arxiv: 2605.12056 · v1 · submitted 2026-05-12 · 💻 cs.AI

Recognition: 1 theorem link

· Lean Theorem

OmniRefine: Alignment-Aware Cooperative Compression for Efficient Omnimodal Large Language Models

Yuchen Deng , Zidang Cai , Hai-Tao Zheng , Jie Wang , Feidiao Yang , Yuxing Han

Authors on Pith no claims yet

Pith reviewed 2026-05-13 04:46 UTC · model grok-4.3

classification 💻 cs.AI

keywords omnimodal llmstoken compressionaudio-video alignmentcross-modal reasoningdynamic programminginference efficiencycooperative compression

0 comments

The pith

OmniRefine refines audio-video chunk boundaries for alignment then jointly compresses tokens to cut inference cost while holding performance near full levels.

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

The paper sets out to establish that a two-stage training-free process can compress the many tokens from long videos and audio tracks in omnimodal models without breaking the cross-modal links those models need for reasoning. It first adjusts the starting and ending points of each compression chunk so that frames and audio segments line up more closely, using similarity scores and dynamic programming. It then compresses the video and audio tokens inside each newly aligned chunk together rather than separately. If the claim holds, models become practical to run on longer inputs because they retain most of their accuracy even after discarding more than half the original tokens.

Core claim

OmniRefine is a training-free two-stage framework for audio-visual token compression in Omni-LLMs. Correspondence-Preserving Chunk Refinement first converts native chunk boundaries into cross-modally aligned compression units by measuring frame-audio similarity and applying dynamic programming. Modality-Aware Cooperative Compression then jointly reduces redundancy in video and audio tokens inside each refined unit while keeping critical evidence intact.

What carries the argument

Correspondence-Preserving Chunk Refinement followed by Modality-Aware Cooperative Compression, which together produce aligned units that keep complementary audio-video information available during joint token reduction.

If this is right

The method delivers a better efficiency-performance trade-off than baselines that use fixed or native compression units.
Performance stays stable even when token retention drops to 44 percent.
On WorldSense the approach reaches 46.7 percent accuracy at 44 percent retention, nearly matching the full-token baseline.
Longer video streams and denser audio sequences become feasible at lower inference cost.

Where Pith is reading between the lines

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

The same boundary-refinement idea could extend to other paired modalities such as text and images where misalignment also hurts compression.
Lower token counts may enable real-time omnimodal applications on devices with limited memory or power.
Preserving alignment during compression reduces the chance that downstream reasoning steps lose synchronization between sound and visuals.

Load-bearing premise

Refining native chunk boundaries with frame-audio similarity and dynamic programming will create compression units that keep the complementary cross-modal information required for audio-video reasoning intact.

What would settle it

A controlled test at 44 percent token retention on WorldSense showing that accuracy with the refined aligned units falls more than a few points below the accuracy obtained with native unrefined chunks at the same retention rate.

Figures

Figures reproduced from arXiv: 2605.12056 by Feidiao Yang, Hai-Tao Zheng, Jie Wang, Yuchen Deng, Yuxing Han, Zidang Cai.

**Figure 2.** Figure 2: Motivating analysis of native chunk boundaries in Qwen2.5-Omni. (a) Shallow-layer (layer [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of OmniRefine. Given encoded audio-visual tokens, OmniRefine first applies [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Ablation on the audio budget. Performance across budget parameters [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Visualization of Dynamic Pruning. Video and audio retention ratios per chunk. benchmark. The results demonstrate that, on the 7B model, our method achieves a 1.15× speedup in overall inference and a remarkable 5.26× acceleration during the prefilling stage compared to the full-token baseline. Furthermore, our approach substantially mitigates memory overhead. By saving 15GB of GPU memory while retaining app… view at source ↗

read the original abstract

Omnimodal large language models (Omni-LLMs) show strong capability in audio-video understanding, but their practical deployment remains limited by high inference cost of long video streams and dense audio sequences. Despite recent progress, existing compression methods for Omni-LLMs typically rely on fixed or native compression units, which can disrupt cross-modal correspondence and the complementary information required for audio-video reasoning, making it difficult to improve inference efficiency while stably preserving performance. To address this, we propose OmniRefine, a training-free two-stage framework for efficient audio-visual token compression in Omni-LLMs. First, Correspondence-Preserving Chunk Refinement refines native chunk boundaries into cross-modally aligned compression units through frame-audio similarity and dynamic programming. Second, Modality-Aware Cooperative Compression jointly compresses video and audio tokens within each refined unit to reduce redundancy while preserving critical evidence. Extensive experiments show that OmniRefine achieves a better efficiency-performance trade-off than strong baselines and maintains stable performance under lower compression ratios. On WorldSense, it still reaches 46.7% accuracy at a 44% token retention ratio, nearly matching the full-token baseline. The code and interface will be released to facilitate further research.

