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REVIEW 3 major objections 4 minor 47 references

Evaluating Multimodal Large Language Models on Video Captioning via Monte Carlo Tree Search

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read An automatic MCTS framework, AutoCaption, builds a fine-grained video captioning benchmark with 122.3 key points per video and uses the same generated data to fine-tune MLLMs.

desk verdict MCTS-based video caption benchmark with a solid core and a ground-truth audit hole that needs closing before the rankings can be trusted. read the letter →

arxiv 2506.11155 v1 pith:GDXKQ4C5 submitted 2025-06-11 cs.CV

Linhao Yu , Xinguang Ji , Yahui Liu , Fanheng Kong , Chenxi Sun , Jingyuan Zhang , Hongzhi Zhang , V. W.
show 2 more authors
Fuzheng Zhang Deyi Xiong
This is my paper
classification cs.CV
keywords videocaptioningmultimodallargelanguagemodelsMonteCarloTreeSearchbenchmarkconstructionkeypointverificationsynthetictrainingdatafine-grainedevaluationMLLMfine-tuning
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 argues that a search-based automatic pipeline can replace manual annotation in building video captioning benchmarks, and that the same pipeline can generate training data that improves the models being evaluated. It introduces AutoCaption, which uses Monte Carlo Tree Search to iteratively explore six kinds of video description and verify each new detail before keeping it. The resulting benchmark, MCTS-VCB, covers 1,765 videos with an average of 122.3 key points each, split into five content dimensions. Across 21 open and closed multimodal models, Gemini-1.5-Pro scores highest, and fine-tuning InternVL2.5-8B on AutoCaption-generated data improves its F1 score by 25.0% on MCTS-VCB and 16.3% on DREAM-1K. If the framework holds up, costly manual key-point annotation can be replaced by a cheap automatic process that is also a source of training signal.

What carries the argument

The load-bearing object is the MCTS search tree over six description actions, with each node scored by the state value $Q(s,a)=0.5^{1-MC(s)}\cdot 0.5^{SM(s)}$, where $MC(s)$ is the fraction of the node's atomic key points that pass verification by two multimodal models answering yes/no questions, and $SM(s)$ is the average similarity of the node to earlier nodes on its path. High correctness and low redundancy give a node a high value, so the search keeps expanding into new video details. Post-processing filters, deduplicates at a similarity threshold of 0.8, paraphrases, and human-checks the surviving key points to form the final benchmark.

What would settle it

Take a random sample of the final key points produced at the official 0.8 similarity threshold, have independent human annotators judge each one against the original video, and compare the pass rate with the reported 94.7% (measured at the stricter 0.9 threshold); a large drop, or a systematic disagreement between the two verifier models, would show that the ground truth and the model rankings built on it are not anchored to reality.

Watch

Extended reading notes

Core claim

The central claim is that Monte Carlo Tree Search, a planning method from game AI, can be repurposed to enumerate the many details of a video that a good caption should mention. AutoCaption treats captioning as a search tree in which six actions (overall, detail, temporal, spatial, background, and camera-movement description) generate candidate sentences, each sentence is decomposed into atomic key points, and each key point must pass a two-model yes/no verification before it contributes to the node's score. A similarity penalty suppresses repetition, and backpropagation drives the search toward unexplored details. The verified key points become a benchmark that ranks 21 models, and the same search output, converted into thought-process-guided captions, serves as fine-tuning data that lifts a mid-size model on both the new benchmark and the existing DREAM-1K benchmark.

Load-bearing premise

The whole framework rests on the assumption that two multimodal models' yes/no answers about whether a generated detail actually appears in the video are reliable enough to serve as ground truth, and that the small human-checked subset is representative of the final key point set.

Editorial extensions

If this is right

  • Manual key-point annotation can be replaced by an automatic MCTS search, lowering the cost of building fine-grained captioning benchmarks.
  • MCTS-VCB separates 21 MLLMs by five key-point dimensions and ten video genres, exposing category-specific weaknesses, with Gemini-1.5-Pro leading at an overall F1 of 71.2.
  • AutoCaption-generated data, without human annotation, improves InternVL2.5-8B by 25.0% on MCTS-VCB and 16.3% on DREAM-1K.
  • The fine-tuned model also improves on out-of-domain benchmarks such as Video-MME and MMBench-Video, suggesting the synthetic captions teach transferable video description skill.
  • On samples of MCTS-VCB, the benchmark's scoring correlates with human judgment more closely than existing metrics such as CLAIR, AutoDQ, BertScore, and ROUGE.

