arxiv: 2604.20937 · v1 · submitted 2026-04-22 · 💻 cs.LG

Recognition: unknown

Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs

Kibum Kim , Jiwan Kim , Kyle Min , Yueqi Wang , Jinyoung Moon , Julian McAuley , Chanyoung Park

Authors on Pith no claims yet

Pith reviewed 2026-05-10 00:40 UTC · model grok-4.3

classification 💻 cs.LG

keywords sink tokensvisual token pruningVideo LLMsfine-grained video understandinghallucination evaluationtoken efficiencyattention attraction

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The pith

Sink tokens that attract too much attention cause standard pruning to fail on precise video tasks, but suppressing them with a new score restores performance even at 90% token reduction.

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

Video LLMs rely on many visual tokens that slow down inference, so pruning methods remove most of them to speed things up. These methods perform well on simple question answering but lose accuracy badly on tasks that require careful attention to specific visual details. Analysis shows this happens because uninformative sink tokens survive pruning and distort how the model uses visual information. The proposed method adds a sink score to existing pruning techniques to identify and remove these problematic tokens instead. This leads to much better results on challenging benchmarks while still achieving large reductions in token count.

Core claim

Sink tokens are semantically uninformative visual tokens that attract excessive attention in Video LLMs, and their survival during pruning distorts visual evidence leading to poor performance on fine-grained tasks. Sink-Token-aware Pruning (SToP) introduces a sink score to quantify this behavior and applies it to suppress sink tokens within spatial and temporal pruning methods. This plug-and-play approach improves performance of existing methods on hallucination evaluation, open-ended generation, compositional reasoning, and MCQA, maintaining effectiveness even when pruning up to 90% of visual tokens.

What carries the argument

A sink score that measures each visual token's tendency to attract disproportionate attention and is used to suppress such tokens during pruning.

If this is right

Existing pruning methods like VisionZip, FastVid, and Holitom gain substantial performance boosts on fine-grained tasks when combined with sink suppression.
Pruning ratios up to 90% become viable for Video LLMs without the usual collapse in detailed understanding capabilities.
The method works across diverse benchmarks including hallucination detection and compositional reasoning.
Attention distortion from sink tokens is mitigated, allowing better visual grounding in generated responses.

Where Pith is reading between the lines

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

This suggests that attention patterns in multimodal models may have systematic biases that pruning can correct without retraining.
Similar sink token phenomena could be investigated in other domains like audio or text-only long context models.
Designing pruning strategies that prioritize sink identification from the start might yield even higher efficiency gains.
Longer video sequences could be handled efficiently if token reduction is made reliable for fine details.

Load-bearing premise

That the performance collapse on fine-grained tasks is primarily driven by the presence of sink tokens rather than by the loss of other important visual information during pruning.

What would settle it

If experiments show that suppressing tokens identified as sinks does not improve or even harms performance on fine-grained benchmarks compared to standard pruning, this would indicate the sink score does not address the root issue.

Figures

Figures reproduced from arXiv: 2604.20937 by Chanyoung Park, Jinyoung Moon, Jiwan Kim, Julian McAuley, Kibum Kim, Kyle Min, Yueqi Wang.

**Figure 1.** Figure 1: (a) Overview of temporal and spatial pruning. (b) Performance drop rate relative to the vanilla model for temporal+spatial pruning and spatial-only pruning methods on the MCQA (MVBench [23]) and hallucination benchmark (EventHallusion [50]). compression during training, these approaches often demand computationally expensive retraining. To circumvent this, many recent studies [14, 35, 47, 52] adopt train… view at source ↗

**Figure 2.** Figure 2: (a) Performance degradation upon the removal of temporal pruning. (b) Comparison of selected visual token distributions between temporal+spatial pruning and the variant without temporal pruning as a retention ratio of 0.1. We use the EventHallusion [50] dataset for this analysis. where Q and K are the query and key of the visual tokens, respectively. Following [10, 34, 47], a token’s attention score is … view at source ↗

**Figure 3.** Figure 3: Visualization of attention scores across consecutive frames. To characterize these high-frequency tokens identified in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Performance degradation rate across varying sink token removal ratios. To verify whether the frequent selection of sink tokens directly hinders fine-grained video understanding and exacerbates hallucination, we perform a diagnostic experiment using VisionZip [47], a spatial-only pruning method that is relatively prone to hallucination (see [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Overall Framework. Building on our analysis in Sec. 3, we propose SToP, a plug-andplay token pruning method designed to explicitly guide the pruning of sink tokens. We begin by defining a sink score to quantify how likely each token is to behave as a sink token (Sec. 4.1). This score is then seamlessly incorporated into existing spatial and temporal frameworks [14,34,35,47] via two proposed modules: … view at source ↗

**Figure 6.** Figure 6: Performance over the different number of frames. Different Number of Frames. To investigate how performance varies with the different number of frames (16, 32, 64), we evaluate VisionZip and VisionZip+SToP on the EventHallusion dataset. For each setting, we report the overall performance and the performance drop rate compared to the corresponding vanilla model. As illustrated in [PITH_FULL_IMAGE:figure… view at source ↗

