arxiv: 2605.14458 · v1 · submitted 2026-05-14 · 💻 cs.AI

Recognition: no theorem link

OmniDrop: Layer-wise Token Pruning for Omni-modal LLMs via Query-Guidance

Yeo Jeong Park , Hyemi Jang , Minseo Choi , Jongsun Lee , Jooyoung Choi , Yongkweon Jeon

Authors on Pith no claims yet

Pith reviewed 2026-05-15 01:32 UTC · model grok-4.3

classification 💻 cs.AI

keywords token pruningomni-modal LLMslayer-wise pruningquery guidancetemporal diversityaudiovisual compressionmultimodal efficiency

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The pith

Layer-wise token pruning inside the LLM decoder, guided by text queries, allows omni-modal models to process audiovisual inputs faster while maintaining or improving accuracy.

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

The paper introduces OmniDrop to solve the token explosion problem in omni-modal LLMs caused by high-resolution audio and video. Rather than pruning tokens early at the embedding stage, it allows initial fusion in early decoder layers and then prunes more aggressively in deeper layers using guidance from the text query. A temporal diversity score helps keep a balanced set of tokens to retain global context. This approach is training-free and leads to better results on audiovisual benchmarks with significant reductions in latency and memory.

Core claim

OmniDrop is a training-free framework that progressively prunes audiovisual tokens within the LLM decoder layers instead of at the input level, using text queries for modality-agnostic guidance and a temporal diversity score to preserve context, resulting in improved performance and efficiency on multimodal tasks.

What carries the argument

Layer-wise token pruning mechanism that uses query-guided attention scores to select tokens after early fusion, combined with a temporal diversity score for balanced retention.

If this is right

Omni-modal LLMs can handle longer audiovisual inputs in real time.
Prefill latency decreases by up to 40 percent across benchmarks.
Memory usage drops by up to 14.7 percent.
Accuracy improves by up to 3.58 points over existing pruning methods.
Task-adaptive pruning works without retraining the model.

Where Pith is reading between the lines

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

Early-layer fusion before pruning may be more effective than input-level methods for preserving cross-modal semantics.
Query guidance could extend to other pruning strategies in multimodal models.
Testing on tasks with conflicting text queries might reveal limits of the guidance approach.

Load-bearing premise

Text queries provide reliable guidance for identifying which audiovisual tokens are semantically important at different layers.

What would settle it

Measuring performance drop when text queries are deliberately mismatched to the audiovisual content on the same benchmarks.

Figures

Figures reproduced from arXiv: 2605.14458 by Hyemi Jang, Jongsun Lee, Jooyoung Choi, Minseo Choi, Yeo Jeong Park, Yongkweon Jeon.

**Figure 2.** Figure 2: (a) Layer-wise text-to-audiovisual attention scores, averaged within each audiovisual chunk [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of OmniDrop. (a) Progressive layer-wise token pruning (PLP) schedule based on [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Comparison of audio/video retained ratio per decoder-layer across three different tasks, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: (a) Layer-wise text-to-audiovisual attention scores, averaged within each audiovisual chunk [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗

read the original abstract

Omni-modal large language models have demonstrated remarkable potential in holistic multimodal understanding; however, the token explosion caused by high-resolution audio and video inputs remains a critical bottleneck for real-time applications and long-form reasoning. Existing omni-modal token compression methods typically prune tokens at the input embedding level, relying on audio-video similarity or temporal co-occurrence as proxies for semantic relevance. In practice, such assumptions are often unreliable. To address this limitation, we propose OmniDrop, a training-free, layer-wise token pruning framework that progressively prunes audiovisual tokens within the LLM decoder layers rather than at the input-level, allowing early layers to preserve sufficient omni-modal information fusion before aggressively removing tokens in deeper layers. We further utilize text queries as guidance for modality-agnostic and task-adaptive token pruning. We also introduce a temporal diversity score that encourages balanced token survival to preserve global temporal context. Experimental results across various audiovisual benchmarks demonstrate that OmniDrop outperforms all baselines by up to 3.58 points while reducing prefill latency by up to 40% and memory usage by up to 14.7%.

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

OmniDrop moves token pruning into the decoder layers with query guidance and a diversity term, which is a reasonable step beyond input-level methods but rests on thin experimental reporting.

read the letter

The paper's main contribution is a training-free pruning routine that waits until early decoder layers have done some cross-modal fusion, then uses the text query plus a temporal diversity score to drop audiovisual tokens progressively. This differs from the similarity-based input pruning cited in the abstract, and the claimed speedups (40% lower prefill latency, 14.7% less memory) plus modest accuracy gains (up to 3.58 points) address a real bottleneck for long audiovisual inputs. If the numbers hold under scrutiny, the approach could be adopted quickly because it requires no retraining. The idea itself is straightforward and the motivation is clear. The soft spots are the missing pieces needed to evaluate it. The abstract gives no concrete list of benchmarks, no pseudocode or equations showing exactly how query embeddings become layer-wise importance scores, and no ablation isolating the diversity term. The central assumption—that query-guided pruning after early fusion will reliably keep task-relevant tokens—looks plausible for query-driven tasks but could fail on background audio events or visual transitions that never appear in the text query. Because the method is training-free, there is no recovery mechanism if an important token is dropped. This is the kind of work that would interest groups building efficient multimodal agents or video models. A referee could check the implementation details and rerun the experiments without much trouble. I would send it for review to get the full experimental record and see whether the gains survive closer inspection.

