arxiv: 2605.03276 · v2 · submitted 2026-05-05 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing

Andong Deng , Dawei Du , Zhenfang Chen , Wen Zhong , Fan Chen , Guang Chen , Chia-Wen Kuo , Longyin Wen

show 2 more authors

Chen Chen Sijie Zhu

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:40 UTC · model grok-4.3

classification 💻 cs.CV

keywords video editing benchmarklarge multimodal modelsediting techniquesmultimodal reasoningoperation simulationvideo understandingcinematic techniques

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The pith

A new benchmark reveals large gaps in how well large multimodal models handle real-world video editing.

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

This paper creates VEBench to test large multimodal models on real video editing skills. The benchmark uses over 3,900 edited videos and more than 3,000 question-answer pairs to check two skills: spotting seven different editing techniques and simulating edits by picking the right clips at the right times from several options. Tests on many models show they do much worse than people, which matters because it points out what AI needs to learn to help with actual video production work. The results suggest models must connect basic video understanding to practical decision-making for editing tasks.

Core claim

VEBENCH is introduced as the first benchmark to evaluate both editing knowledge understanding and operational reasoning in realistic video editing scenarios. It includes 3.9K high-quality edited videos totaling over 257 hours and 3,080 human-verified QA pairs constructed via a three-round human-AI collaborative pipeline. The two tasks are Video Editing Technique Recognition, which tests identification of seven editing techniques via multimodal cues, and Video Editing Operation Simulation, which requires selecting and temporally localizing relevant clips from multiple candidates to model editing workflows. Experiments across proprietary and open-source models demonstrate a large gap to human-

What carries the argument

VEBench benchmark with its two complementary QA tasks: technique recognition using multimodal cues for seven editing methods and operation simulation that requires selecting and temporally localizing clips from multiple candidate videos to model real editing workflows.

Load-bearing premise

The three-round human-AI collaborative annotation pipeline produces QA pairs that accurately reflect real-world video editing demands without introducing systematic bias.

What would settle it

A model achieving or exceeding human performance levels across all 3,080 QA pairs on both the technique recognition and operation simulation tasks would disprove the claimed performance gap.

Figures

Figures reproduced from arXiv: 2605.03276 by Andong Deng, Chen Chen, Chia-Wen Kuo, Dawei Du, Fan Chen, Guang Chen, Longyin Wen, Sijie Zhu, Wen Zhong, Zhenfang Chen.

**Figure 1.** Figure 1: Examples of the two tasks in VEBENCH. Video Editing Technique Recognition (top) detects the editing technique at a specified position; Video Editing Operation Simulation (bottom) first selects the most suitable footage from a candidate list and then localizes the proper segment based on a reference video. Correct answers are indicated in green. Abstract Real-world video editing demands not only expert know… view at source ↗

**Figure 2.** Figure 2: Video Editing Techniques Illustrations. The red arrows view at source ↗

**Figure 3.** Figure 3: Annotation pipelines of the two VEBENCH subtasks. (a) TechRec: GPT-4o provides YouTube video search queries and Gemini2.5-Pro filter video candidates; human annotators inspect the videos and finalize the editing technique labels. (b) OpSim: Gemini-2.5-Pro analyzes the selected high-quality edited videos and generate metadata; human annotators perform A/B Roll paring and refine timestamps; human annotators… view at source ↗

**Figure 4.** Figure 4: Distributions of video duration and editing techniques in view at source ↗

**Figure 5.** Figure 5: Examples of two different visual prompts in the stitched view at source ↗

**Figure 6.** Figure 6: Qualitative example of Gemini-2.5-Pro [7], Qwen3-VL-8B-Instruct [28], and InternVL3-8B [42]. 0 500 1000 1500 2000 2500 3000 3500 Start End Gemini-2.5-Pro 0 5 10 15 20 25 30 Start End Qwen3-VL-8B-Instruct 0 20 40 60 80 100 120 140 160 Timestamp (s) Start End InternVL3-8B view at source ↗

**Figure 7.** Figure 7: Predicted timestamp distributions in OpSim for Gemini view at source ↗

**Figure 8.** Figure 8: The prompt (P1) used for video analysis in VEB view at source ↗

**Figure 9.** Figure 9: The prompt (P2) used for video analysis in VEB view at source ↗

**Figure 10.** Figure 10: Operation simulation metadata example. 16 view at source ↗

**Figure 11.** Figure 11: Qualitative example of Gemini-2.5-Pro [7], Qwen3-VL-8B-Instruct [28], and InternVL3-8B [42]. 18 view at source ↗