**Figure 7.** Figure 7: Performance with naive approaches. Comparison with Naive Approaches. To better understand the underlying mechanics of SToP beyond the main results, we compare it against two naive approaches applied to VisionZip on the EventHallusion dataset. For these approaches, we first identify tokens with the top K% highest attention weights per frame, which often behave as sink tokens (see Sec. 3.2). We then apply… view at source ↗

**Figure 8.** Figure 8: Performance degradation rate across varying sink token removal ratios. In Sec. 3.3 of the main paper, we demonstrated that removing sink tokens from the selected token set—while replacing them with tokens of the next highest attention scores—reduces hallucination. Here, we provide a more systematic ablation to further isolate the inherent impact of sink tokens on fine-grained video understanding. Setup.… view at source ↗

**Figure 9.** Figure 9: Performance degradation rate across varying sink token removal ratios. In Eq. (4) of the main paper, the hyperparameter w controls the sharpness of the sink score distribution. Specifically, raising sˆi to the power w before min-max normalization amplifies the contrast between high-and low-sink scores when w > 1, and diminishes it when w < 1. As shown in [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗

**Figure 10.** Figure 10: (a) Hyperparameter sensitivity of µs and (b) w using VisionZip [47] under retention ratio of 10% on the EventHallusion [50] dataset. paper, and evaluate on EventHallusion and VideoComp at retention ratios of 10% and 15%. As shown in Tab. 6, we observe that applying SToP to FlashVid consistently improves over the baseline, demonstrating that SToP is broadly applicable beyond the three primary baselines (V… view at source ↗

**Figure 11.** Figure 11: Hyperparameter sensitivity of µt. Sensitivity to µt. As shown in [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗

read the original abstract

Video Large Language Models (Video LLMs) incur high inference latency due to a large number of visual tokens provided to LLMs. To address this, training-free visual token pruning has emerged as a solution to reduce computational costs; however, existing methods are primarily validated on Multiple-Choice Question Answering (MCQA) benchmarks, where coarse-grained cues often suffice. In this work, we reveal that these methods suffer a sharp performance collapse on fine-grained understanding tasks requiring precise visual grounding, such as hallucination evaluation. To explore this gap, we conduct a systematic analysis and identify sink tokens--semantically uninformative tokens that attract excessive attention--as a key obstacle to fine-grained video understanding. When these sink tokens survive pruning, they distort the model's visual evidence and hinder fine-grained understanding. Motivated by these insights, we propose Sink-Token-aware Pruning (SToP), a simple yet effective plug-and-play method that introduces a sink score to quantify each token's tendency to behave as a sink and applies this score to existing spatial and temporal pruning methods to suppress them, thereby enhancing video understanding. To validate the effectiveness of SToP, we apply it to state-of-the-art pruning methods (VisionZip, FastVid, and Holitom) and evaluate it across diverse benchmarks covering hallucination, open-ended generation, compositional reasoning, and MCQA. Our results demonstrate that SToP significantly boosts performance, even when pruning up to 90% of visual tokens.

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

SToP adds a sink score to existing pruning methods and recovers performance on fine-grained video tasks at up to 90% token reduction, with consistent empirical gains but limited methodological transparency.

read the letter

The main takeaway is that standard visual token pruning works fine for coarse MCQA but collapses on tasks needing precise grounding, and this paper pins the problem on sink tokens that attract too much attention. They introduce a sink score to downweight those tokens inside spatial and temporal pruning pipelines, then plug it into VisionZip, FastVid, and Holitom. The result is better scores on hallucination evaluation, open-ended generation, compositional reasoning, and MCQA while still pruning heavily. That combination of diagnosis plus a lightweight fix is the useful part. The analysis of why sinks hurt fine-grained understanding looks systematic, and the gains hold across multiple base methods and benchmarks, which gives the claim some weight. The approach stays additive rather than forcing a full redesign, so it is easy to try on top of prior work. On the softer side, the abstract gives no exact formula for the sink score, no error bars, and no full protocol for how tokens were labeled or excluded. Without those, it is hard to judge how sensitive the improvements are to implementation choices or whether the score might suppress useful tokens in other settings. The central empirical pattern still stands on the reported results, but the details matter for reproducibility. This is aimed at researchers working on efficient Video LLMs for applications that require detailed visual evidence rather than just high-level answers. A reader already following token pruning or multimodal efficiency papers would find the targeted fix and the benchmark spread worth looking at. I would send it for peer review because the motivation is concrete, the method is simple to implement, and the gains are shown on relevant tasks even if more statistical and implementation detail would help.

Referee Report

3 major / 2 minor

Summary. The paper claims that standard training-free visual token pruning methods for Video LLMs suffer sharp performance drops on fine-grained tasks (e.g., hallucination evaluation) because semantically uninformative 'sink tokens' that attract excessive attention survive pruning and distort visual evidence. It introduces Sink-Token-aware Pruning (SToP), a plug-and-play extension that computes a sink score for each token and applies it to suppress sinks within existing spatial/temporal pruners (VisionZip, FastVid, Holitom). Experiments across hallucination, open-ended generation, compositional reasoning, and MCQA benchmarks reportedly show consistent gains even at 90% pruning ratios.

