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arxiv: 2605.09223 · v2 · pith:ZE5KO7FXnew · submitted 2026-05-09 · 💻 cs.CV

CREST: Curvature-Regulated Event-Centric Sampling for Efficient Long-Video Understanding

Mehrajul Abadin Miraj , Abdul Mohaimen Al Radi , Shariful Islam Rayhan , Md. Tanvir Alam , Ismat Rahman , Yu Tian , Md Mosaddek Khan This is my paper

Pith reviewed 2026-06-30 22:45 UTC · model grok-4.3

classification 💻 cs.CV
keywords frame selectionlong video understandingtemporal curvaturequery-frame relevanceevent-centric samplingtraining-free methodvideo question answering
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The pith

Local curvature in query-frame relevance over time guides selection of informative frames from long videos.

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

The paper proposes a training-free method to pick frames from long videos by tracking how sharply relevance to a given query changes across time. Relevance tends to curve sharply near important events and flatten in repetitive stretches, so the approach samples more densely where curvature is high. This allocates a fixed frame budget toward decisive moments rather than wasting it on redundant segments. The result is higher accuracy than lightweight baselines on standard long-video benchmarks while using only a small fraction of the preprocessing required by heavier retrieval pipelines. A reader would care because most frames in long videos add little information, and cheap, effective selection makes downstream tasks like question answering feasible at scale.

Core claim

CREST is a training-free frame selection method grounded in the temporal geometry of query-frame relevance. It is based on the observation that relevance over time exhibits structured local variation: sharp curvature around salient events and flatter regions in redundant segments. By using local curvature to guide selection, CREST allocates a fixed frame budget more effectively across brief decisive events and slowly evolving evidence. Under a fixed backbone and frame budget, CREST achieves higher accuracy than AKS on LongVideoBench and VideoMME while retaining 93-95% of the accuracy of MIRA at only 3-4% of its preprocessing cost. On TempRel, CREST achieves a 6.88% relative improvement over

What carries the argument

Local curvature of query-frame relevance scores over time, which detects rapid changes at salient events to prioritize frame sampling within a fixed budget.

If this is right

  • Higher accuracy than AKS on LongVideoBench and VideoMME under identical backbone and frame budget.
  • Retains 93-95% of MIRA accuracy while using only 3-4% of its preprocessing cost.
  • 6.88% relative improvement over AKS on the TempRel diagnostic benchmark.
  • Selected frames produce more coherent query-conditioned descriptions according to pairwise LLM judgments (60.58% and 54.50% win rates).

Where Pith is reading between the lines

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

  • The same curvature signal could be tested on other sequential media such as long audio recordings or time-series sensor streams to select salient segments.
  • If curvature remains stable across different backbone models, the method could serve as a lightweight plug-in for existing video pipelines without retraining.
  • An experiment that varies total frame budget while measuring total integrated curvature could test whether the method adapts naturally to videos of different lengths.

Load-bearing premise

Relevance between a query and successive video frames changes in a structured way, bending sharply at key events and staying flat elsewhere.

What would settle it

Applying the same fixed frame budget to LongVideoBench or VideoMME and finding that curvature-guided selection yields no accuracy gain over uniform sampling or the AKS baseline would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.09223 by Abdul Mohaimen Al Radi, Ismat Rahman, Md Mosaddek Khan, Md. Tanvir Alam, Mehrajul Abadin Miraj, Shariful Islam Rayhan, Yu Tian.

Figure 1
Figure 1. Figure 1: Efficiency–performance trade-off of CATS compared to MIRA. CATS reduces preprocessing time by up to [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: Efficiency–performance trade-off of CREST com [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of keyframe selection strategies. Given the same query, AKS selects frames based on a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the proposed CATS framework. Given a user query and a video, frame-level relevance scores [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the CREST selection process. At each iteration, the highest-scoring frame is selected and a curvature [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the CATS selection process. Given a query and a video, frame-level relevance scores form [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: This is an example of verifiability beyond answer correctness. Both methods predict the correct answer, but [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 5
Figure 5. Figure 5: Insufficient grounding leads to hallucinated rationale and failure on follow-up queries. CREST preserves critical [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: From rationale fidelity to future-query reasoning. Although both methods produce the correct answer, [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
read the original abstract

