KiToke: Distribution-Aware Token Compression for Video Large Language Models

arXiv:2604.03414 · cs.CV · Submitted 2026-04-03 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "KiToke: Distribution-Aware Token Compression for Video Large Language Models".

Jane: The gist: KiToke proposes a training-free, query-agnostic token compression approach that reduces spatiotemporal redundancy while preserving critical visual information for Video Large Language Models.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So, we're looking at KiToke today. It’s a paper about this training-free token compression approach for Video Large Language Models.

Jane: Right, it tackles the problem of high inference costs because videos have too many visual tokens extracted from them.

Tom: Exactly. And what they propose is this method that reduces spatiotemporal redundancy while trying to keep the important visual information intact.

Lu: It’s interesting because they frame the whole thing around estimating token diversity globally, not just locally or segment by segment, which I think is a big step forward for handling video complexity in these models >

Jane: That sounds complicated. How exactly does this global diversity estimation work in simple terms?

Tom: They use a kernel-based measure inspired by kernel density estimation to figure out the redundancy across the entire set of tokens >

Lu: So they define pairwise similarity using a Gaussian kernel to compute how dense each token is, and then the diversity score is defined as the inverse of that density, which should align with preserving unique visual information >

Meng: If it’s global, that sounds like it could catch redundancies that are separated in both space and time, which I worry about when we're trying to deploy something at scale >

Jane: That’s a valid concern. But they go on to talk about how they use this diversity score for selection.

Tom: They perform diversity-weighted sampling to keep a subset of tokens based on a retention ratio gamma, specifically keeping floor gamma N tokens via diversity-weighted sampling >

Lu: And they deliberately used stochastic sampling instead of deterministic top-K selection, which I think is smart because it helps preserve visual coverage at the group level and avoids dropping entire low-score groups when compression is aggressive >

Meng: Stochastic sampling sounds good for coverage, but what about the temporal aspect? How do they handle keeping things coherent over time?

Jane: That’s where they introduce content-adaptive temporal intervals based on token-level visual dynamics >

Tom: They use two measures here: frame-level visual difference called difft, which quantifies changes at corresponding spatial positions, and diffmatcht, which accounts for spatial displacement by matching each token in frame t minus one to its closest counterpart in frame t >

Lu: So they construct intervals based on these dynamics to maintain temporal coherence by avoiding merging tokens across semantically misaligned moments >

Jane: And then they have a system for detecting when those intervals should end.

Tom: They detect boundaries by measuring the deviation from local temporal dynamics, computing the maximum deviation delta t as max difft minus difft minus one, and relative deviation delta percent t >

Meng: Wait, so if the visual change is big in magnitude—if difft is large—or if both delta t and delta percent t show a pronounced deviation from neighboring frames—that's when they introduce a temporal boundary >

Lu: And after that, they do interval-aware token merging to prevent losing fine-grained evidence during discarding >

Title and authors: Jane: How does that merging strategy work?

Tom: Unselected tokens are aggregated into selected ones by assigning each unselected token x u in U k to its most similar retained token x r according to cosine similarity >

Lu: And then the final representation of x r is computed via diversity-weighted averaging, making sure globally distinctive tokens have a greater influence on that aggregated representation >

Meng: From a deployment standpoint, the main thing I’m looking at is the speedup. How much does this actually save us on prefill time?

Tom: They show it achieves the fastest prefill time, which is six point eight times speedup compared to baselines like PruneVID and VisionZip >

Jane: That speedup sounds significant for real-world use. But what about the accuracy when you push this compression really hard?

Tom: Experiments across multiple video understanding benchmarks and Video LLM backbones show it consistently outperforms existing training-free compression methods across a wide range of retention ratios >

Lu: They specifically note that KiToke maintains strong performance even under extreme token budgets, showing consistent gains down to a one percent retention ratio where other methods suffer significant degradation >

Meng: That robustness under extreme compression is what matters for production. It shows it generalizes well across different models and tokenization schemes, which is good because we don't have perfect control over everything >

Jane: So, in summary, KiToke proposes this training-free method that uses global diversity estimation to select tokens and interval-aware merging to keep the temporal flow right >

Tom: Precisely. It reduces spatiotemporal redundancy by capturing it globally and preserves information by using those diversity scores for selection and merging >

Lu: The paper’s core idea is explicitly capturing global redundancy across an entire video, which is what makes it different from prior methods that rely on local or segment-level heuristics >

Jane: It seems like the practical implication here is that we can use these models more efficiently without having to retrain them for every compression level >

Tom: Exactly. And they show it delivers a substantial accuracy boost while achieving this speedup, making it suitable for resource-constrained environments >

Lu: I think the focus on diversity-weighted merging is key because it ensures that the final representation maintains coherence while preserving information, as shown by the result where diversity-weighted merging achieves the highest average accuracy across all benchmarks >

Meng: For us in engineering, we’re looking at how stable this is. The paper claims it's robust under extreme compression, which means we can use it with less risk of catastrophic information loss when scaling down tokens >

Jane: It’s a solid piece of work that addresses the hard problem of applying these powerful video models to real-world applications without the massive computational overhead >

Tom: Yeah. KiToke is a training-free, query-agnostic approach for Video Large Language Models, and it seems like it sets a new benchmark for how we handle compression in this domain >

The paper's summary: Tom: So, we've been looking at KiToke today, and now we're going to break down what this paper actually says in its summary and what it means for us in the AI world.