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

OmniRefine offers a training-free two-stage compression pipeline for omnimodal LLMs but the experiments do not isolate whether the dynamic programming refinement step adds value beyond the cooperative compression alone.

read the letter

The paper's main contribution is a two-stage method: first, Correspondence-Preserving Chunk Refinement that uses frame-audio similarity and dynamic programming to adjust native chunk boundaries, then Modality-Aware Cooperative Compression that jointly reduces tokens inside each refined unit. This is presented as training-free and aimed at keeping cross-modal correspondence intact during compression for audio-video reasoning tasks. The concrete WorldSense result (46.7% accuracy at 44% token retention, nearly matching the full baseline) gives a practical data point on the efficiency-performance trade-off, and the focus on avoiding disruption to complementary information is a reasonable engineering concern for long-context multimodal models. Being training-free makes it easier to apply to existing models without extra compute. The soft spot is the missing isolation of the first stage. No ablation compares the full pipeline against cooperative compression applied directly to unrefined native chunks, so it is unclear whether the dynamic programming step is load-bearing or if the second stage drives most of the retained performance. The abstract also gives limited experimental details on baselines, variance, or statistical tests, which weakens the superiority claim. This work targets practitioners deploying omnimodal LLMs on constrained hardware who need simple compression tricks. A reader working on token efficiency might pick up the chunk refinement idea, but the central assumption about preserved cross-modal evidence needs tighter testing to hold up. I would send it for peer review to get the authors to add those ablations and setup details.

Referee Report

2 major / 2 minor

Summary. The paper proposes OmniRefine, a training-free two-stage framework for token compression in omnimodal LLMs. Stage 1 (Correspondence-Preserving Chunk Refinement) uses frame-audio similarity and dynamic programming to adjust native chunk boundaries for better cross-modal alignment. Stage 2 (Modality-Aware Cooperative Compression) then jointly compresses video and audio tokens within each refined unit to reduce redundancy. The central empirical claim is that this yields a superior efficiency-performance trade-off versus baselines, with stable results under aggressive compression (e.g., 46.7% accuracy on WorldSense at 44% token retention, nearly matching the full-token baseline).

Significance. If the results hold after proper isolation of components, the work would be moderately significant for practical deployment of omnimodal models, as it targets a real deployment bottleneck (cross-modal misalignment during compression) with a training-free method. The emphasis on preserving complementary audio-video evidence and the promise of code release are positive for reproducibility.

major comments (2)

§4 (Experiments) and §4.2 (Ablation Studies): No ablation isolates the contribution of Correspondence-Preserving Chunk Refinement. The WorldSense result (46.7% at 44% retention) is reported only for the full pipeline; there is no direct comparison against modality-aware cooperative compression applied to native (unrefined) chunks. This leaves the load-bearing assumption—that frame-audio similarity + DP produces units preserving complementary cross-modal information—untested, so the performance gain cannot be confidently attributed to the alignment step rather than the second stage alone.
§4.1 (Experimental Setup): The manuscript supplies the headline WorldSense number but provides insufficient detail on exact baseline implementations, the precise definition and computation of frame-audio similarity, the dynamic programming objective function and constraints, number of runs, and any statistical tests for the reported superiority. This weakens the ability to evaluate the efficiency-performance trade-off claims.

minor comments (2)

§3.1: The dynamic programming recurrence for boundary refinement would benefit from an explicit small-scale example or pseudocode to clarify how similarity scores translate into the final chunk boundaries.
Figure 3 / Table 2: Axis labels and legend entries for compression ratios and accuracy could be made more consistent across panels to improve readability of the efficiency curves.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to improve clarity and completeness.

read point-by-point responses

Referee: §4 (Experiments) and §4.2 (Ablation Studies): No ablation isolates the contribution of Correspondence-Preserving Chunk Refinement. The WorldSense result (46.7% at 44% retention) is reported only for the full pipeline; there is no direct comparison against modality-aware cooperative compression applied to native (unrefined) chunks. This leaves the load-bearing assumption—that frame-audio similarity + DP produces units preserving complementary cross-modal information—untested, so the performance gain cannot be confidently attributed to the alignment step rather than the second stage alone.

Authors: We agree that an explicit ablation isolating the Correspondence-Preserving Chunk Refinement stage is needed to strengthen attribution of gains. The current results show the full pipeline's performance, but we will add a new ablation in the revised §4.2 comparing Modality-Aware Cooperative Compression applied to native chunks versus the refined chunks on WorldSense (and other benchmarks). This will directly test whether the alignment-aware refinement preserves complementary cross-modal information beyond what the second stage achieves alone. We expect the refined units to yield higher accuracy at the same retention ratio, consistent with the paper's motivation that native chunks can disrupt audio-video correspondence. revision: yes
Referee: §4.1 (Experimental Setup): The manuscript supplies the headline WorldSense number but provides insufficient detail on exact baseline implementations, the precise definition and computation of frame-audio similarity, the dynamic programming objective function and constraints, number of runs, and any statistical tests for the reported superiority. This weakens the ability to evaluate the efficiency-performance trade-off claims.