**Figure 12.** Figure 12: Qualitative example of Gemini-2.5-Pro [7], Qwen3-VL-8B-Instruct [28], and InternVL3-8B [42]. 19 view at source ↗

read the original abstract

Real-world video editing demands not only expert knowledge of cinematic techniques but also multimodal reasoning to select, align, and combine footage into coherent narratives. While recent Large Multimodal Models (LMMs) have shown remarkable progress in general video understanding, their abilities in multi-video reasoning and operational editing workflows remain largely unexplored. We introduce VEBENCH, the first comprehensive benchmark designed to evaluate both editing knowledge understanding and operational reasoning in realistic video editing scenarios. VEBENCH contains 3.9K high-quality edited videos (over 257 hours) and 3,080 human-verified QA pairs, built through a three-round human-AI collaborative annotation pipeline that ensures precise temporal labeling and semantic consistency. It features two complementary QA tasks: 1) Video Editing Technique Recognition, assessing models' ability to identify 7 editing techniques using multimodal cues; and 2) Video Editing Operation Simulation, modeling real-world editing workflows by requiring the selection and temporal localization of relevant clips from multiple candidates. Extensive experiments across proprietary (e.g., Gemini-2.5-Pro) and open-source LMMs reveal a large gap between current model performance and human-level editing cognition. These results highlight the urgent need for bridging video understanding with creative operational reasoning. We envision VEBENCH as a foundation for advancing intelligent video editing systems and driving future research on complex reasoning.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note private letter to a colleague

VEBench is a reasonable first benchmark for video editing reasoning in LMMs, but the annotation pipeline needs quantitative checks before the performance gaps can be trusted.

read the letter

VEBench introduces two tasks that make sense together: recognizing one of seven editing techniques from multimodal cues and then simulating real edits by choosing and timing clips from multiple candidates. That combination is new compared to prior video benchmarks that mostly stop at captioning or action recognition. The dataset scale is decent too, with 3.9K edited videos and 3,080 human-verified QA pairs built from over 257 hours of footage. Testing both proprietary models like Gemini-2.5-Pro and open-source ones, then showing a gap to human performance, gives a concrete baseline that researchers can use to track progress on operational video reasoning. The human-AI annotation pipeline is described as three rounds for precision and consistency, which is a reasonable approach on paper. The soft spot is exactly where the stress-test note points: no inter-annotator agreement numbers, no error rates, and no ablation on whether the AI suggestions introduced consistent biases in temporal boundaries or technique labels. Without those, the reported gaps could partly reflect dataset artifacts rather than pure model shortcomings. The choice of the seven techniques also lacks visible justification for coverage or balance. This paper is aimed at people building or evaluating multimodal models for creative video work. A reader who needs a yardstick for editing-specific reasoning would find the tasks and numbers useful even if the validation details are thin. It deserves peer review because the core setup is grounded and the scale is there; a referee can push for the missing agreement stats and technique rationale without the whole thing falling apart.

Referee Report

2 major / 2 minor

Summary. The paper introduces VEBENCH, a benchmark comprising 3.9K high-quality edited videos (257+ hours) and 3,080 human-verified QA pairs, constructed via a three-round human-AI collaborative annotation pipeline. It defines two tasks—Video Editing Technique Recognition (identifying 7 techniques via multimodal cues) and Video Editing Operation Simulation (selecting and temporally localizing clips from candidates)—and reports extensive experiments on proprietary and open-source LMMs showing a substantial performance gap relative to human-level editing cognition.

Significance. If the benchmark's annotations prove free of systematic bias, VEBENCH would be a valuable contribution as the first large-scale resource specifically targeting the intersection of video editing knowledge and operational multimodal reasoning. The scale, dual-task design, and human verification pipeline are clear strengths that could catalyze progress on creative video workflows beyond standard understanding benchmarks.

major comments (2)

[Section 3] Section 3 (Dataset Construction and Annotation Pipeline): The three-round human-AI pipeline is presented as ensuring 'precise temporal labeling and semantic consistency,' yet the manuscript provides no inter-annotator agreement statistics, error-rate analysis, or ablation comparing AI-assisted vs. human-only annotations. This is load-bearing for the central claim of a genuine gap, as unquantified bias in technique labeling or clip selection could artifactually inflate model shortfalls.
[Section 4.2] Section 4.2 (Video Editing Operation Simulation Results): The reported performance gap is stated without accompanying statistical significance tests, confidence intervals, or per-technique breakdowns that would confirm the gap is robust rather than driven by a subset of the 7 techniques or particular video categories.

minor comments (2)

[Abstract and Section 3] The abstract and introduction refer to '3.9K high-quality edited videos' while the methods section should explicitly reconcile this with the 3,080 QA pairs (e.g., average pairs per video) for clarity.
[Figure 1] Figure 1 (benchmark overview) would benefit from explicit callouts to the two QA task formats and the 7 editing techniques to improve immediate readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on VEBench. The comments highlight important aspects of annotation reliability and experimental rigor. We address each major comment point by point below, indicating planned revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Section 3] Section 3 (Dataset Construction and Annotation Pipeline): The three-round human-AI pipeline is presented as ensuring 'precise temporal labeling and semantic consistency,' yet the manuscript provides no inter-annotator agreement statistics, error-rate analysis, or ablation comparing AI-assisted vs. human-only annotations. This is load-bearing for the central claim of a genuine gap, as unquantified bias in technique labeling or clip selection could artifactually inflate model shortfalls.

