Pith. sign in

REVIEW 3 major objections 7 minor 76 references

MASR: Self-Reflective Reasoning through Multimodal Hierarchical Attention Focusing for Agent-based Video Understanding

T0 review · 3 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read MASR claims that a training-free self-reflective attention-focusing loop lifts video question answering accuracy by up to 5.2 points across four benchmarks.

desk verdict MASR's 5-point EgoSchema gain is worth a referee's time, but the confidence-gated stopping rule is uncalibrated and the small SOTA margins rest on test-set tuning. read the letter →

arxiv 2504.17213 v2 pith:5EYRGOGG submitted 2025-04-24 cs.CV cs.AI

Shiwen Cao , Zhaoxing Zhang , Junming Jiao , Juyi Qiao , Guowen Song , Rong Shen , Xiangbing Meng This is my paper · ORCID
classification cs.CVcs.AI
keywords agent-basedvideounderstandingself-reflectivereasoningattentionfocusingquestionansweringdilatedtemporalexpansionconfidencefeedbackmultimodalrelevancesensingtraining-freeagent
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

The paper is trying to establish that an agent-based video understanding system can improve answer accuracy by repeatedly focusing attention on query-relevant frames rather than consuming an entire video at once. MASR clusters sampled frames, coarsely selects candidate clips through LLM reasoning, fine-selects individual frames by comparing visual tokens with the query text, expands each focused frame around its temporal neighborhood, and only then answers; if the LLM's self-assessed confidence is low, the loop repeats with a sharper selection. On the EgoSchema benchmark the reported accuracy is 73.4%, a 5.2-point gain over the previous best listed baseline, with smaller gains on NExT-QA and IntentQA and 57.1% on Video-MME's long split. These results matter because they suggest that training-free agents can match or beat fine-tuned video models by better allocating attention. If correct, the framework also inherits improvements in its underlying LLMs automatically.

What carries the argument

The load-bearing mechanism is the MCRS-DTE-self-reflection loop. MCRS is a two-stage relevance sensor: an LLM coarsely names query-relevant clips from accumulated context, then a visual encoder embeds frames and counts how many visual tokens are highly similar to the query's text token to fine-select the most relevant frames. DTE treats each focused frame as a temporal anchor, concatenating neighboring frames at fixed dilation intervals to widen the receptive field, analogous to dilated convolution in 1D. The self-reflection stops only when the same LLM that answers the question reports a confidence score of C = 3; below that, the loop re-selects clips and frames with the new context. The argument requires this confidence signal to be meaningful.

What would settle it

On a labeled subset, run MASR and record the confidence score and round at which it stops, then compare accuracy for items that stop at C = 3 against items forced to stop at C = 2 or after one round; if forced-early answers are not measurably worse, the confidence-based stopping rule is not the cause of the reported gains.

Watch

Extended reading notes

Core claim

MASR's central claim is that the main bottleneck in video QA is not model capacity but attention allocation: a model needs to know where to look before it can answer. The paper argues that a multimodal coarse-to-fine relevance-sensing mechanism, which combines LLM-based clip selection with visual-token-to-query similarity matching, followed by dilated temporal expansion around the chosen frames, produces a more complete and more relevant context than uniform sampling or single-pass captioning. The self-reflection loop then monitors the LLM's confidence score and re-enters the focusing process whenever the score is below the stopping threshold. With this loop, MASR reports 73.4% on EgoSchema, 73.1% on IntentQA, 75.8% on NExT-QA, and 57.1% on the Video-MME long split, all without supervised fine-tuning of the backbone model.

Load-bearing premise

The framework assumes the LLM's self-reported confidence score is reliably correlated with answer correctness, yet the paper presents no calibration data for that score.

Editorial extensions

If this is right

  • If the reported numbers hold, training-free agent pipelines can outperform fine-tuned video-specific MLLMs on medium-length QA without any in-domain training.
  • A single LLM can simultaneously answer, evaluate its own confidence, and select relevant clips; no separate reward model is required for accurate attention focusing.
  • The ablation predicts that better visual encoders and stronger reasoning LLMs directly translate into higher video QA accuracy for the same framework.
  • Because the stopping rule caps self-reflection rounds, computation cost is bounded, but higher dilation windows or frame intervals degrade accuracy, so temporal expansion must be tuned rather than maximized.
  • Performance on very long videos, including the Video-MME long split, also improves over agent-based baselines, suggesting the same focusing loop scales to longer inputs.