Selecting informative frames from long videos is a combinatorial problem that existing methods address either through efficient heuristics without explicit modeling of query-conditioned temporal structure, or through multi stage retrieval pipelines with substantial preprocessing cost. We propose \textbf{CREST}, a training-free frame selection method grounded in the temporal geometry of query--frame relevance. CREST is based on the observation that relevance over time exhibits structured local variation: sharp curvature around salient events and flatter regions in redundant segments. By using local curvature to guide selection, CREST allocates a fixed frame budget more effectively across brief decisive events and slowly evolving evidence. Under a fixed backbone and frame budget, CREST achieves higher accuracy than AKS, a lightweight relevance--coverage baseline, on LongVideoBench and VideoMME, while retaining 93--95\% of the accuracy of MIRA, a stronger multi-stage retrieval pipeline, at only 3--4\% of its preprocessing cost.\footnote{Code and implementation details are included in the supplementary material and will be released publicly upon acceptance.} On TempRel, our diagnostic benchmark for temporal frame selection, CREST achieves a 6.88\% relative improvement over AKS. Pairwise LLM-as-a-judge evaluation further shows that CREST-selected frames yield more coherent frame-conditioned descriptions, with win rates of 60.58\% and 54.50\% on the two benchmarks. These results show that local temporal geometry provides a simple and efficient basis for long-video frame selection.

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

3 major / 2 minor

Summary. The paper proposes CREST, a training-free frame selection method for long-video understanding that uses local curvature of the query-frame relevance time series to allocate a fixed frame budget, prioritizing sharp changes around salient events over flatter redundant segments. It claims higher accuracy than the AKS relevance-coverage baseline on LongVideoBench and VideoMME while retaining 93-95% of MIRA's accuracy at 3-4% of its preprocessing cost, a 6.88% relative improvement over AKS on the TempRel diagnostic benchmark, and higher win rates (60.58% and 54.50%) in LLM-as-a-judge coherence evaluations.

Significance. If the central claim holds after clarification of the experimental controls, the work would demonstrate that a simple, parameter-free geometric property of relevance trajectories can improve frame selection efficiency without training or multi-stage retrieval, offering a low-cost alternative for long-video tasks. The training-free design, public code commitment, and introduction of TempRel as a diagnostic benchmark are constructive contributions.

major comments (3)
  1. [Abstract and §4 (experimental results)] The central claim that curvature drives the accuracy gains rests on the comparison to AKS, but the manuscript does not explicitly state that AKS uses identical relevance scores and the same frame budget while omitting only the curvature term (see abstract and experimental comparisons). Without this isolation, the reported deltas on LongVideoBench, VideoMME, and the 6.88% TempRel improvement could arise from differences in relevance-to-mask conversion, hyperparameters, or implementation rather than the curvature mechanism itself.
  2. [§3 (method) and §4.3 (ablations)] No ablation is described that replaces the curvature term with an alternative local-variation measure (e.g., simple variance or first-difference) while keeping all other components fixed; such a control is required to establish that the second-derivative geometry, rather than any local-variation heuristic, is responsible for the allocation improvement.
  3. [§3 (CREST algorithm)] The abstract states concrete accuracy percentages and relative improvements, yet the main text supplies neither the precise definition of the curvature operator (e.g., discrete second difference or spline-based) nor the exact selection algorithm that converts curvature values into the final frame mask under the fixed budget.
minor comments (2)
  1. [Footnote 1] The footnote promises code and implementation details in supplementary material; ensure the supplementary file contains the exact pseudocode for curvature computation and the AKS baseline re-implementation so that the isolation issue can be verified.
  2. [§4.1 (experimental setup)] Clarify whether the relevance scores fed to CREST and AKS are produced by the identical backbone and query encoder; any difference here would confound the geometry-based claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, clarifying our experimental controls and committing to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Abstract and §4 (experimental results)] The central claim that curvature drives the accuracy gains rests on the comparison to AKS, but the manuscript does not explicitly state that AKS uses identical relevance scores and the same frame budget while omitting only the curvature term (see abstract and experimental comparisons). Without this isolation, the reported deltas on LongVideoBench, VideoMME, and the 6.88% TempRel improvement could arise from differences in relevance-to-mask conversion, hyperparameters, or implementation rather than the curvature mechanism itself.

