V. Common methods typically rely on generating captions for each frame or directly querying LVLMs such as GPT-4o to select keyframes that are most relevant to the … view at source ↗
Figure 3
Figure 3. Comparison of ReAgent-V with OpenVLA and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Comparison of four reflection strategies [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: A case study demonstrating how ReAgent-V enhances video understanding through iterative [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: ReAgent-V inference pipeline: after ECRS keyframe selection, tools are dynamically selected to generate an initial answer. If critical questions arise, tool outputs are updated for reflection. Three reasoning strategies are used to revise or confirm the answer. C.1 Vis…
Figure 7
Figure 7. Figure 7: ECRS sampling better captures the interaction between the photographer and the tourists. [PITH_FULL_IMAGE:figures/full_fig_p025_7.png]
Figure 8
Figure 8. Figure 8: ECRS highlights the woman’s action of removing the organ more clearly than uniform [PITH_FULL_IMAGE:figures/full_fig_p025_8.png]
Figure 9
Figure 9. Figure 9: ECRS sampling emphasizes the reason behind the student eating the banana with supporting [PITH_FULL_IMAGE:figures/full_fig_p026_9.png]
Figure 10
Figure 10. Figure 10: ECRS captures the banana-taping act that replaced the shredded painting, providing better [PITH_FULL_IMAGE:figures/full_fig_p026_10.png]
Figure 11
Figure 11. Figure 11: Visual and textual cues confirm that the video primarily showcases the Dragon Boat [PITH_FULL_IMAGE:figures/full_fig_p027_11.png]
Figure 12
Figure 12. Figure 12: Multimodal analysis revises the bird count in the video from two to three after detecting [PITH_FULL_IMAGE:figures/full_fig_p027_12.png]
Figure 13
Figure 13. Figure 13: Multi-agent collaboration corrects the cat’s color in the video from "Orange" to "Orange [PITH_FULL_IMAGE:figures/full_fig_p028_13.png]
Figure 14
Figure 14. Figure 14: Agents identify icebergs and snowy terrain, correcting the geographical region from [PITH_FULL_IMAGE:figures/full_fig_p028_14.png]
Figure 15
Figure 15. Figure 15: Visualization of robotic task executions before and after policy fine-tuning using reflection [PITH_FULL_IMAGE:figures/full_fig_p029_15.png]
Figure 16
Figure 16. Figure 16: Comparison of frame selection strategies across multiple videos, illustrating how ECRS [PITH_FULL_IMAGE:figures/full_fig_p030_16.png]
Figure 17
Figure 17. Figure 17: This figure shows tracks two compactness metrics - self-compactness (left y-axis, mean [PITH_FULL_IMAGE:figures/full_fig_p030_17.png]
Figure 18
Figure 18. Figure 18: Frame selection strategies on VideoMME (LLaVA-Video-7B). [PITH_FULL_IMAGE:figures/full_fig_p031_18.png]

Discussion (0). Continue with ORCID to comment.

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Reference graph

Works this paper leans on

76 extracted references · 30 canonical work pages · cited by 6 Pith papers

  1. [1]

    Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736, 2022

    Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736, 2022

    2022

  2. [2]

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report.arXiv preprint arXiv:2502.13923, 2025

    arXiv 2025

  3. [3]

    Longbench: A bilingual, multitask benchmark for long context understanding.arXiv preprint arXiv:2308.14508, 2023

    Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al. Longbench: A bilingual, multitask benchmark for long context understanding.arXiv preprint arXiv:2308.14508, 2023

    arXiv 2023

  4. [4]

    Sharegpt4video: Improving video understanding and generation with better captions.Advances in Neural Information Processing Systems, 37:19472– 19495, 2024

    Lin Chen, Xilin Wei, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Zhenyu Tang, Li Yuan, et al. Sharegpt4video: Improving video understanding and generation with better captions.Advances in Neural Information Processing Systems, 37:19472– 19495, 2024

    2024

  5. [5]

    Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling.arXiv preprint arXiv:2412.05271, 2024

    Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shen- glong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling.arXiv preprint arXiv:2412.05271, 2024

    arXiv 2024

  6. [6]

    Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

    Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 24185–24198, 2024

    2024

  7. [7]

    Supervised learning

    Pádraig Cunningham, Matthieu Cord, and Sarah Jane Delany. Supervised learning. InMachine learning techniques for multimedia: case studies on organization and retrieval, pages 21–49. Springer, 2008

    work page 2008

  8. [8]