Significance. If the empirical gains prove robust, SToP would offer a lightweight, training-free way to improve fine-grained video understanding in efficient Video LLMs. The plug-and-play design and consistent application across multiple base pruners are practical strengths; the identification of sink tokens as a distinct failure mode on fine-grained tasks (distinct from coarse MCQA) could inform future pruning research.

major comments (3)

[§3 (Method)] The precise definition and computation of the sink score (central to SToP) are not provided with sufficient mathematical detail or pseudocode; without this, it is impossible to verify whether the score reliably isolates attention sinks without discarding task-critical visual information or introducing new biases.
[§4 (Experiments)] Results lack error bars, standard deviations, or statistics from multiple random seeds/runs; this makes it difficult to judge whether the reported boosts on fine-grained benchmarks (especially at 90% pruning) are statistically reliable or sensitive to implementation choices.
[§2 (Analysis)] The protocol for identifying sink tokens during the systematic analysis (data exclusion rules, attention threshold, video sampling) is not fully specified, weakening the causal claim that surviving sink tokens are the primary driver of the observed performance collapse on fine-grained tasks.

minor comments (2)

[Tables/Figures] Table captions and figure legends should explicitly list the exact pruning ratios and base methods used in each row/column for quick reference.
[Abstract] The abstract mentions 'diverse benchmarks' but does not name the specific datasets (e.g., which hallucination benchmark); adding these would improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have carefully reviewed each major comment and provide point-by-point responses below. We commit to revisions that will improve the clarity, rigor, and reproducibility of the work without altering its core contributions.

read point-by-point responses

Referee: [§3 (Method)] The precise definition and computation of the sink score (central to SToP) are not provided with sufficient mathematical detail or pseudocode; without this, it is impossible to verify whether the score reliably isolates attention sinks without discarding task-critical visual information or introducing new biases.

Authors: We agree that the sink score requires more explicit mathematical formulation and implementation details for full reproducibility and verification. In the revised manuscript, we will expand Section 3 with the precise definition of the sink score (computed from normalized attention weights aggregated across layers and heads), the exact formula, and pseudocode illustrating its integration into existing spatial and temporal pruners. Our experiments applying SToP to VisionZip, FastVid, and Holitom demonstrate consistent gains on fine-grained tasks at high pruning ratios, supporting that the score targets uninformative sinks while preserving task-critical information; the added details will allow readers to confirm this directly. revision: yes
Referee: [§4 (Experiments)] Results lack error bars, standard deviations, or statistics from multiple random seeds/runs; this makes it difficult to judge whether the reported boosts on fine-grained benchmarks (especially at 90% pruning) are statistically reliable or sensitive to implementation choices.

Authors: We acknowledge that the lack of error bars and multi-seed statistics limits assessment of result reliability. In the revised version, we will rerun the key experiments (across hallucination, reasoning, and MCQA benchmarks) with multiple random seeds, report means accompanied by standard deviations in the tables, and include a brief discussion of observed variance, with particular attention to the 90% pruning regime. This will strengthen confidence in the reported improvements. revision: yes
Referee: [§2 (Analysis)] The protocol for identifying sink tokens during the systematic analysis (data exclusion rules, attention threshold, video sampling) is not fully specified, weakening the causal claim that surviving sink tokens are the primary driver of the observed performance collapse on fine-grained tasks.

Authors: We agree that the analysis protocol in Section 2 would benefit from fuller specification to support the causal interpretation. In the revision, we will explicitly detail the video sampling procedure, attention threshold for sink identification, and any data exclusion rules applied during the systematic study. These additions will clarify the methodology and reinforce the link between surviving sink tokens and degraded fine-grained performance. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper identifies sink tokens via systematic analysis of existing pruning methods' failures on fine-grained tasks, then defines a sink score as an additive modifier applied to independent prior pruners (VisionZip, FastVid, Holitom). No equations, predictions, or uniqueness theorems are presented that reduce the performance claims to fitted parameters, self-definitions, or self-citation chains. Validation rests on external benchmarks and empirical gains up to 90% pruning, which are falsifiable outside the method's own construction. The approach is self-contained with no load-bearing internal reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

Based solely on the abstract; no explicit free parameters, background axioms, or independent evidence for new entities are stated.

invented entities (2)

sink tokens no independent evidence
purpose: semantically uninformative tokens that attract excessive attention and distort visual evidence
Identified via analysis as the key obstacle to fine-grained understanding; no external falsifiable handle provided.
sink score no independent evidence
purpose: quantify each token's tendency to behave as a sink for use in pruning
New metric introduced to modify existing pruning methods; no independent validation or derivation details given.

pith-pipeline@v0.9.0 · 5592 in / 1238 out tokens · 33554 ms · 2026-05-10T00:40:28.447792+00:00 · methodology

discussion (0)

Reference graph

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