    Authors: We agree that the isolation of the curvature contribution should be stated more explicitly. The abstract and §4 already note that all methods operate under a fixed backbone and frame budget, with AKS described as the relevance-coverage baseline. In the revised version we will add explicit language in both the abstract and §4 stating that AKS receives the identical relevance scores (computed from the same query-frame similarity function) and the same budget, differing solely in the selection rule. This makes clear that the observed gains are due to the curvature term. revision: yes

  2. Referee: [§3 (method) and §4.3 (ablations)] No ablation is described that replaces the curvature term with an alternative local-variation measure (e.g., simple variance or first-difference) while keeping all other components fixed; such a control is required to establish that the second-derivative geometry, rather than any local-variation heuristic, is responsible for the allocation improvement.

    Authors: We accept that an explicit control replacing curvature with other local-variation heuristics would strengthen the argument. We will add this ablation to §4.3, substituting the curvature operator with first-difference magnitude and local variance while freezing every other component of the pipeline, and report the resulting accuracy on the same benchmarks. revision: yes

  3. Referee: [§3 (CREST algorithm)] The abstract states concrete accuracy percentages and relative improvements, yet the main text supplies neither the precise definition of the curvature operator (e.g., discrete second difference or spline-based) nor the exact selection algorithm that converts curvature values into the final frame mask under the fixed budget.

    Authors: We will expand §3 with the precise mathematical definition of the curvature operator (the discrete second-difference formula actually employed) and a step-by-step account of how curvature values are converted into the final binary mask under the fixed budget. These details will appear in the main text; the supplementary material will continue to contain the full implementation. revision: yes

Circularity Check

0 steps flagged

No circularity: method is self-contained and training-free

full rationale

The paper grounds CREST in an external observation about temporal geometry of query-frame relevance (sharp curvature around events, flatter redundant segments) and applies local curvature to allocate a fixed frame budget. No equations, fitted parameters, or self-citations appear in the provided text that would reduce any prediction or uniqueness claim to the inputs by construction. The approach is explicitly training-free, with accuracy comparisons presented as external benchmarks against AKS and MIRA rather than internal fits. This satisfies the criteria for a self-contained derivation without load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on one domain assumption about the structure of temporal relevance; no free parameters or invented entities are stated in the abstract.

axioms (1)
  • domain assumption Relevance over time exhibits structured local variation: sharp curvature around salient events and flatter regions in redundant segments.
    This observation is stated as the basis for using curvature to guide frame selection.

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Reference graph

Works this paper leans on

17 extracted references · 13 canonical work pages · 9 internal anchors

  1. [1]

    Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

    Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond.arXiv preprint arXiv:2308.12966. Yukang Chen, Fuzhao Xue, Dacheng Li, Qinghao Hu, Ligeng Zhu, Xiuyu Li, Yunhao Fang, Hao- tian Tang, Shang Yang, Zhijian Liu, and 1 oth- ers

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    LongVILA: Scaling Long-Context Visual Language Models for Long Videos

    Longvila: Scaling long-context visual language models for long videos.arXiv preprint arXiv:2408.10188. Zhe Chen, Yizhou Wang, and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

    Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.arXiv preprint arXiv:2312.14238. Cheng and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    Videomme: A com- prehensive evaluation benchmark for multi-modal large language models in video understanding.arXiv preprint arXiv:2405.21075. X. Gao and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    Rui Li, Xiaohan Wang, Yuhui Zhang, Zeyu Wang, and Serena Yeung-Levy

    Bimba: State space models for long video understanding.arXiv preprint. Rui Li, Xiaohan Wang, Yuhui Zhang, Zeyu Wang, and Serena Yeung-Levy. 2025a. Temporal preference op- timization for long-form video understanding.arXiv preprint arXiv:2501.13919. Z. Li and 1 others. 2025b. Preference-aware long video reasoning with supervised fine-tuning and direct pref...

    work page arXiv

  6. [6]

    Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

    Video-llava: Learning united visual representation by alignment before projection.arXiv preprint arXiv:2311.10122. Liu and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    Visual instruction tuning.arXiv preprint arXiv:2304.08485. X. Liu, Y . Chen, and 1 others. 2025b. Nvila: Ef- ficient frontier visual language models.Preprint, arXiv:2501.06175. OpenAI

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding

    Longvu: Spatiotemporal adaptive compression for long video-language under- standing.arXiv preprint arXiv:2410.17434. Xi Tang, Jihao Qiu, Lingxi Xie, Yunjie Tian, Jianbin Jiao, and Qixiang Ye. 2025a. Adaptive keyframe sam- pling for long video understanding.arXiv preprint arXiv:2502.21271. Z. Tang and 1 others. 2025b. Tspo: Temporal agent training via grou...