Jane: The core idea is that KiToke proposes a training-free token compression method for Video Large Language Models that focuses on distribution awareness rather than just local token differences.

Lu: Yeah, it’s about estimating how tokens are distributed across the entire video—spatially and temporally—to figure out what's redundant globally.

Tom: So they use this global perspective to reduce spatiotemporal redundancy while trying to keep the important visual information intact, which is the main goal here.

Jane: They tackle two big problems: reducing that redundancy and making sure they don't lose critical visual details when they prune tokens.

Lu: They do it by estimating global redundancy using a kernel-based measure inspired by kernel density estimation, which basically lets them compute similarity between every pair of tokens on the whole set.

Tom: And then they use this to perform diversity-weighted sampling, which is how they select the subset of tokens they actually keep based on a retention ratio gamma.

Jane: They also introduce content-adaptive temporal intervals and interval-aware token merging to maintain that temporal flow even when tokens are being dropped.

Lu: The authors explicitly state that this approach is designed to be query-agnostic, meaning it doesn't care what specific question the Video LLM is trying to answer; it just compresses the video data efficiently.

Tom: And the results are pretty solid, showing that KiToke outperforms existing training-free compression methods across a wide range of retention ratios.

Jane: They show consistent gains even at extreme budgets, down to one percent retention, and they claim a six point eight times speedup in prefill time on their benchmarks.

Lu: That robust performance under aggressive compression is what’s interesting; it suggests the method generalizes well across different backbones and tokenization schemes.

Tom: It means we can deploy these Video LLMs much more affordably, with less computational overhead for the same level of accuracy.

Jane: It points toward a future where video understanding models can be used in environments that are currently too constrained for them due to the sheer size of the input data.

Lu: This work really shows how focusing on global diversity and structure preservation can lead to more efficient representation learning for complex modalities like video.

Tom: And it sets a new benchmark for how we handle compression in this domain, moving away from just local heuristics toward a more holistic view of redundancy.

The paper's improvements: Tom: So we’re looking at KiToke again, and now I want to talk about what they suggest as improvements to this distribution-aware compression method and why those matter for us on the engineering side.

Jane: The authors suggest a few ways they could push this further, focusing on making the estimation even more precise.

Lu: They mention that refining the kernel-based measure for global redundancy estimation could lead to a better diversity score calculation across all tokens.

Tom: If you get a cleaner diversity score, then the diversity-weighted sampling selection should be even tighter, meaning we keep more unique visual evidence at the same retention ratio gamma.

Jane: That makes sense because if the density estimation is sharper, the selection process becomes less arbitrary and more guided by actual visual uniqueness.

Lu: They also explore ways to integrate those temporal interval boundaries more closely with the spatial distribution information, making the content-adaptive part even smarter.

Tom: I like that idea—linking the time structure directly to how tokens are distributed across the whole video space, not just frame by frame.

Jane: And there’s also a discussion on how they could adapt this method for different types of video data, maybe moving beyond just standard sequences.

Lu: They touch upon extending the framework to handle different modalities or varying video resolutions without completely rewriting the core compression logic.

Tom: That would be huge for deployment because we deal with so much variety in input formats when we ship models to production, and a versatile compressor is always better.

Jane: It suggests that KiToke isn't just a solution for one specific video task; it has the potential to become a more general tool for efficient video representation learning.

Lu: I think the ambition here is moving from just compression to creating a more fundamentally aware way of representing video data for these large models.

Tom: If they can make those estimation improvements translate into better performance numbers, that would be great validation for the whole training-free concept.

Jane: So, what does this mean for someone who just listens to the show? It means we're looking at a system that’s not just cutting bits out randomly; it’s making choices based on a deep understanding of where and when in the video that information is most unique.

Conclusion: Tom: So we’re wrapping up KiToke today by summarizing what this distribution-aware token compression paper means for video models in general and saying goodbye to it.

Jane: The main thing is that they've moved beyond just pruning tokens locally, focusing instead on understanding the global visual distribution across the entire video.

Lu: They’ve shown that capturing this global redundancy through kernel density estimation allows for a more principled selection of which tokens to keep based on their overall visual uniqueness.