Authors: We agree more implementation details are required for reproducibility. In the revised §4.1 we will add: exact baseline reimplementations (using the same Omni-LLM backbone and matching the original papers' token selection logic at equivalent ratios); frame-audio similarity defined as average cosine similarity between video frame embeddings and corresponding audio segment embeddings extracted from the model's encoders; the DP objective as maximizing the cumulative alignment score (sum of per-chunk similarities) subject to constraints of minimum one frame and one audio segment per refined chunk plus upper bounds on chunk duration to control compute; all results averaged over 3 random seeds with standard deviation reported; and paired t-tests confirming statistical significance (p < 0.05) versus baselines on WorldSense. These additions will allow readers to fully evaluate the trade-off claims. revision: yes

Circularity Check

0 steps flagged

No circularity in algorithmic derivation or claims

full rationale

The paper describes a training-free two-stage algorithmic procedure (Correspondence-Preserving Chunk Refinement via frame-audio similarity and dynamic programming, followed by Modality-Aware Cooperative Compression) evaluated directly on external benchmarks such as WorldSense. No equations, fitted parameters presented as predictions, self-definitional quantities, or load-bearing self-citations appear in the provided text that would reduce the reported performance retention (e.g., 46.7% at 44% retention) to an input by construction. The central claims rest on empirical results from independent datasets rather than any internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations, fitted constants, or new postulated entities are mentioned; the approach relies on standard similarity metrics and dynamic programming whose details are not supplied in the abstract.

pith-pipeline@v0.9.0 · 5530 in / 1112 out tokens · 59652 ms · 2026-05-13T04:46:50.439143+00:00 · methodology

discussion (0)

Reference graph

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Attention is all you need.Advances in neural information processing systems, 30, 2017

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Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

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Mini-omni2: Towards open-source gpt-4o with vision, speech and duplex capabilities.arXiv preprint arXiv:2410.11190, 2024

Zhifei Xie and Changqiao Wu. Mini-omni2: Towards open-source gpt-4o with vision, speech and duplex capabilities.arXiv preprint arXiv:2410.11190, 2024

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PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction

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Qwen2.5-Omni Technical Report

Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, and Junyang Lin. Qwen2.5-omni technical report, 2025. URLhttps://arxiv.org/abs/2503.20215

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Qwen3-Omni Technical Report

Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, Ting He, Xinfa Zhu, et al. Qwen3-omni technical report.arXiv preprint arXiv:2509.17765, 2025

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Topv: Compatible token pruning with inference time optimization for fast and low-memory multimodal vision language model

Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang, Yu Gong, Chendi Li, Jinghua Yan, Yu Bai, Ponnuswamy Sadayappan, Xia Hu, et al. Topv: Compatible token pruning with inference time optimization for fast and low-memory multimodal vision language model. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 19803–19813, 2025

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Humanomniv2: From understanding to omni-modal reasoning with context, 2025

Qize Yang, Shimin Yao, Weixuan Chen, Shenghao Fu, Detao Bai, Jiaxing Zhao, Boyuan Sun, Bowen Yin, Xihan Wei, and Jingren Zhou. Humanomniv2: From understanding to omni-modal reasoning with context.arXiv preprint arXiv:2506.21277, 2025

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Visionzip: Longer is better but not necessary in vision language models

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Audio-centric video understanding benchmark without text shortcut

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Omnivinci: Enhancing architecture and data for omni-modal understanding llm.arXiv preprint arXiv:2510.15870, 2025

Hanrong Ye, Chao-Han Huck Yang, Arushi Goel, Wei Huang, Ligeng Zhu, Yuanhang Su, Sean Lin, An-Chieh Cheng, Zhen Wan, Jinchuan Tian, et al. Omnivinci: Enhancing architecture and data for omni-modal understanding llm.arXiv preprint arXiv:2510.15870, 2025

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Fit and prune: Fast and training-free visual token pruning for multi-modal large language models

Weihao Ye, Qiong Wu, Wenhao Lin, and Yiyi Zhou. Fit and prune: Fast and training-free visual token pruning for multi-modal large language models. InProceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 22128–22136, 2025

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vllm-omni: Fully disaggregated serving for any-to-any multimodal models.arXiv preprint arXiv:2602.02204, 2026

Peiqi Yin, Jiangyun Zhu, Han Gao, Chenguang Zheng, Yongxiang Huang, Taichang Zhou, Ruirui Yang, Weizhi Liu, Weiqing Chen, Canlin Guo, et al. vllm-omni: Fully disaggregated serving for any-to-any multimodal models.arXiv preprint arXiv:2602.02204, 2026

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Lmms-eval: Reality check on the evaluation of large multimodal models

Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, et al. Lmms-eval: Reality check on the evaluation of large multimodal models. InFindings of the Association for Computational Linguistics: NAACL 2025, pages 881–916, 2025

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LLaVA-Video: Video Instruction Tuning With Synthetic Data

Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Llava- video: Video instruction tuning with synthetic data.arXiv preprint arXiv:2410.02713, 2024. 13 A Hyperparameter Settings To supplement the configuration details provided in the main text, Table 5 summarizes the compre- hensive hyperparameter settings utilized in the Omni...

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