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    Longvideobench: A benchmark for long-form video understanding.arXiv preprint arXiv:2406.14514. J. Xu and 1 others. 2024a. Sf-llava: Semantic-free lunch for video large language models. Preprint. Minghao Xu and 1 others. 2024b. Pllava: Parameter- efficient large language and vision assistant for video understanding.arXiv preprint arXiv:2404.02123. Z. Xu an...

    work page internal anchor Pith review Pith/arXiv arXiv

  10. [10]

    Minicpm-v: A gpt-4v level mllm on your phone.arXiv preprint arXiv:2408.xxxxx. Jinhui Ye, Zihan Wang, Haosen Sun, Keshigeyan Chan- drasegaran, Zane Durante, Cristobal Eyzaguirre, Yonatan Bisk, Juan Carlos Niebles, Ehsan Adeli, Li Fei-Fei, Jiajun Wu, and Manling Li. 2025a. T*: Re-thinking temporal search for long-form video un- derstanding.arXiv preprint ar...

    work page arXiv

  11. [11]

    Frame-voyager: Learning to query frames for video large language models.arXiv preprint arXiv:2410.03226. Yuan

    work page arXiv

  12. [12]

    InFindings of the Association for Computational Linguistics: NAACL 2025, pages 881–916

    Lmms-eval: Reality check on the evalua- tion of large multimodal models. InFindings of the Association for Computational Linguistics: NAACL 2025, pages 881–916. Yifan Zhang, Chunyuan Li, Haotian Liu, and 1 oth- ers. 2024a. Llava-video: Large language-and-vision assistant for video understanding.arXiv preprint arXiv:2405.03715. Yuanhan Zhang, Jinming Wu, W...

    work page arXiv 2025

  13. [13]

    Minigpt-4: Enhancing vision-language understand- ing with advanced large language models.arXiv preprint arXiv:2304.10592. O. Zohar and 1 others

    work page internal anchor Pith review Pith/arXiv arXiv

  14. [14]

    Preprint

    Apollo: Training and inference guidelines for long video comprehension. Preprint. A Related Works Early MLLMs such as LLaV A (Liu et al., 2023), Qwen-VL (Bai et al., 2023), MiniGPT-4 (Zhu et al., 2023), InternVL (Chen et al., 2023), GPT-4o (Ope- nAI, 2024), and NVILA (Liu et al., 2025b) extend language models to the visual domain by encoding images as vis...

    2023

  15. [15]

    PLLaV A (Xu et al., 2024b) improves parameter efficiency, yet its performance is directly bounded by the quality of the frames it receives

    employs sparse uniform sampling, which misses temporally localized events, while LLaV A-Video (Zhang et al., 2024b) increases den- sity but remains constrained by context length. PLLaV A (Xu et al., 2024b) improves parameter efficiency, yet its performance is directly bounded by the quality of the frames it receives. Across all these methods, frame select...

    2024

  16. [16]

    These meth- ods improve accuracy but require additional data, training, and careful tuning, limiting their general- ity across deployment settings

    distills effective practices for long-range temporal understanding, and TSPO (Tang et al., 2025b) ex- plores lightweight policy adaptation. These meth- ods improve accuracy but require additional data, training, and careful tuning, limiting their general- ity across deployment settings. Training-free methods avoid these costs by op- erating entirely at in...

    2025

  17. [17]

    23-04-2018 11:06 AM

    Evaluate how additional visible elements from the same scene/domain are in- cluded that could support future ques- tions on the same topic. Score higher for: • More exhaustive coverage of other objects, steps, contextual elements, variations, or supporting details in- side the exact same scene (even if not needed for the current question). • Better readin...

    2018