arxiv: 2602.04804 · v2 · submitted 2026-02-04 · 💻 cs.CL

Recognition: no theorem link

OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models

Yue Ding , Yiyan Ji , Jungang Li , Xuyang Liu , Xinlong Chen , Junfei Wu , Bozhou Li , Bohan Zeng

show 7 more authors

Yang Shi Yushuo Guan Yuanxing Zhang Jiaheng Liu Qiang Liu Pengfei Wan Liang Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-16 07:21 UTC · model grok-4.3

classification 💻 cs.CL

keywords token compressionomni-modal LLMsvideo pruningaudio selectionmultimodal efficiencyspatio-temporal redundancylarge language models

0 comments

The pith

OmniSIFT reduces omni-modal token sequences to 25 percent of their length while matching or exceeding full-context accuracy on several tasks.

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

Omni-modal large language models incur heavy compute costs from long combined video and audio token streams. The paper presents OmniSIFT as a modality-asymmetric compression method that first prunes video tokens by removing both intra-frame spatial and inter-frame temporal redundancies, then filters audio tokens according to visual content. The two stages are trained jointly through a differentiable straight-through estimator and add only a small number of parameters. Experiments on five benchmarks show that retaining just one-quarter of the original tokens produces results superior to other compression techniques and, in some cases, better than the uncompressed model. If the approach holds, it would let these models process longer inputs or run with lower latency on existing hardware.

Core claim

OmniSIFT applies a two-stage modality-asymmetric compression: a spatio-temporal video pruning module eliminates redundancy from both intra-frame structure and inter-frame overlap, followed by a vision-guided audio selection module that filters audio tokens; the framework is optimized end-to-end with a differentiable straight-through estimator. On Qwen2.5-Omni-7B this adds 4.85 million parameters, runs faster than training-free baselines, and at 25 percent token context consistently beats all compression baselines while surpassing the full-token model on multiple tasks.

What carries the argument

Modality-asymmetric token compression that performs spatio-temporal video pruning followed by vision-guided audio selection, trained end-to-end via a differentiable straight-through estimator.

If this is right

Only 4.85 million added parameters for a 7B-scale omni-model while achieving lower latency than training-free methods.
Consistent gains over existing compression baselines across five representative benchmarks.
Ability to exceed full-token performance on select tasks even after aggressive 75 percent reduction.
End-to-end differentiability allows the compression to adapt directly to task objectives.

Where Pith is reading between the lines

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

The structured redundancy in multimodal streams may be larger and more predictable than previously assumed, opening room for further compression ratios.
Vision-guided audio filtering could be reversed to let audio cues influence video selection in future variants.
The same asymmetric logic might transfer to text-plus-image or text-plus-3D settings where one modality dominates task relevance.

Load-bearing premise

The pruning and selection steps retain every piece of information required by downstream tasks without introducing systematic bias across varied video-audio content.

What would settle it

A controlled experiment in which OmniSIFT is applied to video clips containing rapid actions or audio events misaligned with the main visual subject and the method shows a clear accuracy drop relative to the full-token baseline.

read the original abstract

Omni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences leads to substantial computational overhead. Despite this challenge, token compression methods designed for Omni-LLMs remain limited. To bridge this gap, we propose OmniSIFT (Omni-modal Spatio-temporal Informed Fine-grained Token compression), a modality-asymmetric token compression framework tailored for Omni-LLMs. Specifically, OmniSIFT adopts a two-stage compression strategy: (i) a spatio-temporal video pruning module that removes video redundancy arising from both intra-frame structure and inter-frame overlap, and (ii) a vision-guided audio selection module that filters audio tokens. The entire framework is optimized end-to-end via a differentiable straight-through estimator. Extensive experiments on five representative benchmarks demonstrate the efficacy and robustness of OmniSIFT. Notably, for Qwen2.5-Omni-7B, OmniSIFT introduces only 4.85M parameters while maintaining lower latency than training-free baselines such as OmniZip. With merely 25% of the original token context, OmniSIFT consistently outperforms all compression baselines and even surpasses the performance of the full-token model on several tasks.

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

OmniSIFT cuts omni-modal tokens to 25% via asymmetric video pruning and vision-guided audio selection, with reported gains over baselines and sometimes the full model.

read the letter

OmniSIFT shows that you can cut omni-modal token sequences down to 25% and still beat standard compression methods, sometimes even the full model, by pruning video tokens based on space and time and then picking audio tokens using vision cues. The two-stage asymmetric setup is the main addition here. Video gets spatio-temporal pruning to cut intra-frame and inter-frame redundancy, while audio tokens are filtered using vision features. They train the whole thing end-to-end with a differentiable straight-through estimator. On Qwen2.5-Omni-7B this adds just 4.85M parameters and runs faster than some training-free baselines. The experiments on five benchmarks report consistent outperformance at the reduced token count. That is the practical win they are selling. The potential weak spot is the vision-guided audio selection. It assumes visual information is a good proxy for which audio matters. That may not hold when audio events are independent of the visuals, such as pure dialogue or off-screen sounds. The abstract also skips details on statistical significance and exact data splits, so the headline results need verification for robustness. This paper is for people working on efficient multimodal models, especially those handling video and audio together. The compression idea is concrete and the efficiency claims are worth testing. I would recommend sending it to peer review. The core method addresses a real scaling issue and the reported gains make it worth a closer look from referees.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes OmniSIFT, a modality-asymmetric token compression framework for Omni-modal LLMs. It employs a two-stage strategy consisting of (i) spatio-temporal video pruning to eliminate intra-frame structural and inter-frame overlap redundancies and (ii) vision-guided audio token selection, with the full pipeline optimized end-to-end via a differentiable straight-through estimator. Experiments on five benchmarks using models such as Qwen2.5-Omni-7B report that retaining only 25% of the original token context yields consistent outperformance over compression baselines and, on several tasks, exceeds the full-token model, while adding only 4.85M parameters and incurring lower latency than training-free methods.

