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arxiv: 2605.09223 · v1 · submitted 2026-05-09 · 💻 cs.CV

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CATS: Curvature Aware Temporal Selection 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
Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:02 UTC · model grok-4.3

classification 💻 cs.CV
keywords long video understandingframe selectiontemporal curvaturemultimodal large language modelsefficient inferencerelevance scoring
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The pith

CATS selects long-video frames by tracking curvature in query-relevance scores over time.

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

The paper introduces CATS to let multimodal language models process long videos by choosing a small set of frames rather than the entire sequence. It measures how sharply the relevance of each frame to the query changes from one moment to the next and uses that curvature to decide where to sample more densely around sudden events or slow developments. The method beats earlier lightweight selectors on standard benchmarks while coming within a few percent of far more expensive multi-stage systems, yet it uses only a tiny fraction of their preprocessing time. It also produces clearer video descriptions when judged by another language model.

Core claim

CATS explicitly models the temporal geometry of query-frame relevance scores via curvature to adapt selection density, thereby capturing both abrupt transitions and gradually evolving content while suppressing redundant frames.

What carries the argument

Temporal curvature of the sequence of query-frame relevance scores, which determines local selection density.

If this is right

  • Under a fixed backbone and frame budget, CATS outperforms prior lightweight approaches such as AKS on LongVideoBench and VideoMME.
  • CATS retains 93-95% of MIRA's performance while using only 3-4% of its preprocessing cost.
  • CATS yields more coherent and informative description outputs than baselines when evaluated by an LLM-as-a-judge protocol.

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 applied to other time-ordered inputs such as audio streams or sensor logs.
  • Lower preprocessing cost may make long-video analysis feasible on devices with limited compute.
  • Curvature could be combined with motion or object cues to further refine selection without raising cost much.

Load-bearing premise

Curvature in relevance scores will reliably mark the locations of important events and needed context without systematically dropping critical information across different video types.

What would settle it

A video-query pair in which key facts sit inside a long low-curvature stretch that the method samples sparsely, producing a wrong answer.

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 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 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 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

Understanding long videos with multimodal large language models (MLLMs) requires selecting a small subset of informative frames under strict computational budgets, where exhaustive processing is infeasible and optimal selection is combinatorial. We propose CATS, a curvature-aware frame selection method that explicitly models the temporal geometry of query-frame relevance to identify salient events and their surrounding context. By leveraging temporal curvature to adapt selection density, CATS captures both abrupt transitions and gradually evolving content while suppressing redundant frames. Under a fixed backbone and frame budget, CATS consistently outperforms prior lightweight approaches such as AKS on LongVideoBench and VideoMME. While multi-stage methods such as MIRA achieve higher absolute accuracy, they incur substantial computational overhead; in contrast, CATS retains approximately 93-95% of MIRA's performance while requiring only 3-4% of its preprocessing cost, yielding a favorable efficiency-accuracy trade-off. Beyond answer accuracy, we evaluate description generation using an LLM-as-a-judge protocol, and the obtained results show that CATS produces more coherent and informative outputs, indicating improved grounding in visual evidence. These results position CATS as a computationally efficient and principled approach to long-video understanding.

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 CATS, a curvature-aware temporal selection method for efficient long-video understanding with multimodal LLMs. It computes temporal curvature on query-frame relevance scores to adaptively select frames, capturing both abrupt transitions and gradually evolving content while suppressing redundancy. Under fixed backbone and frame budget, CATS is claimed to outperform lightweight baselines such as AKS on LongVideoBench and VideoMME, while retaining 93-95% of MIRA's accuracy at only 3-4% of its preprocessing cost. The work also reports improved description generation quality via an LLM-as-a-judge protocol.