Reading between the lines

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

  • A testable extension: vary the confidence threshold and the round cap; if accuracy is insensitive to them, the gain comes from the focusing mechanism, whereas if it spikes only at C = 3, the stopping rule is doing more work than the paper measures.
  • The paper does not calibrate confidence against correctness; I infer that a miscalibrated self-report could either halt the loop too early on hard questions or force extra rounds on easy ones, so the reported margins may depend on backbone-specific calibration.
  • Because DTE borrows the dilation idea from convolutional networks, a natural next step the paper leaves implicit is adaptive per-clip dilation rates chosen by the coarse selector rather than fixed hyperparameters.
  • The headline EgoSchema gain combines the MCRS/DTE loop with a specific captioner; unless the captioner is held fixed across baselines, part of the margin may be captioning quality rather than attention focusing.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. The paper proposes MASR, a training-free agent-based video question-answering framework. The pipeline samples and clusters video frames, then iteratively applies multimodal coarse-to-fine relevance sensing (MCRS) and dilated temporal expansion (DTE) to focus on query-relevant frames; a single LLM generates the answer, assigns a confidence score C, and decides whether to continue the loop until C==3 or a round cap N. Experiments on EgoSchema, NExT-QA, IntentQA, and the Video-MME long split report state-of-the-art accuracies, with the largest claimed gain being 5.2 points on EgoSchema.

Significance. If the reported numbers hold, the paper shows that a plug-and-play, training-free agent can improve video QA on several benchmarks without fine-tuning the underlying LLM. The architecture is clearly described, the ablation suite covers each module, and the decision to use a single LLM as responder, evaluator, and coarse selector is an appealing efficiency choice. The EgoSchema improvement is large enough that the mechanism deserves scrutiny. However, the evidence for the confidence-gated stopping mechanism, the small-margin SOTA claims, and the hyperparameter sensitivity is currently incomplete; the paper also does not release code or full prompt details, and several table and figure labels are inconsistent.

major comments (3)
  1. [Section III-D, Algorithm 1 line 9] The stopping rule `if C == 3 then break` is never validated. The paper provides no calibration curve, no conditional probability P(correct | C), no threshold-sensitivity analysis, and no comparison against a fixed-round stopping baseline. Since Section IV-C and Figure 5 report that accuracy increases with repeated self-reflective rounds, a miscalibrated confidence read could terminate the loop before MCRS and DTE supply the missing context. The claim that adaptive, confidence-driven self-reflection is responsible for the reported accuracy gains is therefore not established without a calibration analysis.
  2. [Section IV-C, Table I] All results appear to be single runs, and the NExT-QA and IntentQA margins over the prior best results are 0.2 and 0.3 percentage points, respectively. No error bars, confidence intervals, significance tests, or run-to-run variability are reported. On evaluation sets of roughly 4,969 and 2,134 items, these margins are within typical sampling noise; the SOTA claims need statistical support or a statement of variance.
  3. [Section IV-B and Section IV-E, Tables VI-VIII] The DTE hyperparameters (wn, s, r, w) are adjusted per dataset in Section IV-B, and Kv, wn, and r are selected from the EgoSchema ablations in Tables VI, VII, and VIII. Because the same EgoSchema benchmark is used for the headline +5.2 improvement claim, the reported gain may partly reflect test-set selection rather than a property of the architecture. The authors should use a validation split for hyperparameter selection or report sensitivity of all four datasets to these parameters.
minor comments (7)
  1. [Section IV-C, Table I] The (+x.x) annotations are not tied to a named baseline; for example, the EgoSchema +5.2 is against LVNet at 68.2 rather than the adjacent VideoTree row at 66.2. Please state the reference baseline in the table or caption.
  2. [Section IV-C, Figure 5] The caption describes a 'demonstration' but the surrounding text describes a quantitative comparison of accuracy across self-reflective rounds; the caption should match the content.
  3. [Section IV-B] Please specify the exact clustering algorithm and number of clusters N, and provide the full text of the captioning prompt, the answer prompt, and the confidence-score prompt, since these are essential for reproducibility in an agent-based method.
  4. [Table III] For EVA-CLIP-8B-plus, increasing the input resolution from 224 to 448 decreases accuracy from 73.4 to 71.4; adding a sentence of explanation would prevent confusion.
  5. [Section II and throughout] There are several typos and grammatical issues, including 'Mutilmodal', 'feet the LLMs', and 'This types'; the paper needs a careful copyedit.
  6. [Section IV-C and Figure 7] The method is called MASR in Section III, but the result row in Table I is labeled 'CLARF (Ours)' and Figure 7 refers to 'MACF'. Please use one consistent name for the proposed method.
  7. [Abstract and Section IV-C] The abstract says 'a remarkable 5% performance gain' while the table reports +5.2 points; please use consistent wording (percentage points versus percent) throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: all claimed results are externally measured benchmark accuracies; no derivation step reduces to its inputs.

