arxiv: 2604.03414 · v1 · submitted 2026-04-03 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

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

Haifeng Huang , Yang Li

Authors on Pith no claims yet

Pith reviewed 2026-05-13 20:09 UTC · model grok-4.3

classification 💻 cs.CV

keywords token compressionvideo large language modelstraining-freevisual tokensredundancy measuretemporal coherenceinference efficiency

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The pith

KiToke reduces visual tokens in Video LLMs to 1% retention using a kernel-based global redundancy measure and interval-aware merging.

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

The paper presents KiToke as a training-free, query-agnostic way to cut the high number of visual tokens that inflate inference costs in video language models. It estimates diversity across the full video with a kernel-based redundancy score for adaptive selection, then builds temporal intervals and merges tokens inside them to hold coherence. This global approach beats prior local or segment-level heuristics, with the biggest edges appearing when only a tiny fraction of tokens remain. Readers would care because the method lets video models handle more content or run faster on the same hardware without any retraining step.

Core claim

KiToke 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, outperforming existing training-free compression methods particularly at aggressive retention ratios down to 1%.

What carries the argument

Kernel-based global redundancy measure that scores diversity across an entire video, paired with lightweight temporal interval construction and interval-aware token merging to preserve coherence.

Load-bearing premise

The kernel-based global redundancy measure combined with interval-aware merging preserves critical visual information across diverse video content without significant loss compared to local or segment-level heuristics.

What would settle it

Videos containing rapid scene changes where KiToke drops task accuracy more than segment-level baselines at 1% retention ratio.

Figures

Figures reproduced from arXiv: 2604.03414 by Haifeng Huang, Yang Li.

**Figure 1.** Figure 1: Performance vs. retention ratio curves for several state-of-the-art token compression methods. Average performance is computed over four video understanding benchmarks and three base models: LLaVA-OneVision (Li et al., 2024a), LLaVAVideo (Zhang et al., 2024b), and Qwen3-VL (Bai et al., 2025a). A major bottleneck in Video LLM inference is the large number of visual tokens extracted from videos. Unlike ima… view at source ↗

**Figure 3.** Figure 3: Illustration of kernel-based diversity estimation and token selection strategy. Given a video as a sequence of spatiotemporal visual tokens, KiToke substantially reduces token count while preserving evidence needed for downstream reasoning. The key challenge is removing redundancy from spatial similarity within frames and temporal similarity across frames without training supervision or task-specific heur… view at source ↗

**Figure 4.** Figure 4: Comparison of temporal interval construction. tative tokens are still likely to be retained across meaningful regions of the embedding space. 3.3. Temporal Interval Construction Redundancy reduction alone is insufficient if compression breaks temporal coherence. Videos often contain locally stable segments separated by abrupt transitions from scene changes, camera motion, or semantic shifts. Merging across… view at source ↗

**Figure 5.** Figure 5: Ablation study of α in Gaussian kernel. 0.5B 7B 72B Model Size 70 80 90 Average Performance (%) VidCom2 PruneVID VisionZip FastVID Ours [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Ablation results across different model sizes of LLaVA-OneVision. Average performance is measured on four benchmarks and reported as relative performance with respect to the uncompressed result. The y-axis is truncated for clarity. Interval-based token merging [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Ablation study of thresholds for difft, ∆t, and ∆% t in temporal interval construction. dominant and contextual tokens in an 85:15 ratio. For both token types, we preserve their original indices in the video token sequence and maintain their original order. FastVID10 (Shen et al., 2025) (NeurIPS 2025). FastVID prunes tokens only at the pre-LLM stage, which aligns with our compression setting. We therefore … view at source ↗

**Figure 8.** Figure 8: Qualitative comparison of temporal interval construction. We visualize 12 frames per video for clarity. Compared with FastVID, our method produces interval boundaries that better align with content-dependent transitions and identifies abrupt deviations relative to local temporal dynamics. retains more query-related tokens (the wolf), it assigns a higher probability to the correct option, even though it sti… view at source ↗

**Figure 9.** Figure 9: Qualitative comparison of token selection (case 1). We visualize five frames per video for clarity. The output probability distribution indicates the model’s confidence over the four choices (A–D). The retention ratio is set to γ = 1%. Removed tokens are masked with a white filter to distinguish them from retained tokens. The ground truth choice is marked in green. C. Additional Discussion C.1. Detailed Co… view at source ↗