    A long video caption generation algorithm for big video data retrieval.Future Generation Computer Systems, 93:583–595, 2019

    Songtao Ding, Shiru Qu, Yuling Xi, and Shaohua Wan. A long video caption generation algorithm for big video data retrieval.Future Generation Computer Systems, 93:583–595, 2019

    work page 2019

Show all 76 references
  1. [9]

    Videoagent: A memory-augmented multimodal agent for video understanding

    Yue Fan, Xiaojian Ma, Rujie Wu, Yuntao Du, Jiaqi Li, Zhi Gao, and Qing Li. Videoagent: A memory-augmented multimodal agent for video understanding. InEuropean Conference on Computer Vision, pages 75–92. Springer, 2024

    2024

  2. [10]

    Video-r1: Reinforcing video reasoning in mllms.arXiv preprint arXiv:2503.21776, 2025

    Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Benyou Wang, and Xiangyu Yue. Video-r1: Reinforcing video reasoning in mllms.arXiv preprint arXiv:2503.21776, 2025

    2025 arXiv

  3. [11]

    Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis.arXiv preprint arXiv:2405.21075, 2024

    Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis.arXiv preprint arXiv:2405.21075, 2024

    2024 arXiv

  4. [12]

    Visbench: A framework for remote data visualization and analysis

    Randy W Heiland, M Pauline Baker, and Danesh K Tafti. Visbench: A framework for remote data visualization and analysis. InInternational Conference on Computational Science, pages 718–727. Springer, 2001

    2001

  5. [13]

    Video-mmmu: Evaluating knowledge acquisition from multi-discipline professional videos

    Kairui Hu, Penghao Wu, Fanyi Pu, Wang Xiao, Yuanhan Zhang, Xiang Yue, Bo Li, and Ziwei Liu. Video-mmmu: Evaluating knowledge acquisition from multi-discipline professional videos. arXiv preprint arXiv:2501.13826, 2025

    2025 arXiv

  6. [14]

    Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024. 10

    2024 arXiv

  7. [15]

    Bimba: Selective-scan compression for long-range video question answering.arXiv preprint arXiv:2503.09590, 2025

    Md Mohaiminul Islam, Tushar Nagarajan, Huiyu Wang, Gedas Bertasius, and Lorenzo Torresani. Bimba: Selective-scan compression for long-range video question answering.arXiv preprint arXiv:2503.09590, 2025

    2025 arXiv

  8. [16]

    Videorag: Retrieval- augmented generation over video corpus.arXiv preprint arXiv:2501.05874, 2025

    Soyeong Jeong, Kangsan Kim, Jinheon Baek, and Sung Ju Hwang. Videorag: Retrieval- augmented generation over video corpus.arXiv preprint arXiv:2501.05874, 2025

    2025 arXiv

  9. [17]

    New generation deep learning for video object detection: A survey.IEEE Transactions on Neural Networks and Learning Systems, 33(8):3195–3215, 2021

    Licheng Jiao, Ruohan Zhang, Fang Liu, Shuyuan Yang, Biao Hou, Lingling Li, and Xu Tang. New generation deep learning for video object detection: A survey.IEEE Transactions on Neural Networks and Learning Systems, 33(8):3195–3215, 2021

    2021

  10. [18]

    A survey of frontiers in llm reasoning: Inference scaling, learning to reason, and agentic systems.arXiv preprint arXiv:2504.09037, 2025

    Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, Peifeng Wang, Silvio Savarese, et al. A survey of frontiers in llm reasoning: Inference scaling, learning to reason, and agentic systems.arXiv preprint arXiv:2504.09037, 2025

    2025

  11. [19]

    Mmctagent: Multi-modal crit- ical thinking agent framework for complex visual reasoning.arXiv preprint arXiv:2405.18358, 2024

    Somnath Kumar, Yash Gadhia, Tanuja Ganu, and Akshay Nambi. Mmctagent: Multi-modal crit- ical thinking agent framework for complex visual reasoning.arXiv preprint arXiv:2405.18358, 2024

    2024 arXiv

  12. [20]

    Video-vot-r1: An efficient video inference model integrating image packing and aoe architecture.arXiv preprint arXiv:2503.15807, 2025

    Cheng Li, Jiexiong Liu, Yixuan Chen, and Yanqin Jia. Video-vot-r1: An efficient video inference model integrating image packing and aoe architecture.arXiv preprint arXiv:2503.15807, 2025

    2025 arXiv

  13. [21]

    Videochat: Chat-centric video understanding.arXiv preprint arXiv:2305.06355, 2023

    KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. Videochat: Chat-centric video understanding.arXiv preprint arXiv:2305.06355, 2023

    2023 arXiv

  14. [22]

    Mvbench: A comprehensive multi-modal video understanding benchmark

    Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22195–22206, 2024

    2024

  15. [23]

    Evaluating real-world robot manipulation policies in simulation

    Xuanlin Li, Kyle Hsu, Jiayuan Gu, Oier Mees, Karl Pertsch, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, and Ted Xiao. Evaluating real-world robot manipulation policies in simulation. In8th...