full rationale

The paper's central claims are empirical accuracies on EgoSchema, NExT-QA, IntentQA, and Video-MME. These are external measurements, not consequences of the framework's definitions. The attention-focusing mechanism (MCRS) selects frames by cosine similarity between visual tokens and query text; DTE expands temporally; the self-reflection loop stops on LLM confidence C==3. None of these equations define the accuracy value or fit a parameter to the target result. Hyperparameters such as wn, s, r, and w were configured per dataset, which is test-set tuning and a generalization concern, not circular reasoning. The uncalibrated confidence threshold is an unsupported empirical assumption, not a circular derivation. No load-bearing self-citation chain is present; cited prior methods are external. Minor manuscript inconsistencies (CLARF/MACF vs MASR) do not constitute circularity. Appendix limitations (computational latency, context balance) are stated but do not create circularity. Therefore score 0.

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

The central method relies on assumptions about relevance signals, caption fidelity, clustering quality, and LLM confidence calibration. The only hand-fitted numbers are the DTE hyperparameters, sampling rates, Kv, and the confidence threshold, all selected per dataset without external validation.

free parameters (4)
  • DTE parameters (wn, s, r, w) = wn=3, s=3, r=2, w=6 for EgoSchema/NExT-QA/IntentQA; wn=3, s=5, r=1, w=6 for Video-MME long split
    Hand-selected per dataset in Section IV-B; ablations in Tables VII and VIII show sensitivity, with accuracy ranging from 69.0 to 73.4 for wn and 70.4 to 73.4 for r.
  • Frame sampling rate = 1 FPS for EgoSchema/NExT-QA/IntentQA; 0.5 FPS for Video-MME
    Chosen per dataset in Section IV-B without justification; the sampling rate determines which frames enter clustering and later selection.
  • Number of similarity candidates Kv = 90 (best value in ablation)
    Table VI shows accuracy improving from 70.6 at Kv=30 to 73.4 at Kv=90 on EgoSchema; the reported SOTA uses the best-seen value.
  • Confidence threshold C = 3
    In Section III-D the loop halts at C=3; the threshold is fixed without calibration evidence or sensitivity analysis.
assumptions (4)
  • domain assumption Cosine similarity between visual tokens and the query's text token is a valid indicator of query relevance for selecting frames.
    Used in MCRS fine focusing (Section III-B, Step 2). No evidence in the paper that this similarity ranking correlates with answer correctness.
  • ad hoc to paper The LLM's self-reported confidence score C is informative enough to gate whether more frames should be inspected.
    Section III-D defines the loop around C; no calibration experiment or correlation analysis is provided.
  • domain assumption Uniform frame sampling plus visual feature clustering yields semantically coherent clips that contain the relevant content.
    Initialization in Section III-A; if relevant content falls between sampled frames or clusters, it will never be selected.
  • domain assumption Generated captions from the chosen VLM retain enough query-relevant visual detail for the LLM to answer.
    Step 4 in the pipeline uses VLM captions as the only visual information entering the answerer; caption quality is not measured.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MASR: Self-Reflective Reasoning through Multimodal Hierarchical Attention Focusing for Agent-based Video Understanding." pith.science (2026). https://pith.science/paper/5EYRGOGG

@misc{pith2026250417213,
  author       = {Pith},
  title        = {Pith review of: MASR: Self-Reflective Reasoning through Multimodal Hierarchical Attention Focusing for Agent-based Video Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5EYRGOGG}},
  note         = {Machine review of arXiv:2504.17213}
}
read the original abstract

