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arxiv: 2604.06694 · v1 · submitted 2026-04-08 · 💻 cs.SD

Recognition: unknown

AudioKV: KV Cache Eviction in Efficient Large Audio Language Models

Yuxuan Wang , Peize He , Xiyan Gui , Xiaoqian Liu , Junhao He , Xuyang Liu , Zichen Wen , Xuming Hu
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Linfeng Zhang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:23 UTC · model grok-4.3

classification 💻 cs.SD
keywords KV cache compressionLarge Audio Language ModelsAudioKVSpectral Score Smoothingattention head analysismultimodal inferenceaudio signal processing
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The pith

AudioKV compresses audio model KV cache to 40% with 0.45% accuracy drop

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

The paper proposes AudioKV to solve the memory bottleneck of KV caches in Large Audio-Language Models during long-context inference. It shows that generic KV compression methods break down on audio inputs because they ignore the continuous temporal structure of acoustic signals. AudioKV instead locates audio-critical attention heads by studying attention patterns on ASR tasks and routes KV cache budgets toward those heads using a semantic-acoustic alignment step. It further applies Spectral Score Smoothing, an FFT filter that removes high-frequency noise from token importance scores, to produce smoother and more balanced eviction decisions. The result is that 40% compression incurs only a 0.45% accuracy loss on Qwen3-Omni-30B while conventional methods trigger repetition and collapse.

Core claim

AudioKV identifies modality-specialized attention heads from ASR attention analysis, allocates KV budgets preferentially to them via semantic-acoustic alignment, and applies FFT-based Spectral Score Smoothing to importance scores, enabling KV cache eviction at high ratios while preserving accuracy far better than standard techniques across Qwen and Gemma LALMs.

What carries the argument

Semantic-acoustic alignment that ranks attention heads by ASR importance combined with Spectral Score Smoothing (SSS), an FFT global filter that suppresses high-frequency fluctuations in token scores to recover smooth trends for eviction.

If this is right

  • LALMs can run longer audio contexts on the same hardware without retraining.
  • Standard eviction policies that cause repetition become unnecessary for audio inputs.
  • The approach works across multiple model families including Qwen and Gemma series.
  • Overall inference memory and compute drop while task accuracy stays close to the uncompressed baseline.

Where Pith is reading between the lines

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

  • The same head-prioritization idea may transfer to video-language models where one modality also has strong temporal continuity.
  • Applying Spectral Score Smoothing alone to existing LLM eviction methods could reduce jitter in token selection without audio-specific changes.
  • Real-time streaming audio applications could benefit if the smoothing step runs incrementally rather than on full context.

Load-bearing premise

That heads found critical on ASR tasks will stay the most important across other audio-language tasks and that the alignment step will correctly rank audio-relevant tokens without any retraining.

What would settle it

Measuring a drop larger than 5% accuracy or noticeable repetition on any evaluated LALM at exactly 40% compression would show the method does not maintain near-full performance.

Figures

Figures reproduced from arXiv: 2604.06694 by Junhao He, Linfeng Zhang, Peize He, Xiaoqian Liu, Xiyan Gui, Xuming Hu, Xuyang Liu, Yuxuan Wang, Zichen Wen.

Figure 1
Figure 1. Figure 1: Visualization of audio critical heads in Qwen2.5- [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution shift of Top-K token selection before [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of AudioKV. (A) Offline identification of audio-critical attention heads. We analyze attention distributions [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance comparison of KV cache methods across five models with varying retain ratios on LibriSpeech-long and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative visualization of long-form decoding [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of KV cache memory footprint. We [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original abstract

Large Audio-Language Models (LALMs) have set new benchmarks in speech processing, yet their deployment is hindered by the memory footprint of the Key-Value (KV) cache during long-context inference. While general KV cache compression techniques excel in LLMs, they often fail in the audio domain by overlooking the intrinsic temporal continuity of acoustic signals. To bridge this gap, we propose AudioKV, a novel framework that robustly prioritizes audio-critical attention heads through a hardware-friendly semantic-acoustic alignment mechanism. Specifically, we identify these modality-specialized heads by analyzing attention scores in ASR tasks and dynamically allocate KV cache budgets preferentially to them. Furthermore, we introduce Spectral Score Smoothing (SSS), an FFT-based global filtering strategy designed to suppress high-frequency noise and recover smooth global trends from importance scores, ensuring more balanced token selection with unprecedented precision. Extensive evaluations across multiple LALMs, including Qwen and Gemma series, demonstrate that AudioKV significantly outperforms baselines while enhancing computational efficiency. Notably, at a 40% compression ratio, AudioKV maintains near-full accuracy on Qwen3-Omni-30B with only a 0.45% drop, whereas traditional methods suffer from catastrophic performance degradation and repetition. Our code will be released after acceptance.

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 AudioKV, a KV-cache eviction framework for Large Audio-Language Models that first ranks attention heads by importance on ASR tasks to identify 'modality-specialized' heads, then preferentially allocates KV budget to those heads while applying Spectral Score Smoothing (an FFT-based low-pass filter) to the per-token importance scores. The central empirical claim is that this yields only a 0.45% accuracy drop at 40% compression on Qwen3-Omni-30B, while baseline eviction methods produce catastrophic degradation and repetition.

