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arxiv: 2606.29445 · v1 · pith:FHWNFW5Gnew · submitted 2026-06-28 · 💻 cs.CV · cs.AI

Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction

Sunqi Fan , Qingle Liu , Runqi Yin , Meng-Hao Guo , Shuojin Yang This is my paper

Pith reviewed 2026-06-30 07:45 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords keyframe extractionVideoQAGUI agentsmultimodal large language modelsvideo-guided tasksbenchmark
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The pith

A keyframe extraction method that weighs task relevance against scene changes improves results on both VideoQA and video-guided GUI agent tasks.

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

The paper creates VG-GUIBench to check whether multimodal models can watch a video tutorial and then carry out the matching GUI actions. It notes that success on both ordinary VideoQA and these longer agent tasks hinges on picking the right frames rather than using every frame or poor selections. The authors introduce TASKER to choose keyframes by combining what the task asks for with how the visual scene is evolving. Experiments show this approach raises scores above prior methods on EgoSchema and NExT-QA while also supporting the new benchmark. The work positions generalized keyframe search as a practical bridge between perception benchmarks and procedural skill transfer.

Core claim

TASKER is a keyframe extraction algorithm that jointly considers task relevance and scene dynamics to identify informative frames, producing measurable gains on VideoQA datasets and on the introduced VG-GUIBench for video-guided GUI agents.

What carries the argument

TASKER (Task-driven And Scene-aware Keyframe searchER), the algorithm that selects keyframes by balancing task relevance and scene dynamics.

If this is right

  • TASKER raises accuracy 2.0 percent above the strongest baseline on the EgoSchema full set.
  • TASKER raises accuracy 1.8 percent above the strongest baseline on the NExT-QA dataset.
  • Effective keyframe extraction matters for both short VideoQA questions and longer video-guided GUI tasks.
  • VG-GUIBench supplies a concrete way to measure whether models can translate video tutorials into GUI actions.

Where Pith is reading between the lines

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

  • The same selection logic could be tested on robotic manipulation videos to see whether task-aware frames help agents learn physical procedures.
  • If the method scales, real-time systems might process only a few dozen frames instead of entire videos and still retain most performance.
  • Extending VG-GUIBench beyond desktop interfaces would clarify whether the keyframe principle applies to other long-horizon agent settings.

Load-bearing premise

The performance of models on both VideoQA and video-guided agentic tasks critically depends on effective keyframe extraction.

What would settle it

A test in which models given every video frame or randomly chosen frames match or exceed TASKER's accuracy on EgoSchema, NExT-QA, and VG-GUIBench would undermine the claim.

Figures

Figures reproduced from arXiv: 2606.29445 by Meng-Hao Guo, Qingle Liu, Runqi Yin, Shuojin Yang, Sunqi Fan.

Figure 1
Figure 1. Figure 1: Demonstration of the 2 progressive levels. This work aims to advance video un￾derstanding from the VideoQA paradigm (low-level understanding) toward the Video￾Guided Agentic Task paradigm (high-level understanding). procedural skills from videos and generalize them to solve new tasks that require long-horizon agentic capabilities? This limitation becomes particularly evident in real-world learning scenario… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the VG-GUI-Bench benchmark, including benchmark pipeline, action space, metrics and formulas. Data Source We build upon the high-quality dataset provided by MON￾DAY [21], from which we obtain input tutorial videos, ground-truth action se￾quences, and keyframe screenshots as evaluation references. We further design task-specific prompts to guide the model in generating predicted actions at each … view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of TASKER’s cost function evaluation and node expansion steps. TASKER-GBFS variant evaluates distance based on question relevance. TASKER￾Dijkstra variant evaluates distance based on scene dynamics. overview of the tree-structured keyframe search process. We also explain how the algorithm utilizes the retrieved information to answer questions. The key steps of TASKER (leveraging MLLMs to evalu… view at source ↗
Figure 4
Figure 4. Figure 4: Demonstration of TASKER’s high frame efficiency. When processing the same number of video frames with the same (M)LLM, TASKER achieves higher QA accuracy. Additionally, as shown in [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of tree-search and nodes expansion process of TASKER method solving a VideoQA case from EgoSchema [34]. introduce VG-GUI-Bench, a benchmark that pairs tutorial videos with cor￾responding GUI interaction episodes to evaluate whether MLLM-based agents can extract procedural knowledge from videos and transfer it to long-horizon decision making. Building on the shared bottleneck of temporal conte… view at source ↗
Figure 6
Figure 6. Figure 6: A detailed demonstration of a test case from the VG-GUI-Bench benchmark. The example presents a multi-step task (i.e., saving emails as PDF on an iOS device), displaying the current GUI frame at each step alongside previous actions and the ref￾erence keyframes selected by our TASKER algorithm. It also visualizes the evaluation process by comparing the model’s predicted actions (Pred, including type and arg… view at source ↗
read the original abstract

Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks. However, existing benchmarks primarily evaluate whether models can perceive shallow visual cues, while rarely examining whether MLLMs can learn deeper knowledge or procedural skills from video tutorials and generalize them to downstream long-horizon agentic tasks. To address this gap, we introduce VG-GUIBench (Video-Guided GUI Benchmark), a new benchmark designed to evaluate whether MLLM-based GUI agents can follow video tutorials to complete corresponding GUI interactive tasks. Furthermore, we observe that the performance of models on both VideoQA and video-guided agentic tasks critically depends on effective keyframe extraction. Based on this observation, we propose TASKER (Task-driven And Scene-aware Keyframe searchER), a keyframe extraction algorithm that jointly considers task relevance and scene dynamics to identify informative frames. Experimental results demonstrate that TASKER achieves significant performance improvements on both VideoQA and video-guided agentic task benchmarks, outperforming the best baseline by 2.0% on the EgoSchema fullset and 1.8% on the NExT-QA dataset, respectively. These results further highlight the potential of generalized keyframe extraction methods for video understanding tasks. Our code and data are available at https://github.com/VG-GUI-TASKER/VG-GUI-TASKER.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 2 minor

Summary. The manuscript introduces VG-GUIBench, a benchmark for assessing whether MLLM-based GUI agents can follow video tutorials to perform interactive tasks. It proposes TASKER, a keyframe extraction algorithm that jointly optimizes for task relevance and scene dynamics, and reports that this method yields performance gains on VideoQA datasets (EgoSchema fullset +2.0%, NExT-QA +1.8% over the best baseline) while also improving results on the new agentic benchmark, thereby bridging the two task families via generalized keyframe selection. Code and data are released.

Significance. If the empirical claims hold, the work supplies a concrete mechanism (task-driven, scene-aware keyframe search) that demonstrably lifts both perception-oriented VideoQA and procedural agentic tasks, together with a new evaluation resource. The public release of code and data strengthens the contribution by supporting direct replication and extension.

major comments (1)
  1. [Abstract] Abstract: the central claim that TASKER delivers improvements 'on both VideoQA and video-guided agentic task benchmarks' is only partially quantified. Concrete deltas are supplied solely for EgoSchema (+2.0%) and NExT-QA (+1.8%); no accuracy, success rate, or baseline comparison is stated for VG-GUIBench. Because the bridging thesis rests on gains in the agentic setting, the absence of these numbers is load-bearing.
minor comments (2)
  1. [Abstract] The abstract states that model performance 'critically depends on effective keyframe extraction' yet supplies no supporting ablation or correlation analysis in the provided text; a brief quantitative justification for this premise would strengthen the motivation.
  2. [Experimental Results] Experimental methodology details (number of runs, statistical tests, exact TASKER hyperparameters, and VG-GUIBench construction protocol) are referenced only at a high level; these should be expanded in §4 or the appendix to allow assessment of the reported percentages.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the abstract should quantify the gains on VG-GUIBench to fully support the bridging claim between VideoQA and agentic tasks.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that TASKER delivers improvements 'on both VideoQA and video-guided agentic task benchmarks' is only partially quantified. Concrete deltas are supplied solely for EgoSchema (+2.0%) and NExT-QA (+1.8%); no accuracy, success rate, or baseline comparison is stated for VG-GUIBench. Because the bridging thesis rests on gains in the agentic setting, the absence of these numbers is load-bearing.

    Authors: We agree with the observation. The full manuscript reports concrete success-rate improvements on VG-GUIBench (e.g., +X% over the strongest baseline), but these numbers were omitted from the abstract. In the revision we will insert the missing quantitative results for VG-GUIBench into the abstract so that the bridging claim is fully supported by explicit deltas on both task families. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained empirical proposal

full rationale

The paper introduces VG-GUIBench as a new benchmark and proposes TASKER as a keyframe extraction method based on an observation about performance dependence on keyframe quality. It reports concrete numerical gains only on VideoQA datasets (EgoSchema, NExT-QA) without any equations, fitted parameters renamed as predictions, self-citations that bear the central load, or reductions of claims to inputs by construction. No load-bearing step equates a result to its own definition or prior self-work; the method is presented as a joint consideration of task relevance and scene dynamics with external benchmark validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not provide sufficient technical details to identify any free parameters, axioms, or invented entities used in the TASKER algorithm or benchmark construction.

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