Even in the era of rapid advances in large models, video understanding remains a highly challenging task. Compared to texts or images, videos commonly contain more information with redundancy, requiring large models to properly allocate attention at a global level for comprehensive and accurate understanding. To address this, we propose a Multimodal hierarchical Attention focusing Self-reflective Reasoning (MASR) framework for agent-based video understanding. The key innovation lies in its ability to detect and prioritize segments of videos that are highly relevant to the query. Firstly, MASR realizes Multimodal Coarse-to-fine Relevance Sensing (MCRS) which enhances the correlation between the acquired contextual information and the query. Secondly, MASR employs Dilated Temporal Expansion (DTE) to mitigate the risk of missing crucial details when extracting semantic information from the focused frames selected through MCRS. By iteratively applying MCRS and DTE in the self-reflective reasoning process, MASR is able to adaptively adjust the attention to extract highly query-relevant context and therefore improve the response accuracy. In the EgoSchema dataset, MASR achieves a remarkable 5% performance gain over previous leading approaches. In the Next-QA and IntentQA datasets, it outperforms the state-of-the-art standards by 0.2% and 0.3% respectively. In the Video-MME dataset that contains long-term videos, MASR also performs better than other agent-based methods.

Figures

Figures reproduced from arXiv: 2504.17213 by the authors.

Figure 1
Figure 1. A comparison of the two mainstream MLLM-based video understanding frameworks: Video-MLLM-based and Agent-based. In particular, the purple-highlighted sections in the agent-based method indicate the creative works in our MASR framework. superiority of our single LLM-based self-reflection module. • Plug-and-play architecture: MASR is compatible with concurrent mainstream LLMs and MLLMs. Its architecture ensures that o… view at source ↗
Figure 2
Figure 2. A illustration of the complete MASR pipeline, where the leftmost section displays the input query [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. A example of the implementation of fine-grained relevance sensing process. Between the two [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: A example of DTE process in Step 3 with the parameters w = 7, r = 2, wn = 3 and s = 3 showing a total of 9 frames are expanded through DTE for each fine-focused frame. These parameters can be adjusted adaptively. guide the attention focus of the framework toward the re…
Figure 5
Figure 5. Figure 5: A demonstration of MASR’s reflective reasoning processes to answer questions in the EgoSchema dataset. original video at 1 FPS and obtains three cluster center: the 8th, the 14th and the 38th frame. MASR directly makes these three clustered center frames as the fine-fo…
Figure 6
Figure 6. Figure 6: A demonstration of MASR’s reflective reasoning processes to answer questions in the EgoSchema [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: A demonstration of MASR’s reflective reasoning processes to answer questions in the IntentQA [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

76 extracted references · 18 canonical work pages

  1. [1]

    The llama 3 herd of models,

    Meta, “The llama 3 herd of models,” 2024, arXiv:2407.21783

    arXiv 2024

  2. [2]

    GPT-4o System Card,

    OpenAI, “GPT-4o System Card,” 2024, arXiv:2410.21276

    arXiv 2024

  3. [3]

    DeepSeek-V3 Technical Report,

    DeepSeek, “DeepSeek-V3 Technical Report,” 2025, arXiv:2412.19437v2

    arXiv 2025

  4. [4]

    Qwen2 Technical Report,

    The Qwen Team, “Qwen2 Technical Report,” 2024, arXiv:2407.10671v4

    arXiv 2024

  5. [5]

    Qwen2.5 Technical Report,

    The Qwen Team, “Qwen2.5 Technical Report,” 2024, arXiv:2412.15115v2

    arXiv 2024

  6. [6]

    Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

    The Vicuna Team, “Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.” vicuna.lmsys.org. Accessed: Apr. 27, 2025. [Online.] Available: https://vicuna.lmsys.org

    work page 2025

  7. [7]

    Gpt-4 technical report,

    OpenAI, “Gpt-4 technical report,” 2023, arXiv:2303.08774

    arXiv 2023

  8. [8]

    Llama: Open and efficient foundation language models,

    H. Touvron, et al. , “Llama: Open and efficient foundation language models,” 2023, arXiv:2302.13971

    arXiv 2023

Show all 76 references
  1. [9]