Reading between the lines

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

  • The same tree-search coverage mechanism could transfer to other structured perception tasks where detail coverage is the bottleneck, such as dense image captioning, spatial grounding, or long-video question answering.
  • Because the reward signal comes from two MLLM verifiers, the benchmark's granularity is capped by what those verifiers can perceive; a detail that both verifiers systematically miss would be excluded from the ground truth.
  • A natural next experiment is a self-improvement loop: fine-tune the small generator on AutoCaption data, rerun the search with the improved generator, and measure whether key points become denser and more accurate per iteration.
  • The reported human verification rate of 94.7% was collected at a stricter 0.9 similarity threshold while all evaluation results use 0.8, so an independent check of the 0.8-threshold key point set would clarify whether the final ground truth is as clean as the human-checked subset.
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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

3 major / 4 minor

Summary. The paper introduces AutoCaption, an automated framework that uses Monte Carlo Tree Search (MCTS) to iteratively generate fine-grained descriptive key points from videos, and applies it to build MCTS-VCB, a video captioning benchmark containing 1,765 videos with on average 122.3 key points per video. The authors evaluate 21 open- and closed-source MLLMs on MCTS-VCB, report human-consistency correlations for a subset of models and videos, and show that fine-tuning InternVL2.5-8B on AutoCaption-generated data improves its F1 by 25.0% on MCTS-VCB and 16.3% on DREAM-1K, surpassing fine-tuning directly on GPT-4o-generated captions.

Significance. If the claimed results hold, MCTS-VCB would provide a much denser and more diverse key-point resource than prior video captioning benchmarks such as DREAM-1K, and AutoCaption would offer a scalable and relatively low-cost way to build both evaluation benchmarks and training data. The paper is also valuable for releasing code and data, and for reporting human-consistency correlations that go beyond simple n-gram metrics. However, the benchmark's validity depends critically on the reliability of the automated key-point verification pipeline and on the consistency between the human-checked data and the data actually used in the experiments; these points are currently not established in the manuscript.

major comments (3)
  1. [Appendix A.4 and Appendix B.3] The human-check pass rate of 94.7% is reported for key points generated under a similarity threshold of 0.9, yet all experimental results, including Tables 1, 2, 4 and Figure 4, are stated in Appendix B.3 to be based on a similarity threshold of 0.8. The paper never demonstrates that the 0.8-threshold key points used in the evaluation are identical to, or directly derived from, the 0.9-threshold set that annotators checked. If the 0.8 set was not human-verified, its only quality filter is the automated two-MLLM yes/no verification, and ground-truth errors could distort every model ranking and the fine-tuning gains reported in Section 6.2. The authors should either human-check a random sample of the final 0.8-threshold key points, or explicitly prove that the human-checked 0.9 set covers the 0.8 set, and report the verification results for the actual evaluation set.
  2. [Section 4.1 and Section 6.1 / Table 1] The verification pipeline is potentially circular: GPT-4o and Qwen2-VL-72B are used as the two verifiers that admit key points into MCTS-VCB, and both models are themselves evaluated on MCTS-VCB (Table 1). In addition, Gemini-1.5-Pro generates the initial Overall Description that seeds the search tree, and it is also evaluated and ranks first overall. This creates a concrete correctness risk: if a verifier systematically over-accepts statements that match its own captioning tendencies, the benchmark ground truth becomes biased toward that verifier's style, inflating its score and potentially affecting rankings of the other models. I am not claiming deliberate bias, but the paper should address this by reporting per-verifier pass rates and disagreement statistics, and by showing that rankings remain stable after removing key points that are admitted by only one of the two verifiers or when using a third, unevaluated verifier.
  3. [Section 4.1 vs. Appendix A.3] There is a direct inconsistency in the description of the key-point extraction step. Section 4.1 states that key points are extracted from a node by Qwen2-VL-72B under the prompt in Figure 13, while Appendix A.3 states that GPT-4 is used to extract key points from the node's action results with the same Figure 13 prompt. This matters because the same extraction prompt is later used in Section 6.1 to obtain KP_model from model-generated captions. Without knowing which model actually extracted key points during benchmark construction, the pipeline cannot be fully audited, and the potential for Qwen-family bias in the benchmark cannot be assessed. The authors should unify the description and clearly specify which model was used at each stage.
minor comments (4)
  1. [Section 6.1] In the paragraph 'Performance on different frame numbers', the text refers to 'Figure 3' when reporting the frame-number results; the relevant results appear in Table 3, not in Figure 3 (which illustrates key-point verification). Please correct the cross-reference.
  2. [Throughout] There are numerous typographical and grammatical errors, including 'tranditional', 'excuating', 'MMLM', 'cantains', and 'defination'. A thorough language edit would improve readability.
  3. [Section 4.1, Eq. (2)] The values of alpha and beta in Q(s,a) are set to 0.5 based on the claim that MC(s) and SM(s) are 'same important', but no sensitivity analysis is provided for these weighting parameters or for the PUCT constant c. Since these hyperparameters affect which nodes are expanded, a small robustness study would strengthen the claim that the benchmark is stable to design choices.
  4. [Appendix A.3] The appendix section heading is 'A.3 MCTS Evaluation Phase Details', but the first sentence of the final paragraph says 'Ultimately, our method improves upon Method 2...' without numbering the proposed method as Method 3. Minor renumbering would make the comparison clearer.