Authors: We acknowledge that the current manuscript does not report inter-annotator agreement (IAA) statistics, a dedicated error-rate analysis, or an ablation of AI-assisted versus human-only annotation. The three-round pipeline relies on AI for initial proposals followed by multi-round human verification to enforce consistency, but quantitative validation of this process was omitted. In revision, we will add IAA metrics (e.g., Fleiss' kappa) computed on a sampled subset of the 3,080 QA pairs from the final human verification round. We will also include an error analysis summarizing the most frequent discrepancy types resolved by humans. A full ablation study comparing AI-only versus the full pipeline is not feasible within current resources, but we will expand the pipeline description with qualitative evidence of bias reduction. These changes will better support the annotation quality underlying the reported model-human gap. revision: partial
Referee: [Section 4.2] Section 4.2 (Video Editing Operation Simulation Results): The reported performance gap is stated without accompanying statistical significance tests, confidence intervals, or per-technique breakdowns that would confirm the gap is robust rather than driven by a subset of the 7 techniques or particular video categories.

Authors: We agree that the absence of statistical tests and breakdowns limits the strength of the claims in Section 4.2. In the revised manuscript, we will add bootstrap-based 95% confidence intervals and paired statistical significance tests (with multiple-comparison correction) for all model-versus-human comparisons on both tasks. We will also include per-technique performance tables and breakdowns by video category (e.g., duration, editing complexity) to demonstrate that the performance gap holds consistently across the seven techniques rather than being driven by outliers. These additions will be integrated into the results section and associated tables/figures. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark with independent human-verified labels

full rationale

The paper presents VEBENCH as a new dataset and evaluation suite constructed via a three-round human-AI annotation pipeline, followed by direct measurement of LMM performance on two QA tasks against those human-verified labels. No equations, fitted parameters, derivations, or self-referential definitions appear in the provided text. Reported gaps are observed performance differences, not quantities defined in terms of the models' own outputs or prior self-citations. The annotation pipeline is a methodological choice whose bias properties are external to the reported numbers; it does not reduce the benchmark results to tautology or self-definition. This is a standard empirical benchmark paper whose central claims rest on held-out measurements rather than any of the enumerated circular patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that the chosen 7 editing techniques and the human-verified QA pairs faithfully represent real-world video editing cognition; no free parameters or invented entities are introduced.

axioms (2)

domain assumption The 7 editing techniques cover the key multimodal cues needed for realistic video editing assessment.
Stated as the basis for the Video Editing Technique Recognition task in the abstract.
domain assumption The three-round human-AI annotation pipeline produces temporally precise and semantically consistent labels.
Invoked to justify the quality of the 3,080 QA pairs.

pith-pipeline@v0.9.0 · 5571 in / 1439 out tokens · 26038 ms · 2026-05-12T01:40:15.404352+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Lean theorems connected to this paper

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

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce VEBENCH... two complementary QA tasks: Video Editing Technique Recognition... Video Editing Operation Simulation... three-round human-AI collaborative annotation pipeline
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Metrics... tIoUcond... Extensive experiments across proprietary and open-source LMMs

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high", "medium

**Editing Quality Assessment ** - Evaluate the overall editing quality of the interview video. - Rate it as one of: "high", "medium", or "low". - Consider transitions, pacing, timing to music/speech, and use of overlays. - Provide a detailed justification in "editing_justification"

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Yes"/"No

**Dataset Suitability Decision ** - Decide whether this video is suitable for inclusion in a multimodal dataset focused on edited cinematic clips. - Must include meaningful edits, cinematic framing (16:9), and exclude shorts or raw footage. - Output "Yes"/"No" in "suitable_for_dataset" and explain in "suitability_justification"

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**Interviewee Identification ** - Identify or infer the interviewee’s name from context or transcript

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timestamp_start

**Timestamp-Based Scene Breakdown ** For each scene, return: - "timestamp_start": e.g., "0:24" - "timestamp_end": e.g., "0:31" - "scene_description": visual content summary - "source_movie": name or None - "type": "A-Roll", "B-Roll", "Intro Graphic", etc. - "b_roll_purpose": narrative function (if B-Roll) - "broll_uniqueness": "Highly unique" or "Highly r...

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