    2024

  16. [24]

    Video-llava: Learning united visual representation by alignment before projection.arXiv preprint arXiv:2311.10122, 2023

    Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection.arXiv preprint arXiv:2311.10122, 2023

    2023 arXiv

  17. [25]

    Kangaroo: A powerful video-language model supporting long-context video input.arXiv preprint arXiv:2408.15542, 2024

    Jiajun Liu, Yibing Wang, Hanghang Ma, Xiaoping Wu, Xiaoqi Ma, Xiaoming Wei, Jianbin Jiao, Enhua Wu, and Jie Hu. Kangaroo: A powerful video-language model supporting long-context video input.arXiv preprint arXiv:2408.15542, 2024

    2024 arXiv

  18. [26]

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. InEuropean Conference on Computer Vision, pages 38–55. Springer, 2024

    2024

  19. [27]

    Tempcompass: Do video llms really understand videos?arXiv preprint arXiv:2403.00476, 2024

    Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, and Lu Hou. Tempcompass: Do video llms really understand videos?arXiv preprint arXiv:2403.00476, 2024

    2024 arXiv

  20. [28]

    Inference-time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495, 2025

    Zijun Liu, Peiyi Wang, Runxin Xu, Shirong Ma, Chong Ruan, Peng Li, Yang Liu, and Yu Wu. Inference-time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495, 2025

    2025

  21. [29]

    A real- time object detection algorithm for video.Computers & Electrical Engineering, 77:398–408, 2019

    Shengyu Lu, Beizhan Wang, Hongji Wang, Lihao Chen, Ma Linjian, and Xiaoyan Zhang. A real- time object detection algorithm for video.Computers & Electrical Engineering, 77:398–408, 2019

    2019

  22. [30]

    Video-rag: Visually-aligned retrieval-augmented long video comprehension.arXiv preprint arXiv:2411.13093, 2024

    Yongdong Luo, Xiawu Zheng, Xiao Yang, Guilin Li, Haojia Lin, Jinfa Huang, Jiayi Ji, Fei Chao, Jiebo Luo, and Rongrong Ji. Video-rag: Visually-aligned retrieval-augmented long video comprehension.arXiv preprint arXiv:2411.13093, 2024. 11

    2024

  23. [31]

    Egoschema: A diagnostic benchmark for very long-form video language understanding.Advances in Neural Information Processing Systems, 36:46212–46244, 2023

    Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding.Advances in Neural Information Processing Systems, 36:46212–46244, 2023

    2023

  24. [32]

    Optical character recognition.International journal of recent technology and engineering (IJRTE), 2(1):72–75, 2013

    Ravina Mithe, Supriya Indalkar, and Nilam Divekar. Optical character recognition.International journal of recent technology and engineering (IJRTE), 2(1):72–75, 2013

    2013

  25. [33]

    Direct preference optimization: Your language model is secretly a reward model

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728–53741, 2023

    2023

  26. [34]

    Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300, 2024

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300, 2024

    2024 arXiv

  27. [35]

    Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652, 2023

    Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652, 2023

    2023

  28. [36]

    Clip4caption: Clip for video caption

    Mingkang Tang, Zhanyu Wang, Zhenhua Liu, Fengyun Rao, Dian Li, and Xiu Li. Clip4caption: Clip for video caption. InProceedings of the 29th ACM International Conference on Multimedia, pages 4858–4862, 2021

    2021

  29. [37]

    Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.arXiv preprint arXiv:2403.05530, 2024

    Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.arXiv preprint arXiv:2403.05530, 2024

    2024 arXiv

  30. [38]

    Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191, 2024

    Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191, 2024

    2024 arXiv

  31. [39]

    Lvbench: An extreme long video understanding benchmark

    Weihan Wang, Zehai He, Wenyi Hong, Yean Cheng, Xiaohan Zhang, Ji Qi, Xiaotao Gu, Shiyu Huang, Bin Xu, Yuxiao Dong, et al. Lvbench: An extreme long video understanding benchmark. arXiv preprint arXiv:2406.08035, 2024