Circularity Check

1 steps flagged · score 4.0 of 10

MCTS-VCB's reference key points are partly generated and verified by GPT-4o and Gemini-1.5-Pro, the same models the benchmark then ranks; the reported human check covers only the 0.9-threshold set while all experiments use 0.8, so the rankings embed a construction-level circularity.

  1. self definitional [Section 4.1 Expansion/Evaluation; Section 6.1 Table 1; Appendix A.4; Appendix B.3]
    "To give MCTS iteration a good initialization, the Overall Description action is executed by GPT-4o (OpenAI, 2024) and Gemini-1.5-Pro (Team et al., 2024). ... we utilize GPT-4 ... to construct a 'yes/no' question ... and then use two verifiers (i.e., GPT-4o and Qwen2-VL-72B) to answer them. If all yes/no questions derived from a key point are shown to be 'yes' for both verifiers, we consider the key point passing the verification. ... we let annotators check the key points generated under a similarity threshold of 0.9 ... with a human verification pass rate of 94.7%. ..."

    The reference key points (KPref) are constructed by a pipeline in which GPT-4o and Gemini-1.5-Pro generate the root Overall Description and GPT-4o is a required verifier for every key point; the same two models are then ranked against KPref in Table 1. The human check that would break the self-reference is reported only for the 0.9-threshold set, while Appendix B.3 states that all experimental results use threshold 0.8. Thus the final benchmark used for rankings and fine-tuning comparisons is, for the unevaluated threshold, defined by automated GPT-4o-inclusive verification. Consequently GPT-4o and Gemini-1.5-Pro scores partly measure agreement with text those models themselves produced or approved, not an independent ground truth.

full rationale

The core MCTS search, key-point extraction, verification, and post-processing equations (Q(s,a), MC(s), SM(s), and the evaluation metric) are not themselves circular: the search value is computed from verifier responses and similarity, and the benchmark scores are computed from an external judge (Qwen2.5-72B-Instruct) against the reference key points. The central circularity risk is benchmark self-reference: GPT-4o and Gemini-1.5-Pro are used to seed and verify the very key points against which their video captioning is scored. This is aggravated by the threshold mismatch: the reported 94.7% human verification pass rate applies to the 0.9-similarity set, while all experiments use the 0.8 set, so the final reference is not shown to be human-checked. The paper does contain independent evidence: the fine-tuning gain on the external DREAM-1K benchmark (16.3%) and the human-consistency correlation study do not reduce to the AutoCaption construction. No load-bearing self-citation chain appears; citations to the authors' prior work are confined to related work. Overall, the benchmark's absolute rankings and the 25.0% in-domain MCTS-VCB fine-tuning gain are partially contaminated by construction, but the central claims retain independent support, so the circularity score is moderate rather than extreme.

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

The framework relies on several hand-set hyperparameters and on the assumption that LLM-based verifiers and extractors are accurate. No new physical or mathematical entities are introduced.