Significance. If the performance claims are substantiated, the work offers a practical route to lowering the computational burden of long multimodal sequences in Omni-LLMs without performance degradation. The asymmetric treatment of video and audio modalities together with the parameter-light, end-to-end trainable design constitute clear engineering contributions that could influence efficient deployment of audio-video understanding models.

major comments (2)

[Experiments] Experiments section: the headline result that OmniSIFT at 25% tokens surpasses the full-token model on several tasks is presented without reported statistical significance tests, standard deviations across runs, or explicit confirmation that baseline implementations and data splits match those used for the full model; these omissions make it impossible to judge whether the observed gains are robust or could be explained by experimental variance.
[§3.2] Vision-guided audio selection module (§3.2): the filtering of audio tokens is driven exclusively by vision features, yet the manuscript provides no ablation studies or error analysis on audio-dominant subsets (speech-only segments, background events, or low visual-audio correlation clips) that would test whether the visual proxy systematically discards task-critical audio information.

minor comments (2)

[Abstract and §3] The precise token-reduction ratios applied separately to video and audio streams should be stated explicitly when the overall 25% figure is introduced, so that readers can reproduce the compression schedule.
[Table 1 or Experiments] A short comparison table listing the number of added parameters and FLOPs for each baseline (including OmniZip) would improve clarity of the efficiency claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will incorporate revisions to improve experimental rigor and analysis of the audio selection module.

read point-by-point responses

Referee: [Experiments] Experiments section: the headline result that OmniSIFT at 25% tokens surpasses the full-token model on several tasks is presented without reported statistical significance tests, standard deviations across runs, or explicit confirmation that baseline implementations and data splits match those used for the full model; these omissions make it impossible to judge whether the observed gains are robust or could be explained by experimental variance.

Authors: We acknowledge the need for greater statistical transparency. In the revised version, we will report standard deviations computed over three independent runs with different random seeds for all key results. We will also add paired statistical significance tests (Wilcoxon signed-rank) between OmniSIFT and both the full-token model and the strongest baselines. A new paragraph will explicitly confirm that all baselines were re-implemented with identical data splits, tokenizers, and model checkpoints as the full-token setting. revision: yes
Referee: [§3.2] Vision-guided audio selection module (§3.2): the filtering of audio tokens is driven exclusively by vision features, yet the manuscript provides no ablation studies or error analysis on audio-dominant subsets (speech-only segments, background events, or low visual-audio correlation clips) that would test whether the visual proxy systematically discards task-critical audio information.

Authors: We agree that targeted analysis on audio-dominant cases is important. The revised manuscript will include a new ablation subsection evaluating performance on speech-only and low visual-audio correlation subsets drawn from the existing benchmarks. We will compare vision-guided selection against audio-only and random selection baselines on these subsets and provide qualitative error analysis of failure cases, while noting the current design choice and its limitations. revision: yes

Circularity Check

0 steps flagged

No circularity detected; derivation is self-contained

full rationale

The paper introduces a two-stage token compression method (spatio-temporal video pruning followed by vision-guided audio selection) optimized end-to-end with a straight-through estimator. All performance claims rest on empirical results from five external benchmarks rather than any internal derivation that reduces to fitted parameters or self-citations by construction. No equations, uniqueness theorems, or ansatzes are shown to be equivalent to their own inputs. The framework is presented as a novel engineering contribution whose validity is tested externally, satisfying the criteria for a non-circular finding.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework relies on standard differentiable approximation for discrete token selection and empirical tuning of pruning thresholds; no new physical entities or ungrounded axioms are introduced.

free parameters (1)

pruning thresholds and selection ratios
Compression ratios and selection criteria are tuned during end-to-end training to achieve the reported 25% token reduction.

axioms (1)

standard math Straight-through estimator permits gradient propagation through non-differentiable pruning operations
Invoked to enable joint optimization of the compression modules with the base LLM.

pith-pipeline@v0.9.0 · 5567 in / 1140 out tokens · 49114 ms · 2026-05-16T07:21:07.168044+00:00 · methodology

discussion (0)

Forward citations

Cited by 5 Pith papers

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

TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos
cs.CV 2026-05 unverdicted novelty 8.0

TraceAV-Bench is the first benchmark for multi-hop trajectory reasoning over long audio-visual videos, showing top models reach only 51-68% accuracy with substantial room for improvement.
Keep What Audio Cannot Say: Context-Preserving Token Pruning for Omni-LLMs
cs.CV 2026-05 unverdicted novelty 6.0

ContextGuard prunes 55% of tokens in Qwen2.5-Omni 7B while matching full performance on five of six audio-visual benchmarks by preserving audio-irrecoverable visual context.
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.
OmniRefine: Alignment-Aware Cooperative Compression for Efficient Omnimodal Large Language Models
cs.AI 2026-05 unverdicted novelty 5.0

OmniRefine introduces alignment-aware chunk refinement via similarity and dynamic programming followed by modality-cooperative token compression, achieving near-baseline accuracy at 44% token retention on WorldSense.
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
cs.CV 2026-04 unverdicted novelty 4.0

OpenWorldLib offers a standardized codebase and definition for world models that combine perception, interaction, and memory to understand and predict the world.