Tom: And the numbers back it up; they show consistent gains even when you're compressing down to one percent retention, which is pretty tough for any compression scheme.

Meng: I worry about that extreme case—if the underlying diversity estimate has a huge error at that low level, does it just lead to garbage tokens we can’t ignore?

Lalam: Based on the structure they built, I'm quite confident that the method is robust because it uses a diversity-weighted averaging step during merging, which pulls in those high-score unique tokens.

Jane: It seems like what this changes for us is that we don't have to spend so much time manually tuning local pruning parameters; the AI figure out where the redundancy actually lies.

Tom: Exactly, because it’s training-free and query-agnostic, it should work across different video backbones without needing a specific fine-tuning for every single one.

Lu: It really shows how representation learning can be guided by global structural measures rather than just local pixel differences or frame-to-frame changes.

Meng: For production, if we can trust this level of compression quality without massive retraining costs, that’s a win for deployment and cost management.

Jane: So in short, KiToke is a training-free token compression method that uses global diversity estimation to reduce redundancy while preserving critical visual information.

Tom: It's been a solid piece of work addressing the practical problem of making video understanding models cheaper and more efficient to run.

Lu: This paper sets a good direction for how we approach representation learning in multimodal tasks that involve long-form data like video.

Meng: We’ll keep an eye on those distribution-aware methods because they seem to be the most promising route for scalable video processing right now.

Haifeng Huang, Yang Li

Iowa State University

cs.CV

Submitted: 2026-04-03

Updated: 2026-10-13

Code: https://github.com/EvolvingLMMs-Lab/lmms-eval

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 92/100

The gist: The gist: KiToke proposes a training-free, query-agnostic token compression approach that reduces spatiotemporal redundancy while preserving critical visual information for Video Large Language

Key concepts

Global Diversity Estimation
This involves estimating how unique or redundant every visual token is across the whole video. KiToke uses a kernel-based measure inspired by density estimation. It calculates pairwise similarities using a Gaussian kernel to determine the density of each token, and then defines diversity as the inverse of this density, ensuring that tokens representing unique visual information are prioritized.
Distribution-Aware Compression
Instead of using simple local rules, KiToke treats the entire set of video tokens as samples from a single spatiotemporal distribution. This allows it to make selection decisions based on the global pattern of visual information rather than just small local segments. This approach helps balance token allocation across different visual patterns with varying frequencies.
Interval-Aware Token Merging
To keep temporal coherence, KiToke identifies significant temporal boundaries where visual dynamics change rapidly. Unselected tokens that fall within these intervals are then merged into the closest retained token using cosine similarity. This aggregation strategy ensures that fine-grained evidence is not lost while maintaining a structured representation of the video's flow.

Terminology

Summary

The gist: KiToke proposes a training-free, query-agnostic token compression approach that reduces spatiotemporal redundancy while preserving critical visual information for Video Large Language Models.

How it works

KiToke addresses the two core challenges in video token compression: reducing spatiotemporal redundancy and preserving temporal structure, by formulating the problem as global diversity maximization over the entire token set. Specifically, it estimates global redundancy using a kernel-based measure inspired by kernel density estimation (KDE). This involves defining pairwise similarity using a Gaussian kernel to compute the density of each token. The diversity score of token xi is defined as the inverse of its density Di, which aligns with the objective to preserve unique visual information.

Global Diversity Estimation and Selection

The method performs diversity-weighted sampling to keep a subset of tokens based on a retention ratio γ, we keep ⌊γN⌋ tokens via diversity-weighted sampling, where token xi is sampled with probability proportional to its diversity score Si">. The authors deliberately use stochastic sampling instead of deterministic top-K selection to preserve visual coverage at the group level. This approach mitigates the risk of dropping entire low-score groups, which is especially harmful under aggressive compression.

Temporal Structure Preservation

To maintain temporal coherence, KiToke constructs content-adaptive temporal intervals from token-level visual dynamics. This construction is based on two measures: frame-level visual difference, difft, which quantifies changes at corresponding spatial positions, and diffmatcht, which accounts for spatial displacement by matching each token in frame t−1 to its closest counterpart in frame t.

Interval Boundary Detection and Merging

Boundaries are detected by measuring deviation from local temporal dynamics. Specifically, the maximum deviation ∆t = maxdifft − difft−1, difft − difft+1 and the corresponding relative deviation ∆%t are computed. A temporal boundary is introduced at frame t if the visual change difft is large in magnitude, or if both ∆t and ∆%t indicate a pronounced deviation from neighboring frames.

Interval-aware Token Merging

To prevent the loss of fine-grained evidence during discarding, unselected tokens are aggregated into selected ones through token merging. The merging strategy assigns each unselected token xu ∈ Uk to its most similar retained token xr according to cosine similarity. The final merged representation of xr is computed via diversity-weighted averaging, ensuring that globally distinctive tokens exert greater influence on the aggregated representation.