free parameters (4)
  • Alpha and Beta in Q(s,a) = 0.5 each
    Hand-set to balance MC and SM in the node value function (Section 4.1).
  • PUCT exploration constant c = 0.125
    Taken from Luo et al. (2024), not tuned in this paper (Section 4.1).
  • Number of MCTS iterations = 25
    Chosen because more iterations produce more duplicated content (Section 5).
  • Deduplication similarity threshold = 0.8
    Selected for final benchmark; results for 0.7 to 0.9 are explored in Appendix B.3.
assumptions (4)
  • domain assumption The two-MLLM verification process (GPT-4o and Qwen2-VL-72B) reliably determines whether a generated key point is present in the video.
    The benchmark ground truth relies on this verification to filter correct key points (Section 4.1).
  • domain assumption Qwen2-VL-72B accurately extracts atomic key points from both generated descriptions and model captions.
    Used for both node evaluation and final evaluation metric (Sections 4.1 and 6).
  • ad hoc to paper The six predefined action types (Overall, Detail, Temporal, Spatial, Background, Camera) comprehensively cover video content for captions.
    The action set is designed by the authors and assumed to ensure breadth and depth (Section 4.1).
  • domain assumption Qwen2.5-72B-Instruct's entailment judgments align with human judgments.
    Used as the judge in the evaluation metric; validated only on a 100-video subset (Section 6.1).

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Cite this review

Pith. "Pith review of Evaluating Multimodal Large Language Models on Video Captioning via Monte Carlo Tree Search." pith.science (2026). https://pith.science/paper/GDXKQ4C5

@misc{pith2026250611155,
  author       = {Pith},
  title        = {Pith review of: Evaluating Multimodal Large Language Models on Video Captioning via Monte Carlo Tree Search},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GDXKQ4C5}},
  note         = {Machine review of arXiv:2506.11155}
}
read the original abstract

Video captioning can be used to assess the video understanding capabilities of Multimodal Large Language Models (MLLMs). However, existing benchmarks and evaluation protocols suffer from crucial issues, such as inadequate or homogeneous creation of key points, exorbitant cost of data creation, and limited evaluation scopes. To address these issues, we propose an automatic framework, named AutoCaption, which leverages Monte Carlo Tree Search (MCTS) to construct numerous and diverse descriptive sentences (\textit{i.e.}, key points) that thoroughly represent video content in an iterative way. This iterative captioning strategy enables the continuous enhancement of video details such as actions, objects' attributes, environment details, etc. We apply AutoCaption to curate MCTS-VCB, a fine-grained video caption benchmark covering video details, thereby enabling a comprehensive evaluation of MLLMs on the video captioning task. We evaluate more than 20 open- and closed-source MLLMs of varying sizes on MCTS-VCB. Results show that MCTS-VCB can effectively and comprehensively evaluate the video captioning capability, with Gemini-1.5-Pro achieving the highest F1 score of 71.2. Interestingly, we fine-tune InternVL2.5-8B with the AutoCaption-generated data, which helps the model achieve an overall improvement of 25.0% on MCTS-VCB and 16.3% on DREAM-1K, further demonstrating the effectiveness of AutoCaption. The code and data are available at https://github.com/tjunlp-lab/MCTS-VCB.

Figures

Figures reproduced from arXiv: 2506.11155 by the authors.

Figure 1
Figure 1. Comparison of MCTS-VCB against DREAM￾1K with InternVL2.5-8B (Chen et al., 2024b) after fine￾tuning on caption data generated by AutoCaption. capabilities of MLLMs across various video cate￾gories (Wang et al., 2024d; Li et al., 2024b; Hong et al., 2025). Previous video captioning benchmarks (Wang et al., 2024a; Chen and Dolan, 2011; Xu et al., 2016; Wang et al., 2019) commonly involve cre￾ating key points (i.e., des… view at source ↗
Figure 2
Figure 2. The overall workflow of AutoCaption, illustrating the key points generation process from a video. The [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Illustration of the key point verification pro [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (17 more)
Figure 4
Figure 4. Figure 4: F1 score across different video categories. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Illustration of “Key-Info as Answer” verifica [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Video counts and average key point counts [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Video length distribution in seconds. points may exist, and the subtle differences con￾tained within these semantically similar key points could be a weakness of MLLMs. Therefore, when forming MCTS-VCB, we also analyzed data with different similarity thresholds. We eva…
Figure 8
Figure 8. Figure 8: Video counts across different deduplicate sim [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: The distribution of key points of a video. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Illustration of key points categories. InternVL2_5-8B InternVL2_5-26B InternVL2_5-38B InternVL2_5-78B LLaVa_OV_Qwen2_7B LLaVa_OV_Qwen2_72B LLaVa_Video_Qwen2_7B LLaVa_Video_Qwen2_72B MiniCPM-V-2_6 PLLaVA-7B PLLaVA-13B PLLaVA-34B Qwen2-VL-7B Qwen2-VL-72B Tarsier-7B Tars…
Figure 11
Figure 11. Figure 11: F1 score across different similarity threshold. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: The prompt used for GPT-4o to filter video clips. [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]
Figure 13
Figure 13. Figure 13: The prompt used for Qwen2-VL-72B to extract key points [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]
Figure 14
Figure 14. Figure 14: The prompt used for Qwen2.5-72B-Instruct to determine the relationship between [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: The prompt used for GPT-4 to paraphase key points. [PITH_FULL_IMAGE:figures/full_fig_p022_15.png]
Figure 16
Figure 16. Figure 16: The prompt used for Qwen2-VL-72B to generate verification questions of a key point. [PITH_FULL_IMAGE:figures/full_fig_p022_16.png]
Figure 17
Figure 17. Figure 17: The prompt used for both GPT-4o and Qwen2-VL-72B to answer verification questions of a key point. [PITH_FULL_IMAGE:figures/full_fig_p023_17.png]
Figure 18
Figure 18. Figure 18: The prompt used for GPT-4 to filter too subjective and broad key points. [PITH_FULL_IMAGE:figures/full_fig_p024_18.png]
Figure 19
Figure 19. Figure 19: The prompt used for GPT-4 to generate thought process according to video key points. [PITH_FULL_IMAGE:figures/full_fig_p025_19.png]
Figure 20
Figure 20. Figure 20: An example of prompting GPT-4o to generate more detailed caption based on thought process. [PITH_FULL_IMAGE:figures/full_fig_p026_20.png]