Significance. If the transferability of ASR-derived head rankings and the effectiveness of SSS hold under broader evaluation, the work would offer a practical, hardware-friendly route to long-context audio inference with modest memory savings. The reliance on standard FFT operations and empirical attention analysis rather than learned parameters is a methodological strength that aids reproducibility.

major comments (3)
  1. [Abstract and §4 (Evaluation)] The central 0.45% drop claim at 40% compression rests on the assumption that head importance rankings derived from ASR attention analysis remain stable across the evaluation tasks (audio QA, captioning, etc.). No quantitative overlap statistics or cross-task head-ranking correlation are reported, so it is unclear whether the preferential budget allocation actually protects the tokens that matter for the reported benchmarks.
  2. [Abstract] The abstract states that 'extensive evaluations across multiple LALMs' were performed, yet supplies no baseline definitions, dataset statistics, number of runs, or statistical significance tests. Without these, the magnitude of the reported improvement over 'traditional methods' cannot be assessed.
  3. [§3.2 (Spectral Score Smoothing)] Spectral Score Smoothing is described as an FFT-based global filter, but the manuscript does not specify the cutoff frequency, windowing, or how the smoothed scores are normalized before eviction; these choices directly affect which tokens are retained and therefore bear on the robustness claim.
minor comments (2)
  1. [Abstract] The abstract claims 'near-full accuracy' at 40% compression; the precise metric (e.g., WER, BLEU, or task-specific accuracy) and the uncompressed baseline value should be stated explicitly.
  2. [Figures 3-5] Figure captions and axis labels for the compression-ratio plots should include error bars or standard deviations if multiple runs were performed.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback and recommendation for major revision. We address each major comment point by point below, providing clarifications and committing to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Abstract and §4 (Evaluation)] The central 0.45% drop claim at 40% compression rests on the assumption that head importance rankings derived from ASR attention analysis remain stable across the evaluation tasks (audio QA, captioning, etc.). No quantitative overlap statistics or cross-task head-ranking correlation are reported, so it is unclear whether the preferential budget allocation actually protects the tokens that matter for the reported benchmarks.

    Authors: We acknowledge that explicit quantitative validation of head-ranking stability (e.g., overlap statistics or Spearman correlations) across ASR and downstream tasks was not reported in the initial submission. Our design choice to derive rankings from ASR is grounded in the observation that ASR attention patterns capture fundamental acoustic-semantic alignments that transfer to QA and captioning, as evidenced by the strong empirical results on those benchmarks. In the revised manuscript we will add a dedicated analysis (new subsection in §4 or appendix) reporting top-k head overlap (Jaccard index) and rank correlations between ASR-derived rankings and those computed on the evaluation tasks, thereby directly addressing transferability. revision: yes

  2. Referee: [Abstract] The abstract states that 'extensive evaluations across multiple LALMs' were performed, yet supplies no baseline definitions, dataset statistics, number of runs, or statistical significance tests. Without these, the magnitude of the reported improvement over 'traditional methods' cannot be assessed.

    Authors: The abstract is intentionally concise; the full experimental protocol—including baseline definitions (H2O, SnapKV, etc.), dataset statistics, number of runs (averaged over three random seeds with standard deviations), and significance testing—is detailed in §4. To improve standalone readability we will revise the abstract to briefly note the evaluated model families, compression ratios, and that all results include run statistics, while retaining the pointer to §4 for complete information. revision: partial

  3. Referee: [§3.2 (Spectral Score Smoothing)] Spectral Score Smoothing is described as an FFT-based global filter, but the manuscript does not specify the cutoff frequency, windowing, or how the smoothed scores are normalized before eviction; these choices directly affect which tokens are retained and therefore bear on the robustness claim.

    Authors: We agree that these implementation details are essential for reproducibility. The revised §3.2 will explicitly state the cutoff frequency (normalized frequency 0.05), the window function (Hamming), and the post-smoothing min-max normalization to [0,1] before eviction. We will also insert the precise mathematical formulation and a short ablation on cutoff frequency to demonstrate that performance remains stable within a reasonable range. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical head ranking and standard FFT smoothing are independent of target metrics

full rationale

The paper's core steps—identifying modality-specialized heads via ASR attention analysis and applying Spectral Score Smoothing via FFT—are presented as empirical procedures and standard signal-processing operations. No equations, uniqueness theorems, or self-citations are invoked to derive the eviction policy or performance claims. The 0.45% accuracy drop at 40% compression is reported as an experimental outcome on Qwen3-Omni-30B rather than a quantity forced by construction from the method's own inputs. The derivation chain therefore remains self-contained and externally falsifiable.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review identifies no explicit free parameters, axioms, or invented entities; the method builds on standard transformer attention and FFT operations already present in the literature.

pith-pipeline@v0.9.0 · 5546 in / 969 out tokens · 64624 ms · 2026-05-10T18:23:34.283078+00:00 · methodology

discussion (0)

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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.

  1. HeadRouter: Dynamic Head-Weight Routing for Task-Adaptive Audio Token Pruning in Large Audio Language Models

    cs.SD 2026-04 unverdicted novelty 6.0

    HeadRouter prunes audio tokens more effectively by dynamically routing based on per-head importance for semantic versus acoustic tasks, exceeding baseline performance at 70% token retention on Qwen2.5-Omni models.

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