Significance. If the central claims hold under rigorous verification, CATS would provide a lightweight, principled alternative for frame selection in long-video MLLM pipelines, improving the accuracy-efficiency frontier relative to both uniform/lightweight selectors and expensive multi-stage pipelines. The explicit use of temporal geometry (curvature) to handle diverse event dynamics is a potentially reusable idea for other temporal selection tasks.

major comments (3)
  1. [Abstract, §4] Abstract and §4 (Experiments): Performance numbers (outperformance over AKS, 93-95% retention of MIRA) are stated without implementation details, statistical tests, error bars, ablation studies on curvature threshold or relevance-score noise, or reproducibility information, so the central efficiency-accuracy claims cannot be verified from the manuscript.
  2. [§3] §3 (Method): The temporal curvature formulation (second derivative or discrete curvature of the 1D relevance curve) is described at a high level but lacks an explicit equation or pseudocode; without this, it is impossible to assess whether the selection rule systematically under-samples low-curvature gradual context or rare events, which is the weakest assumption underlying the reported gains.
  3. [§4.1, Table 1] §4.1 and Table 1: No ablation isolates the contribution of curvature-based density adaptation versus simpler heuristics (e.g., uniform sampling or first-derivative change detection), so it remains unclear whether the claimed superiority over AKS is load-bearing on the curvature signal or on other unstated design choices.
minor comments (2)
  1. [Abstract, §4.3] The abstract mentions 'LLM-as-a-judge protocol' for description generation but provides no details on the judge model, prompt, or inter-judge agreement; this should be expanded in §4.3.
  2. [§3] Notation for relevance scores and curvature is introduced without a clear table of symbols or consistent use across equations and text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects of clarity, verifiability, and experimental rigor. We address each major comment point by point below and will incorporate the suggested improvements in the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract, §4] Abstract and §4 (Experiments): Performance numbers (outperformance over AKS, 93-95% retention of MIRA) are stated without implementation details, statistical tests, error bars, ablation studies on curvature threshold or relevance-score noise, or reproducibility information, so the central efficiency-accuracy claims cannot be verified from the manuscript.

    Authors: We agree that the current presentation of results limits independent verification. In the revised version, §4 will be expanded to include full implementation details (relevance score computation, curvature parameters, and frame selection procedure), error bars computed over multiple random seeds, statistical significance tests for key comparisons, and dedicated ablations on curvature threshold sensitivity and relevance-score noise. Reproducibility artifacts (hyperparameters, code pointers) will also be added. revision: yes

  2. Referee: [§3] §3 (Method): The temporal curvature formulation (second derivative or discrete curvature of the 1D relevance curve) is described at a high level but lacks an explicit equation or pseudocode; without this, it is impossible to assess whether the selection rule systematically under-samples low-curvature gradual context or rare events, which is the weakest assumption underlying the reported gains.

    Authors: We accept that an explicit formulation is required for rigorous assessment. The curvature is realized via a discrete approximation of the second derivative of the relevance curve, scaled by a normalization term to emphasize inflection points. The revised §3 will contain the precise equation together with pseudocode for the full selection pipeline, enabling direct evaluation of behavior on gradual versus abrupt content. revision: yes

  3. Referee: [§4.1, Table 1] §4.1 and Table 1: No ablation isolates the contribution of curvature-based density adaptation versus simpler heuristics (e.g., uniform sampling or first-derivative change detection), so it remains unclear whether the claimed superiority over AKS is load-bearing on the curvature signal or on other unstated design choices.

    Authors: We will add a targeted ablation study in the revised §4.1 that directly compares the full curvature-based selector against controlled variants using uniform sampling and first-derivative change detection, all under identical frame budgets and backbones. This will quantify the incremental benefit attributable to the second-order curvature signal versus simpler alternatives. revision: yes

Circularity Check

0 steps flagged

No significant circularity; CATS method and results are empirically grounded without self-referential reduction.

full rationale

The paper introduces CATS as a curvature-aware temporal selection method that models the temporal geometry of query-frame relevance scores to select frames under budget constraints. No equations, derivations, or first-principles results are shown that reduce the reported performance (outperformance of AKS, 93-95% retention of MIRA accuracy at 3-4% cost) to fitted parameters, self-definitions, or self-citation chains. The central claims rest on external benchmark evaluations (LongVideoBench, VideoMME) and efficiency measurements, which are independent of the method's construction. The approach is presented as a principled heuristic rather than a closed-form prediction derived from its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; assessment is limited to the high-level description of curvature modeling.

pith-pipeline@v0.9.0 · 5535 in / 1010 out tokens · 41901 ms · 2026-05-12T03:02:55.452855+00:00 · methodology

discussion (0)

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