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Reference graph

Works this paper leans on

47 extracted references · 41 canonical work pages

  1. [1]

    The Verifier we used is Qwen2-VL-72B

    Paraphrase + Multi-V erify: We initially cre- ate three paraphrased versions of each key point extracted from the video and employed a Verifier to ascertain if each paraphrased point is indeed present in the video. The Verifier we used is Qwen2-VL-72B. However, during our experiments, it becomes evident that the Verifier has issues with misclassification....

    work page

  2. [2]

    video caption

    Key-Info as Answer : In this approach, we first use GPT-4 to identify key information and then formulate three questions with the key information as the answer. We then use GPT-4o to answer these three questions, con- sidering the key point correct if at least two out of the three answers matched the key infor- mation. However, most of the generated ques-...

    work page

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    The defination and examples of each category is described in Figure 10

    Environment Description, 5) Object Descrip- tion, and 6) Camera Movement and Composition. The defination and examples of each category is described in Figure 10. Additionally, Figure 9 shows the distribution of different keypoint categories in an average video. It can be observed that Environment Description keypoint type is the most prevalent among the k...

    work page

  4. [4]

    As shown in Figure 6, the Nature and Wildlife category has the highest number of videos, reaching 382, while the Arts and Creativity category has the fewest, with 57 videos

    Fashion and Beauty, 5) Home and Living, 6) Travel and Adventure, 7) Nature and Wildlife, 8) Technology and Science, 9) Arts and Creativity, and 10) Food and Drink. As shown in Figure 6, the Nature and Wildlife category has the highest number of videos, reaching 382, while the Arts and Creativity category has the fewest, with 57 videos. Additionally, the a...

    work page

  5. [5]

    For clarity, consider these examples: ## Example 1 ### Key Points {1

    Historical/Cultural: Key points that relate to the historical background or cultural context of the video. For clarity, consider these examples: ## Example 1 ### Key Points {1. These people could be tourists or travelers visiting the site.} {...} ### Output

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    Advances in Neural Information Processing Systems (NeurIPS)

    Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems (NeurIPS). Linhao Yu, Yongqi Leng, Yufei Huang, Shang Wu, Haixin Liu, Xinmeng Ji, Jiahui Zhao, Jinwang Song, Tingting Cui, Xiaoqing Cheng, Liutao Liutao, and Deyi Xiong. 2024. Cmoraleval: A moral evaluation benchmark for chinese large...

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    Appearance Description, 2) Action Description,

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  8. [13]

    Elaborate on the visual and narrative elements of the video in detail

    For VILA series, we use the prompt displayed on GitHub 4: “Elaborate on the visual and narrative elements of the video in detail.”

    work page

Show all 47 references
  1. [14]

    Reply to me with a precise yet detailed re- sponse

    For Pllava series, we use the prompt shown on official script 5: “You are to assist me in accomplishing a task about the input video. Reply to me with a precise yet detailed re- sponse. For how you would succeed in the recaptioning task, read the following Instruc- tions secti...