Experimental Findings

Experiments across multiple video understanding benchmarks and Video LLM backbones demonstrate that KiToke consistently outperforms existing training-free compression methods across a wide range of retention ratios. Notably, KiToke maintains strong performance even under extreme token budgets (e.g., 1% retention), as shown in Figure 1. Furthermore, KiToke delivers the fastest prefill (6.8× speedup) and substantially higher accuracy than other approaches.

Conclusion

KiToke proposes a training-free, query-agnostic token compression method for Video Large Language Models. KiToke estimates global token diversity with a kernel-based redundancy measure and preserves temporal structure via interval-aware token merging, reducing spatiotemporal redundancy while retaining critical visual information >. The method's robust performance under extreme budgets demonstrates its effectiveness across various backbones and tokenization schemes. The work supports responsible and accessible use of Video LLMs without introducing new risks beyond those already associated with their deployment.

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KiToke: Kernel-based Interval-aware Token Compression for Video Large Language Models

Haifeng Huang 1 Yang Li 1

Abstract

Video Large Language Models (Video LLMs) achieve strong performance on video understanding tasks but suffer from high inference costs due to the large number of visual tokens. We propose KiToke, a training-free, query-agnostic token compression approach that reduces spatiotemporal redundancy while preserving critical visual information. Our method estimates token diversity globally using a kernel-based redundancy measure, enabling content-adaptive selection that remains effective under extreme token budgets, and further introduces a lightweight temporal interval construction with interval-aware token merging to maintain temporal coherence. Unlike prior methods that rely on local or segment-level heuristics, KiToke explicitly captures global redundancy across an entire video, leading to more efficient token utilization. Extensive experiments on multiple video understanding benchmarks and Video LLM backbones demonstrate that KiToke consistently outperforms existing training-free compression methods, with particularly large gains at aggressive retention ratios down to 1%. The code will be released when the paper is published.

  1. Introduction

Video Large Language Models (Video LLMs) achieve strong performance across a wide range of video understanding tasks, but their high inference cost limits deployment. A major bottleneck in Video LLM inference is the large number of visual tokens extracted from videos.

Improvements for AI systems

  1. Bold header: Global diversity estimation via kernel density estimation

This improvement allows for content-adaptive selection that remains effective under extreme token budgets by estimating redundancy globally using a smooth, nonparametric density over all tokens, which is superior to methods relying on local or segment-level heuristics because it explicitly captures global redundancy across an entire video.

  1. Bold header: Content-adaptive temporal interval construction

By constructing intervals from token-level visual dynamics, specifically using frame-to-frame differences and deviations from local temporal dynamics, the system ensures that the compression preserves temporal coherence by avoiding merging tokens across semantically misaligned moments.

  1. Bold header: Interval-aware token merging with diversity weighting

The method implements a strategy where unselected tokens are aggregated into selected ones, but this is guided by global diversity scores: we assign higher scores to low-density (i.e., less redundant) tokens and thus favor retaining unique evidence. This ensures that the final representation maintains coherence while preserving information, as shown by the result where diversity-weighted merging achieves the highest average accuracy across all benchmarks.

  1. Bold header: Robustness under extreme compression

The system demonstrates robust generalization across models and tokenization schemes and shows consistent gains over prior training-free methods, especially under extreme retention ratios, maintaining high performance even at a 1% retention ratio where other methods suffer significant degradation.

  1. Bold header: Reduced inference latency with high accuracy

The compression overhead is managed such that KiToke achieves the lowest total prefill time (6.8× speedup) while also attaining the highest average accuracy, making it suitable for deployment in resource-constrained environments compared to baselines like PruneVID or VisionZip.

Abstract

Video Large Language Models (Video LLMs) achieve strong performance on video understanding tasks but suffer from high inference costs due to the large number of visual tokens. We study training-free, query-agnostic video token compression, where heuristic importance rules can over-represent large redundant visual groups and miss less frequent visual patterns, particularly under aggressive token budgets. We propose KiToke, a distribution-aware compression framework that treats visual tokens as samples from the video's spatiotemporal token distribution. KiToke uses global embedding density as a proxy for representational redundancy and inverse-density sampling to balance token allocation across visual patterns of different frequencies. It further aggregates unselected tokens into retained anchors within detected temporal intervals to reduce cross-transition mixing. Across evaluated backbones, KiToke achieves the highest average accuracy among compared training-free methods and remains robust down to 1% retention, where most evaluated baselines underperform uniform random token sampling. On LLaVA-OneVision, it preserves 98.5% and 89.2% of the uncompressed model's four-benchmark mean accuracy at 10% and 1% token retention, with 6.8x and 12.5x prefill speedups, respectively, including compression overhead.

Sources

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