Referee Report

2 major / 1 minor

Summary. The paper introduces OmniDrop, a training-free layer-wise token pruning framework for omni-modal LLMs. It performs progressive pruning of audiovisual tokens inside the decoder layers (after early cross-modal fusion) rather than at the input embedding stage, using text queries for modality-agnostic guidance and adding a temporal diversity score to maintain global context. The central claim is that this yields up to 3.58-point gains over baselines on audiovisual benchmarks while cutting prefill latency by up to 40% and memory by up to 14.7%.

Significance. If the experimental claims hold under rigorous validation, the work would be significant for efficient inference in long-context multimodal models, as a training-free, query-adaptive pruning strategy that exploits internal layer-wise fusion could reduce the token explosion problem without task-specific retraining.

major comments (2)

[Abstract] Abstract: the performance numbers (3.58-point gains, 40% latency reduction, 14.7% memory reduction) are presented without any description of the exact benchmarks, baseline implementations, number of runs, or statistical tests. Because these numbers are the primary evidence for the central claim that query-guided layer-wise pruning outperforms prior input-level methods, the absence of this information is load-bearing.
[Method] Method section (description of query-guided scoring): the manuscript states that text queries guide modality-agnostic pruning and introduces a temporal diversity term, yet provides neither the explicit formula for projecting query embeddings into per-layer token importance scores nor pseudocode showing how this score is combined with attention maps. Without this, it is impossible to verify whether the pruning criterion systematically discards query-independent but semantically relevant audiovisual structure, which is the weakest assumption identified in the approach.

minor comments (1)

[Abstract] The abstract and introduction repeatedly use the phrase 'modality-agnostic and task-adaptive' without clarifying whether this is a design property or an empirical observation; a short clarifying sentence would improve precision.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment point-by-point below, indicating the revisions we will make to improve clarity and transparency.

read point-by-point responses

Referee: [Abstract] Abstract: the performance numbers (3.58-point gains, 40% latency reduction, 14.7% memory reduction) are presented without any description of the exact benchmarks, baseline implementations, number of runs, or statistical tests. Because these numbers are the primary evidence for the central claim that query-guided layer-wise pruning outperforms prior input-level methods, the absence of this information is load-bearing.

Authors: We agree that the abstract would be strengthened by additional context on the experimental setup. In the revised version, we will expand the abstract to name the specific audiovisual benchmarks, note that the gains are measured against input-level pruning baselines, and state that results are averaged over multiple runs (with full details, including standard deviations and significance tests, provided in Section 4). Given abstract length constraints, we will add a concise qualifier rather than exhaustive statistics. revision: partial
Referee: [Method] Method section (description of query-guided scoring): the manuscript states that text queries guide modality-agnostic pruning and introduces a temporal diversity term, yet provides neither the explicit formula for projecting query embeddings into per-layer token importance scores nor pseudocode showing how this score is combined with attention maps. Without this, it is impossible to verify whether the pruning criterion systematically discards query-independent but semantically relevant audiovisual structure, which is the weakest assumption identified in the approach.

Authors: We acknowledge that the current method description is high-level and would benefit from greater mathematical precision. In the revised manuscript, we will add the explicit formula for the query-guided per-layer importance scoring (including how the text query embedding is projected and combined with attention maps) as well as the temporal diversity term. We will also include pseudocode for the full layer-wise pruning procedure to enable verification that the criterion preserves semantically relevant structure. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the proposed pruning framework

full rationale

The paper presents OmniDrop as a training-free procedural framework for layer-wise token pruning guided by text queries and a temporal diversity score. No equations, derivations, or parameter-fitting steps are described that reduce by construction to the method's own inputs. The central claims rely on external benchmark validation rather than self-citations, self-definitions, or renamed empirical patterns. The derivation chain is self-contained as a set of algorithmic choices with independent experimental support.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the domain assumption that text queries serve as reliable proxies for audiovisual token relevance and that early-layer fusion followed by later pruning preserves task performance. No free parameters or invented entities are mentioned.

axioms (2)

domain assumption Text queries provide reliable, modality-agnostic guidance for identifying semantically relevant audiovisual tokens.
Invoked to enable task-adaptive pruning without training.
domain assumption Progressive pruning after initial cross-modal fusion in early layers preserves sufficient information for downstream performance.
Core premise of the layer-wise design.

pith-pipeline@v0.9.0 · 5512 in / 1292 out tokens · 57674 ms · 2026-05-15T01:32:16.886455+00:00 · methodology

discussion (0)

Reference graph

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Phase 1 (Preservation): Sincep init = 0, the mean retained ratio is constant:¯rphase1 =r 0 = 0.45
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Phase 2 (Aggressive compression): To satisfy ¯R= 0.30, the required mean retained ratio for Phase 2 is: ¯rphase2 = 2 ¯R−¯rphase1 = 2(0.30)−0.45 = 0.15 Using a linear approximation of the geometric decay and Taylor expansion, the mean retained ratio of Phase 2 corresponds approximately to the ratio at midpoint of the Phase 2 (i.e.Layer 21, the 7-th layer i...