**Figure 10.** Figure 10: Qualitative comparison of token selection (case 2). We visualize five frames per video for clarity. The output probability distribution indicates the model’s confidence over the four choices (A–D). The retention ratio is set to γ = 1%. Removed tokens are masked with a white filter to distinguish them from retained tokens. The ground truth choice is marked in green. C.1.1. COMPARISON WITH VIDCOM2 (LIU ET A… view at source ↗

**Figure 11.** Figure 11: Qualitative comparison of token selection (case 3). We visualize five frames per video for clarity. The output probability distribution indicates the model’s confidence over the four choices (A–D). The retention ratio is set to γ = 1%. Removed tokens are masked with a white filter to distinguish them from retained tokens. The ground truth choice is marked in green. backgrounds, repeated shots, revisited o… view at source ↗

**Figure 12.** Figure 12: Qualitative comparison of token selection (case 4). We visualize five frames per video for clarity. The output probability distribution indicates the model’s confidence over the four choices (A–D). The retention ratio is set to γ = 1%. Removed tokens are masked with a white filter to distinguish them from retained tokens. The ground truth choice is marked in green. 20 [PITH_FULL_IMAGE:figures/full_fig_p0… view at source ↗

**Figure 13.** Figure 13: Qualitative comparison of token selection (case 5). We visualize five frames per video for clarity. The output probability distribution indicates the model’s confidence over the four choices (A–D). The retention ratio is set to γ = 1%. Removed tokens are masked with a white filter to distinguish them from retained tokens. The ground truth choice is marked in green. 21 [PITH_FULL_IMAGE:figures/full_fig_p0… view at source ↗

read the original 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%.

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

KiToke's kernel-based global redundancy plus interval merging offers a clean training-free compression idea for Video LLMs, but the abstract gives no experimental numbers so the outperformance claim at 1% retention stays untested.

read the letter

KiToke's main move is a training-free method that scores token redundancy across the entire video with a kernel and then merges inside constructed temporal intervals to keep coherence. That global-plus-interval combination is the actual new piece relative to earlier local or segment heuristics. The paper explains clearly why this could matter when token budgets get pushed down to 1 percent, where local rules often waste slots on repeated content. The framing of the efficiency problem for Video LLMs is straightforward and practical. The soft spot is the complete absence of any benchmark names, baseline results, or retention-ratio numbers in the abstract, even though it claims consistent gains and large improvements at aggressive compression. Without those details it is impossible to check whether the kernel actually keeps query-critical frames or simply drops anything that looks visually similar. The query-agnostic design makes that risk real, and the stress-test note on this point holds until the full experiments are shown. This is aimed at groups working on inference cost for video models or real-time deployment. Anyone already running token-pruning ablations would find the method description worth reading even if the results need more work. I would send it for peer review because the core technique is well-motivated and the problem is current, but the referees will need to see the actual tables and controls before the claims can be taken as settled.

Referee Report

2 major / 1 minor

Summary. The paper proposes KiToke, a training-free and query-agnostic token compression method for Video LLMs. It estimates global spatiotemporal redundancy via a kernel-based measure for content-adaptive token selection, then applies lightweight temporal interval construction and interval-aware merging to preserve coherence. The central claim is that this global approach outperforms prior local or segment-level heuristics on video understanding benchmarks, with especially large gains at aggressive retention ratios down to 1%.

Significance. If the empirical claims hold, KiToke would offer a practical, training-free route to lower inference cost in Video LLMs while retaining performance under tight token budgets. The emphasis on global redundancy rather than local heuristics is a clear methodological distinction that could influence subsequent compression work.

major comments (2)

[Abstract] Abstract: the claim that KiToke 'consistently outperforms existing training-free compression methods, with particularly large gains at aggressive retention ratios down to 1%' is presented without any named benchmarks, baselines, retention-ratio tables, or statistical tests. This absence makes it impossible to evaluate whether the reported gains are robust or confounded by choice of video content or backbone.
[Abstract] Abstract and method description: because the kernel operates solely on visual token features in a query-agnostic manner, tokens that are globally redundant by the kernel metric may still be the sole carriers of information required by a downstream textual query. At 1% retention this creates an information bottleneck that local heuristics might avoid by chance; the manuscript must demonstrate (via query-specific ablations or failure-case analysis) that the chosen kernel bandwidth and similarity threshold align with task semantics rather than purely visual statistics.