  2. [15]

    Avoid providing over detailed information such as color, counts of any objects as you are terrible regarding observing these details \n

  3. [16]

    If you are not sure about something, do not include it in you response.\n # Task\n Describe the background, characters and the actions in the provided video.\n”

    Instead, you should carefully go over the provided video and reason about key infor- mation about the overall video \n 3. If you are not sure about something, do not include it in you response.\n # Task\n Describe the background, characters and the actions in the provided video.\n”

  4. [17]

    Please describe the video in detail

    For all other MLLMs, we use “Please describe the video in detail.” B.3 Supplementary Experiment Results As shown in Figure 11, it is interesting to note that as the similarity threshold increases, the eval- uation scores of the models also increase. This phenomenon is reasonab...

  5. [18]

    A young boy is wearing a white shirt

  6. [19]

    Action Description Action description focuses on the specific behavior or activity that takes place in the video

    This girl possesses curly hair. Action Description Action description focuses on the specific behavior or activity that takes place in the video. Task states associated with actions can also fall into this category

  7. [20]

    The young boy returns the flask to the table

  8. [21]

    Environment DescriptionEnvironment description covers the background and environmental features of the scene in the video

    The actions of the young boy are careful and well- measured. Environment DescriptionEnvironment description covers the background and environmental features of the scene in the video

  9. [22]

    The background is kept tidy and orderly

  10. [23]

    Object Description Describes the features, characteristics, or details of inanimate objects or items present in the scene

    The room fosters an educational and orderly atmosphere. Object Description Describes the features, characteristics, or details of inanimate objects or items present in the scene

  11. [24]

    The artwork includes various colors like blue, red, and yellow

  12. [25]

    Camera Movement Describes the camera angles, movements, framing, or other cinematographic techniques used in the scene

    The glassware items include beakers, test tubes, and flasks. Camera Movement Describes the camera angles, movements, framing, or other cinematographic techniques used in the scene

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    Overall Description

    The camera is fixed, providing a consistent and clear view of the activity. Example Video Frames KP Category Explanation Figure 10: Illustration of key points categories. InternVL2_5-8BInternVL2_5-26BInternVL2_5-38BInternVL2_5-78B LLaVa_OV_Qwen2_7BLLaVa_OV_Qwen2_72BLLaVa_Video...

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    The video must have a clear subject (e.g., person, animal, object, etc.)

  15. [28]

    The video should not contain a lot of special effects

  16. [29]

    The video cannot contain long still clips

  17. [30]

    Judgment: [yes/no] Reason: [Brief explanation]

    The video must not be confused, unclear meaning. If the video meets all the criteria, output [yes]; if it fails to meet any of the criteria, output [no]. The input format is: “Judgment: [yes/no] Reason: [Brief explanation]” Please provide your judgment and briefly explain the ...

  18. [31]

    Make sure to substitute pronouns in split individual sentences by the nouns they refer to

  19. [32]

    For clarity, consider these examples: ## Example 1 ### Video Description: The video showcases

    If a individual sentence contains uncertain expressions, these expressions should not be included as key points. For clarity, consider these examples: ## Example 1 ### Video Description: The video showcases ... ### Result: >{Atomic key point} >{Atomic key point} >... {two more...

  20. [33]

    entailment

    “entailment ” means that the breakdown point is accurately reflected within one or more of the human-generated key points

  21. [34]

    contradiction

    “contradiction ” means that breakdown point some detail in the breakdown point contradicts with the infomation mentioned human-generated key points

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    neutral

    “neutral ” means that the relationship is neither “entailment ” nor “contradiction ”. For each breakdown point, provide a brief analysis explaining the reasoning behind your judgment. Please present the result in a JSON dict format: {“ breakdown_point_1”: {“ judgement”: judgem...

  23. [36]

    Subjective: Key points that rely heavily on personal feelings or interpretations

  24. [37]

    Minor: Key points that are overly detailed and do not add significant value to the overall description

  25. [38]

    General: Key points that are too broad and do not provide specific information about the video

  26. [39]

    Speculative: Key points that express guesses or assumptions rather than concrete information

  27. [41]

    [No] Speculative

    “[No] Speculative.” ... {two more examples} With these examples in mind, please help me filter out key points that are too subjective, too minor, too general, or express speculation. ### Key Points {kep points} ### Output Figure 18: The prompt used for GPT-4 to filter too subj...

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    </thought> tags

    Your thought process should be enclosed within<thought> ... </thought> tags

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    Overall Description

    Your thought should contain the provided “Overall Description” and all (Observation,Key Point) pairs

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    ## Example Input and Output: ### Input: Overall Description: {overall_description} Observation: Vehicles Key Point: There is no existence of any vehicles in the video

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Pith tools

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