arXiv AI June 9, 2026 By Guangzhi Sun, Yixuan Li, Yudong Yang, Chao Zhang

OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs

arXiv:2606. 07577v1 Announce Type: new Abstract: Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited by the linear growth of video tokens and key-value (KV) caches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 29

OmniDelta: Skill-Driven Budget Allocation for Token Compression in OmniLLMs

arXiv:2607. 25669v1 Announce Type: new Abstract: Emerging Omni-modal Large Language Models (OmniLLMs) enable unified understanding of text, audio, and video, but their long audio-video token sequences introduce substantial memory and inference costs.

By Haoyang Huang, Wenjie Huang, Tianqi Xu, Hongyaoxing Gu, Kang Tan, Yikai Fu, Yuhao Shen, Tianyu Liu, Baolin Zhang, Jun Zhang, Xinyi Hu, Jun Dai, Shuang Ge, Lei Chen, Yue Li, Mingchen Wang, Meng Zhang

llms efficiency benchmarks

arXiv Computer Vision

4d ago

OmniRoute: Mapping Temporal Semantic Evidence to Audio-Visual Token Budgets for Efficient Omnimodal Large Language Models

arXiv:2609.37052v1 Announce Type: new Abstract: Omnimodal large language models (Omni-LLMs) encode audio and visual streams into temporally interleaved token sequences for multimodal reasoning. Howev...

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

llms multimodal benchmarks

arXiv AI

Aug 11

Deferred Audio Pruning with Local Audio-Visual Dynamics for Omni-LLMs

arXiv:2608. 08794v1 Announce Type: new Abstract: Omni-modal LLMs jointly process audio, video, and text, but long multimodal sequences incur substantial prefill and KV-cache costs.

By Kyeongyoon Lee, Hongyeob Kim, Youngeun Kim, Sungeun Hong

llms efficiency multimodal benchmarks

arXiv AI

Jul 7

OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models

arXiv:2607. 03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-visual inputs, leading to substantial inference cost.

By Shijie Cao, Qingyu Zhang, Boxi Yu, Yuzhong Zhang, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

llms multimodal benchmarks safety

arXiv AI

Aug 11

Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models

arXiv:2608. 09227v1 Announce Type: new Abstract: Omnimodal language models (OLMs) enable unified audio-visual understanding, but processing long joint token sequences makes inference computationally prohibitive.

By Puneet Mathur, Manan Suri, Dinesh Manocha

llms nlp fine-tuning efficiency multimodal benchmarks

arXiv Computation and Language

Sep 10

Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

arXiv:2609.10355v1 Announce Type: cross Abstract: Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained lar...

By Killian Steunou, Yannis Tevissen, Moun\^im A. El Yacoubi

llms nlp

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Related stories

OmniDelta: Skill-Driven Budget Allocation for Token Compression in OmniLLMs

OmniRoute: Mapping Temporal Semantic Evidence to Audio-Visual Token Budgets for Efficient Omnimodal Large Language Models

Deferred Audio Pruning with Local Audio-Visual Dynamics for Omni-LLMs

OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models

Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models

Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs