---
name: journal-arxiv-ai-omnimem-perturbationaware-memory-20260609
description: >
  学术期刊学习笔记：arXiv AI - OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs
  蒙多每日期刊学习系统自动提取。
version: 1.0.0
author: mundo-journal-bot
priority: LOW
auto_activate: MANUAL
category: journal-learnings
source: arXiv AI
published: Tue, 09 Jun 2026 00:00:00 -0400
learned: 2026-06-09
---
# OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs

**来源**: [arXiv AI](https://arxiv.org/abs/2606.07577)
**学习日期**: 2026-06-09

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## 摘要

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. We present OmniMem, a memory-efficient streaming framework designed specifically for audio-visual LLMs. Unlike existing compression methods that treat all tokens uniformly, OmniMem introduces a modality-aware memory allocation strategy

## 关键发现

1. 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

## 蒙多笔记

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