    2024 arXiv

  32. [40]

    Videoagent: Long-form video understanding with large language model as agent

    Xiaohan Wang, Yuhui Zhang, Orr Zohar, and Serena Yeung-Levy. Videoagent: Long-form video understanding with large language model as agent. InEuropean Conference on Computer Vision, pages 58–76. Springer, 2024

    2024

  33. [41]

    Longllava: Scaling multi-modal llms to 1000 images efficiently via a hybrid architecture.arXiv preprint arXiv:2409.02889, 2024

    Xidong Wang, Dingjie Song, Shunian Chen, Chen Zhang, and Benyou Wang. Longllava: Scaling multi-modal llms to 1000 images efficiently via a hybrid architecture.arXiv preprint arXiv:2409.02889, 2024

    2024

  34. [42]

    Sota with less: Mcts-guided sample selection for data-efficient visual reasoning self-improvement.arXiv preprint arXiv:2504.07934, 2025

    Xiyao Wang, Zhengyuan Yang, Chao Feng, Hongjin Lu, Linjie Li, Chung-Ching Lin, Kevin Lin, Furong Huang, and Lijuan Wang. Sota with less: Mcts-guided sample selection for data-efficient visual reasoning self-improvement.arXiv preprint arXiv:2504.07934, 2025

    2025 arXiv

  35. [43]

    Reinforcement learning.Adaptation, learning, and optimization, 12(3):729, 2012

    Marco A Wiering and Martijn Van Otterlo. Reinforcement learning.Adaptation, learning, and optimization, 12(3):729, 2012

    2012

  36. [44]

    Sailing ai by the stars: A survey of learning from rewards in post-training and test-time scaling of large language models.arXiv preprint arXiv:2505.02686, 2025

    Xiaobao Wu. Sailing ai by the stars: A survey of learning from rewards in post-training and test-time scaling of large language models.arXiv preprint arXiv:2505.02686, 2025

    2025 arXiv

  37. [45]

    Next-qa: Next phase of question- answering to explaining temporal actions

    Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question- answering to explaining temporal actions. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9777–9786, 2021

    2021

  38. [46]

    An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report.arXiv preprint arXiv:2412.15115, 2024

    2024 arXiv

  39. [47]

    Vid2seq: Large-scale pretraining of a visual language model for dense video captioning

    Antoine Yang, Arsha Nagrani, Paul Hongsuck Seo, Antoine Miech, Jordi Pont-Tuset, Ivan Laptev, Josef Sivic, and Cordelia Schmid. Vid2seq: Large-scale pretraining of a visual language model for dense video captioning. InProceedings of the IEEE/CVF Conference on Computer Vision a...

    2023

  40. [48]

    Vca: Video curious agent for long video understanding.arXiv preprint arXiv:2412.10471, 2024

    Zeyuan Yang, Delin Chen, Xueyang Yu, Maohao Shen, and Chuang Gan. Vca: Video curious agent for long video understanding.arXiv preprint arXiv:2412.10471, 2024

    2024 arXiv

  41. [49]

    Re-thinking temporal search for long-form video understanding.arXiv preprint arXiv:2504.02259, 2025

    Jinhui Ye, Zihan Wang, Haosen Sun, Keshigeyan Chandrasegaran, Zane Durante, Cristobal Eyzaguirre, Yonatan Bisk, Juan Carlos Niebles, Ehsan Adeli, Li Fei-Fei, et al. Re-thinking temporal search for long-form video understanding.arXiv preprint arXiv:2504.02259, 2025

    2025 arXiv

  42. [50]

    Springer, 2016

    Dong Yu and Lin Deng.Automatic speech recognition, volume 1. Springer, 2016

    2016

  43. [51]

    Merlot: Multimodal neural script knowledge models.Advances in neural information processing systems, 34:23634–23651, 2021

    Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models.Advances in neural information processing systems, 34:23634–23651, 2021

    2021

  44. [52]

    Omagent: A multi-modal agent framework for complex video understanding with task divide-and-conquer.arXiv preprint arXiv:2406.16620, 2024

    Lu Zhang, Tiancheng Zhao, Heting Ying, Yibo Ma, and Kyusong Lee. Omagent: A multi-modal agent framework for complex video understanding with task divide-and-conquer.arXiv preprint arXiv:2406.16620, 2024

    2024 arXiv

  45. [53]

    Agent-pro: Learning to evolve via policy-level reflection and optimization.arXiv preprint arXiv:2402.17574, 2024

    Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, and Weiming Lu. Agent-pro: Learning to evolve via policy-level reflection and optimization.arXiv preprint arXiv:2402.17574, 2024

    2024 arXiv

  46. [54]

    Tinyllava-video-r1: Towards smaller lmms for video reasoning.arXiv preprint arXiv:2504.09641, 2025

    Xingjian Zhang, Siwei Wen, Wenjun Wu, and Lei Huang. Tinyllava-video-r1: Towards smaller lmms for video reasoning.arXiv preprint arXiv:2504.09641, 2025

    2025 arXiv

  47. [55]

    Llava-next: A strong zero-shot video understanding model

    Y Zhang, B Li, H Liu, Y Lee, L Gui, D Fu, J Feng, Z Liu, and C Li. Llava-next: A strong zero-shot video understanding model. 2024

    2024

  48. [56]

    Video instruction tuning with synthetic data.arXiv preprint arXiv:2410.02713, 2024

    Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data.arXiv preprint arXiv:2410.02713, 2024

    2024 arXiv

  49. [57]

    GRAPE: Generalizing robot policy via preference alignment

    Zijian Zhang, Kaiyuan Zheng, Zhaorun Chen, Joel Jang, Yi Li, Siwei Han, Chaoqi Wang, Mingyu Ding, Dieter Fox, and Huaxiu Yao. GRAPE: Generalizing robot policy via preference alignment. InWorkshop on Reasoning and Planning for Large Language Models, 2025

    2025

  50. [58]

    Mmvu: Measuring expert-level multi-discipline video understanding.arXiv preprint arXiv:2501.12380, 2025

    Yilun Zhao, Lujing Xie, Haowei Zhang, Guo Gan, Yitao Long, Zhiyuan Hu, Tongyan Hu, Weiyuan Chen, Chuhan Li, Junyang Song, et al. Mmvu: Measuring expert-level multi-discipline video understanding.arXiv preprint arXiv:2501.12380, 2025

    2025 arXiv

  51. [59]

    Mlvu: A comprehensive benchmark for multi-task long video understanding.arXiv preprint arXiv:2406.04264, 2024

    Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding.arXiv preprint arXiv:2406.04264, 2024

    2024 arXiv

  52. [60]

    function

    Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019. 13 A Experimental Setup A.1 Dataset and Baselines A.1.1 Video...

    1909 arXiv

  53. [61]

    Re-examine the video or image frames forobject-level details(e.g., people, objects, colors, gestures)

  54. [62]

    Determine whether the initial answer failed due to incorrect or missing perception of visual entities

  55. [63]

    Adjust the relevant scene elements accordingly (e.g., update object color, position, or identity)

  56. [64]

    final_answer

    Reuse the original reasoning chain, now applied to the corrected visual grounding. [Evaluation Guidelines] • Remain neutral — do not assume the initial answer is incorrect unless there is clear evidence of perceptual error. • Do not modify the reasoning logic — preserve the or...

  57. [65]

    Re-examine the visual content for any direct contradiction to the initial answer

  58. [66]

    Accept only literal, unambiguous visual cues that fully invalidate the original answer

  59. [67]

    Do not alter object interpretation, scene structure, or logical flow

  60. [68]

    final_answer

    Revise the final output only if the contradiction is undeniable and renders the original answer unsupportable. [Evaluation Guidelines] • Retain the original answer if: –Any uncertainty or ambiguity exists in the evidence –Visual information lacks a clear, literal contradiction...

  61. [69]

    Re-examine all video frames for cues that could support a different interpretation

  62. [70]

    Modify the understanding of relevant visual entities (objects, attributes, spatial relations)

  63. [71]

    Reconstruct the reasoning chain based on the newly grounded observations

  64. [72]

    canine” = “dog

    Select a new answer that best fits the revised reasoning and evidence. [Evaluation Guidelines] • Always replace the original answer — the default assumption is that it is suboptimal or incorrect. • Consider alternative answers even when based on partial, ambiguous, or abstract...

  65. [73]

    Check if the answer fully addresses the question

  66. [74]

    Verify all key elements from context are included

  67. [75]

    Assess whether video content supports the answer

  68. [76]

    What”, “Where

    If not, raise sub-questions to expose missing or uncertain reasoning [Clarification Guidelines] If the answer is incomplete, generate 1–3 ultra-specific clarification questions following these rules: • Must start with: “What”, “Where”, “When”, “Which”, or “How” • Must referenc...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.