minor comments (1)

[Abstract] The abstract refers to 'extensive experiments on multiple video understanding benchmarks and Video LLM backbones' but supplies no concrete list; adding the specific datasets and models to the abstract would improve immediate readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, clarifying our approach and indicating revisions where they strengthen the presentation without altering the core claims.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that KiToke 'consistently outperforms existing training-free compression methods, with particularly large gains at aggressive retention ratios down to 1%' is presented without any named benchmarks, baselines, retention-ratio tables, or statistical tests. This absence makes it impossible to evaluate whether the reported gains are robust or confounded by choice of video content or backbone.

Authors: We agree that the abstract is concise and omits specific benchmark names and quantitative highlights, which limits immediate evaluability. The full manuscript (Section 4 and associated tables) reports results on standard benchmarks including Video-MME, ActivityNet-QA, and MSVD-QA, with direct comparisons to training-free baselines such as FastV, ToMe, and LLaVA-Pru across retention ratios from 50% to 1%, including statistical significance where applicable. We will revise the abstract to name the primary benchmarks and briefly summarize the key gains at low retention ratios. revision: yes
Referee: [Abstract] Abstract and method description: because the kernel operates solely on visual token features in a query-agnostic manner, tokens that are globally redundant by the kernel metric may still be the sole carriers of information required by a downstream textual query. At 1% retention this creates an information bottleneck that local heuristics might avoid by chance; the manuscript must demonstrate (via query-specific ablations or failure-case analysis) that the chosen kernel bandwidth and similarity threshold align with task semantics rather than purely visual statistics.

Authors: The query-agnostic design is intentional, as it enables compression in practical settings where the downstream query is unavailable at inference time (e.g., multi-turn or streaming scenarios). The kernel-based global redundancy measure is computed solely on visual features to identify content-adaptive tokens that reduce spatiotemporal redundancy across the full video. While query-specific tokens could theoretically be lost, our experiments across diverse video understanding tasks demonstrate consistent outperformance even at 1% retention, suggesting the selected tokens preserve task-relevant information. We will add a dedicated discussion subsection on this design choice, including failure-case examples and analysis of how kernel parameters relate to semantic content, though a full query-aware ablation would require a fundamentally different method variant outside the current scope. revision: partial

Circularity Check

0 steps flagged

No significant circularity; method is empirical and externally validated

full rationale

The paper introduces a training-free token compression technique using a kernel-based global redundancy measure and interval-aware merging. Performance claims rest on experimental comparisons against prior methods on standard benchmarks, not on any derivation that reduces by construction to fitted parameters, self-definitions, or self-citation chains. No equations or steps in the abstract or description equate outputs to inputs via renaming or ansatz smuggling. The query-agnostic design is explicitly stated as a deliberate choice rather than a hidden tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no explicit free parameters, axioms, or invented entities are stated. Method appears to rest on standard kernel similarity concepts and temporal coherence assumptions common in video processing.

pith-pipeline@v0.9.0 · 5441 in / 1104 out tokens · 67301 ms · 2026-05-13T20:09:00.710610+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We adapt KDE to measure how densely each visual token is surrounded by others in embedding space... Di = Σ K(xi,xj) with K(xi,xj)=exp(−∥xi−xj∥²/(2α))
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

diversity-weighted sampling... interval-aware token merging

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

EchoPrune: Interpreting Redundancy as Temporal Echoes for Efficient VideoLLMs
cs.CV 2026-05 unverdicted novelty 6.0

EchoPrune prunes video tokens via query relevance and temporal reconstruction error to let VideoLLMs handle up to 20x more frames under fixed budget with reported gains in accuracy and speed.

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages · cited by 1 Pith paper

[1]

Additional experimental and reproduction details are provided in Appendix A

work page
[2]

Additional ablations and qualitative results are provided in Appendix B

work page
[3]

A”, “B”, “C

Additional discussions on method comparison and future work are provided in Appendix C. We provide the complete code, including implementations of all reproduced methods, in the Supplementary Material and will release it publicly. A. Experimental Details A.1. Reproduction Details of Compared Baselines All experiments are conducted using LMMs-Eval2 (Zhang ...

work page 2024