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

    Z. Chen, et al. , “Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,” in CVPR, pp. 24185–24198, 2024

    2024

  2. [10]

    Improved baselines with visual instruction tuning,

    H. Liu, C. Li, Y . Li, and Y . J. Lee, “Improved baselines with visual instruction tuning,” in CVPR, pp. 26296–26306, 2024

    2024

  3. [11]

    Llava-next: Improved reasoning, ocr, and world knowledge

    H. Liu, et al. , “Llava-next: Improved reasoning, ocr, and world knowledge.” llava-vl.github.io. Accessed: Apr. 27, 2025. [Online.] Available: https://llava- vl.github.io/blog/2024-01-30-llava-next/

    2025

  4. [12]

    Visual instruction tuning,

    H. Liu, C. Li, Q. Wu, and Y . J. Lee, “Visual instruction tuning,” in NIPS, vol. 36, pp. 34892-34916, 2024

    2024

  5. [13]

    Deepseek-vl: towards real-world vision- language understanding,

    H. Lu, et al. , “Deepseek-vl: towards real-world vision- language understanding,” 2024, arXiv:2403.05525

    2024 arXiv

  6. [15]

    Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution,

    QwenTeam, “Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution,” 2024, arXiv:2409.12191v2

    2024 arXiv

  7. [16]

    Drivevlm: The convergence of au- tonomous driving and large vision-language models,

    X. Tian, et al. , “Drivevlm: The convergence of au- tonomous driving and large vision-language models,” 2024, arXiv:2402.12289

    2024 arXiv

  8. [17]

    Minicpm-v: A gpt-4v level mllm on your phone,

    Y . Yao, et al. , “Minicpm-v: A gpt-4v level mllm on your phone,” 2024, arXiv:2408.01800

    2024 arXiv

  9. [18]

    xGen-MM (BLIP-3): A Family of Open Large Multimodal Models,

    L. Xue, et al., “xGen-MM (BLIP-3): A Family of Open Large Multimodal Models,” 2024, arXiv:2408.08872

    2024

  10. [19]

    Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,

    J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” 2023, arXiv:2301.12597

    2023 arXiv

  11. [20]

    Blip: Bootstrap- ping language-image pre-training for unified vision-language understanding and generation,

    J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrap- ping language-image pre-training for unified vision-language understanding and generation,” in ICML, vol. 162, pp. 12888–12900, 2022

    2022

  12. [21]

    Cogagent: A visual language model for gui agents,

    W. Hong, et al., “Cogagent: A visual language model for gui agents,” in CVPR, pp. 14281-14290, 2024

    2024

  13. [22]

    Medical Image Un- derstanding with Pretrained Vision Language Models: A Comprehensive Study,

    Z. Qin, H. Yi, Q. Lao, and K. Li, “Medical Image Un- derstanding with Pretrained Vision Language Models: A Comprehensive Study,” 2022, arXiv:2209.15517

    2022 arXiv

  14. [23]

    NaVILA: Legged Robot Vision-Language- Action Model for Navigation,

    A. Cheng, et al., “NaVILA: Legged Robot Vision-Language- Action Model for Navigation,” 2024, arXiv:2412.04453

    2024 arXiv

  15. [24]

    Vtimellm: Empower llm to grasp video moments,

    B. Huang, X. Wang, H. Chen, Z. Song, and W. Zhu, “Vtimellm: Empower llm to grasp video moments,” inCVPR, pp. 14271-14280, 2024

    2024

  16. [25]

    Self-Chained Image-Language Model for Video Localization and Question Answering,

    S. Yu, J. Cho, P. Yadav, and M. Bansal, “Self-Chained Image-Language Model for Video Localization and Question Answering,” in NIPS, pp. 13647-13657, 2023

    2023

  17. [26]

    An image grid can be worth a video: Zeroshot video question answering using a vlm,

    W. Kim, C. Choi, W. Lee, and W. Rhee, “An image grid can be worth a video: Zeroshot video question answering using a vlm,” 2024, arXiv:2403.18406

    2024 arXiv

  18. [27]

    Pllava: Parameter-free llava extension from images to videos for video dense captioning,

    L. Xu, Y . Zhao, D. Zhou, Z. Lin, S. K. Ng, and J. Feng, “Pllava: Parameter-free llava extension from images to videos for video dense captioning,” 2024, arXiv:2404.16994

    2024 arXiv

  19. [28]

    Moviechat: From dense token to sparse memory for long video understanding,

    E. Song, et al. , “Moviechat: From dense token to sparse memory for long video understanding,” in CVPR, pp. 18221- 18232, 2023

    2023

  20. [29]

    Video-chatgpt: Towards detailed video understanding via large vision and language models,

    S. K. M. Maaz, H. Rasheed, and F. Khan, “Video-chatgpt: Towards detailed video understanding via large vision and language models,” 2023, arXiv:2306.05424

    2023 arXiv

  21. [30]

    Video-llama: An instruction- tuned audio-visual language model for video understanding,

    H. Zhang, X. Li, and L. Bing, “Video-llama: An instruction- tuned audio-visual language model for video understanding,” 2023, arXiv:2306.02858

    2023 arXiv

  22. [31]

    VideoLLaMA 2: Advancing Spatial- Temporal Modeling and Audio Understanding in Video- LLMs,

    Z. Cheng, et al. , “VideoLLaMA 2: Advancing Spatial- Temporal Modeling and Audio Understanding in Video- LLMs,” 2023, arXiv:2406.07476v3

    2023 arXiv

  23. [32]

    Videochat: Chat-centric video understanding,

    K. Li, et al. , “Videochat: Chat-centric video understanding,” 2023, arXiv:2305.06355

    2023 arXiv

  24. [33]

    Grounded-VideoLLM: Sharpening Fine- grained Temporal Grounding in Video Large Language Mod- els,

    H. Wang, et al. , “Grounded-VideoLLM: Sharpening Fine- grained Temporal Grounding in Video Large Language Mod- els,” 2024, arXiv:2410.03290

    2024 arXiv

  25. [34]

    Chat-univi: Unified visual representation empowers large language models with image and video understanding,

    P. Jin, R. Takanobu, C. Zhang, X. Cao, and L. Yuan, “Chat-univi: Unified visual representation empowers large language models with image and video understanding,” 2023, arXiv:2311.08046v3

    2023 arXiv

  26. [35]

    VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding,

    M. Maaz, H. Rasheed, S. Khan, and F. Khan, “VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding,” 2023, arXiv:2406.09418

    2023 arXiv

  27. [36]

    MovieChat+: Question-aware Sparse Memory for Long Video Question Answering,

    E. Song, W. Chai, T. Ye, J. Hwang, X. Li and G. Wang, “MovieChat+: Question-aware Sparse Memory for Long Video Question Answering,” 2023, arXiv:2404.17176

    2023 arXiv

  28. [37]

    Videopoet: A large language model for zero-shot video generation,

    D. Kondratyuk, et al. , “Videopoet: A large language model for zero-shot video generation,” 2023, arXiv:2312.14125. 12

    2023 arXiv

  29. [38]

    Video- llava: Learning united visual representation by alignment before projection,

    B. Lin, B. Zhu, Y . Ye, M. Ning, P. Jin, and L. Yuan, “Video- llava: Learning united visual representation by alignment before projection,” 2023, arXiv:2311.10122

    2023 arXiv

  30. [39]

    LongVLM: Efficient Long Video Understanding via Large Language Models,

    Y . Weng, M. Han, H. He, X. Chang, and B. Zhualing, “LongVLM: Efficient Long Video Understanding via Large Language Models,” 2023, arXiv:2404.03384v3

    2023 arXiv

  31. [40]

    ShareGPT4Video: Improving Video Un- derstanding and Generation with Better Captions,

    L. Chen, et al. , “ShareGPT4Video: Improving Video Un- derstanding and Generation with Better Captions,” 2024, arXiv:2406.04325

    2024 arXiv

  32. [41]

    Valley: Video assistant with large language model enhanced ability,

    R. Luo, et al. , “Valley: Video assistant with large language model enhanced ability,” 2024, arXiv:2306.07207

    2024 arXiv

  33. [42]

    ViLA: Efficient Video-Language Alignment for Video Question Answering,

    X. Wang, et al., “ViLA: Efficient Video-Language Alignment for Video Question Answering,” inECCV, pp. 186-204, 2024

    2024

  34. [43]

    Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments,

    T. Xie, et al. , “Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments,” 2024, arXiv:2404.07972

    2024 arXiv

  35. [44]

    ChatVideo: A Tracklet-centric Multi- modal and Versatile Video Understanding System,

    J. Wang, et al. , “ChatVideo: A Tracklet-centric Multi- modal and Versatile Video Understanding System,” 2024, arXiv:2304.14407

    2024 arXiv

  36. [45]

    Language repository for long video understanding,

    K. Kahatapitiya, K. Ranasinghe, J. Park, and M. S. Ryoo, “Language repository for long video understanding,” 2024, arXiv:2403.14622

    2024 arXiv

  37. [46]

    Vipergpt: Visual inference via python execution for reasoning,

    D. Sur ´ıs, S. Menon, and C. V ondrick, “Vipergpt: Visual inference via python execution for reasoning,” in ICCV, pp. 11888–11898, 2023

    2023

  38. [47]

    Zero-shot video question answering with procedural pro- grams,

    R. Choudhury, K. Niinuma, K. M. Kitani, and L. A. Jeni, “Zero-shot video question answering with procedural pro- grams,” 2023, arXiv:2312.00937

    2023 arXiv

  39. [48]

    LifelongMemory: Lever- aging LLMs for answering queries in long-form egocentric videos,

    Y . Wang, Y . Yang, and M. Ren, “LifelongMemory: Lever- aging LLMs for answering queries in long-form egocentric videos,” 2024, arXiv:2312.05269v3

    2024 arXiv

  40. [49]

    DrVideo: Document Retrieval Based Long Video Understanding,

    Z. Ma, et al. , “DrVideo: Document Retrieval Based Long Video Understanding,” 2024, arXiv:2406.12846

    2024 arXiv

  41. [50]

    VCA: Video Curious Agent for Long Video Understanding,

    Z. Yang, D. Chen, X. Yu, M. Shen, and C. Gan, “VCA: Video Curious Agent for Long Video Understanding,” 2025, arXiv:2412.10471v2

    2025 arXiv

  42. [51]

    Understanding Long Videos with Multimodal Language Models,

    K. Ranasinghe, X. Li, K. Kahatapitiya, and M. S. Ryoo, “Understanding Long Videos with Multimodal Language Models,” 2025, arXiv:2403.16998v4

    2025 arXiv

  43. [52]

    Dorae- mongpt: Toward understanding dynamic scenes with large language models,

    Z. Yang, G. Chen, X. Li, W. Wang, and Y . Yang, “Dorae- mongpt: Toward understanding dynamic scenes with large language models,” 2024, arXiv:2401.08392

    2024 arXiv

  44. [53]

    A Simple LLM Framework for Long-Range Video Question-Answering,

    C. Zhang, et al., “A Simple LLM Framework for Long-Range Video Question-Answering,” 2024, arXiv:2312.17235v3

    2024 arXiv

  45. [54]

    Too many frames, not all useful: Efficient strategies for long-form video qa,

    J. Park, K. Ranasinghe, K. Kahatapitiya, W. Ryoo, D. Kim, and M. S. Ryoo, “Too many frames, not all useful: Efficient strategies for long-form video qa,” 2024, arXiv:2406.09396

    2024

  46. [55]

    MM-VID: Advancing Video Understanding with GPT-4V(ision),

    K. Lin, et al. , “MM-VID: Advancing Video Understanding with GPT-4V(ision),” 2023, arXiv:2310.19773

    2023 arXiv

  47. [56]

    MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning,

    C. Zhang, et al., “MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning,” in CVPR, pp. 13647- 13657, 2024

    2024

  48. [57]

    Videoagent: A memory-augmented multi- modal agent for video understanding,

    Y . Fan, et al. , “Videoagent: A memory-augmented multi- modal agent for video understanding,” in ECCV, pp. 75-92, 2025

    2025

  49. [58]

    VideoA- gent: Long-Form Video Understanding with Large Language Model as Agent,

    X. Wang, Y . Zhang, O. Zohar, and S. Yeung-Levy, “VideoA- gent: Long-Form Video Understanding with Large Language Model as Agent,” in ECCV, pp. 58-76, 2024

    2024

  50. [59]

    VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos,

    Z. Wang, et al. , “VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos,” 2025, arXiv:2405.19209v3

    2025 arXiv

  51. [60]

    VideoINSTA: Zero-shot Long Video Under- standing via Informative Spatial-Temporal Reasoning with LLMs,

    R. Liao, et al., “VideoINSTA: Zero-shot Long Video Under- standing via Informative Spatial-Temporal Reasoning with LLMs,” 2024, arXiv:2409.20365v2

    2024 arXiv

  52. [61]

    Longagent: Scaling language models to 128k context through multi-agent collaboration,

    J. Zhao, et al. , “Longagent: Scaling language models to 128k context through multi-agent collaboration,” 2024, arXiv:2402.11550

    2024 arXiv

  53. [62]

    Learning video representations from large language models,

    Y . Zhao, I. Misra, P. Kr ¨ahenb¨uhl, and R. Girdhar, “Learning video representations from large language models,” in CVPR, pp. 6586–6597, 2023

    2023

  54. [63]

    Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond,

    J. Bai, et al. , “Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond,” 2023, arXiv:2308.12966

    2023 arXiv

  55. [64]

    EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding,

    K. Mangalam, R. Akshulakov, and J. Malik, “EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding,” 2023, arXiv:2308.09126

    2023 arXiv

  56. [65]

    NExT-QA:Next Phase of Question-Answering to Explaining Temporal Ac- tions,

    J. Xiao, X. Shang, A. Yao and T. Chua, “NExT-QA:Next Phase of Question-Answering to Explaining Temporal Ac- tions,” 2023, arXiv:2105.08276

    2023 arXiv

  57. [66]

    IntentQA: Context-aware Video Intent Reasoning,

    J. Li, P. Wei, W. Han, and L. Fan, “IntentQA: Context-aware Video Intent Reasoning,” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 11963-11974, 2023

    2023

  58. [68]

    Understanding Long Videos via LLM-Powered Entity Relation Graphs. arXiv preprint arXiv:2405.21075,

    M. Chu, Y . Li, and T. Chua, “Understanding Long Videos via LLM-Powered Entity Relation Graphs. arXiv preprint arXiv:2405.21075,” 2024, arXiv:2501.15953

    2024 arXiv

  59. [69]

    EV A-CLIP-18B: Scaling clip to 18 billion parameters,

    Q. Sun, et al. , “EV A-CLIP-18B: Scaling clip to 18 billion parameters,” 2024, arXiv:2402.04252

    2024 arXiv

  60. [70]

    Mistral 7b,

    A. Q. Jiang, et al. , “Mistral 7b,” 2023, arXiv:2310.06825

    2023 arXiv

  61. [71]

    Mixtral of experts,

    A. Q. Jiang, et al. , “Mixtral of experts,” 2023, arXiv:2401.04088

    2023 arXiv

  62. [72]

    Reproducible scaling laws for contrastive language-image learning,

    M. Cherti, et al. , “Reproducible scaling laws for contrastive language-image learning,” 2024, arXiv:2212.07143v2

    2024 arXiv

  63. [73]

    Vamos: Versatile action models for video understand- ing,

    S. Wang, Q. Zhao, M. Q. Do, N. Agarwal, K. Lee, and C. Sun, “Vamos: Versatile action models for video understand- ing,” 2024, arXiv:2311.13627v3

    2024 arXiv

  64. [74]

    Claude-3.5-sonnet,

    Anthropic, “Claude-3.5-sonnet,” www.anthropic.com. Accessed: Apr. 28, 2025. [Online.] Available: https://www.anthropic.com/news/claude-3-5-sonnet

    2025

  65. [75]

    Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,

    Google, “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,” 2023, arXiv:2403.05530v5

    2023 arXiv

  66. [76]

    Long Context Transfer from Language to Vision,

    P. Zhang, et al. , “Long Context Transfer from Language to Vision,” 2023, arXiv:2406.16852v2

    2023 arXiv

  67. [77]

    InternVL2 Team, ”Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling,” 2024, arXiv:2412.05271

    2024 arXiv

  68. [78]

    LLaV A-OneVision: Easy Visual Task Transfer,

    B. Li, et al., “LLaV A-OneVision: Easy Visual Task Transfer,” 2024, arXiv:2408.03326v3

    2024 arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.