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arXiv · 2607.21179

Out of Sight, Still in Mind: Token Compression for Omni-LLMs

Suho Yoo·Youngjoon Jang·Hyebin Cho·Joon Son Chung
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Abstract

The goal of this paper is to reduce the input token cost of Omni-modal large language models (Omni-LLMs) at inference time. Omni-LLMs reason jointly over audio, video and text, but the cost of the three streams is highly unbalanced: visual tokens account for the vast majority of the input, and are highly redundant. In this paper, we propose ReMo, a training-free framework that compresses visual tokens by redistributing their information across modalities: a visual token is kept only if its information appears nowhere else. ReMo achieves this in two ways: (i) it aligns audio and video in a common embedding space, and removes visual tokens already explained by the audio or by other visual tokens; and (ii) it replaces object-level visual tokens with compact text proxies, short descriptions of each object and its location, conveying the same content in far fewer tokens. On Qwen2.5-Omni at two model scales, ReMo removes 54% of the input tokens with no loss in accuracy. Indeed, it slightly exceeds the full-token model, reaching 101.2% and 101.3% of its average accuracy over five audio-visual benchmarks.

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Suho Yoo, Youngjoon Jang, Hyebin Cho, Joon Son Chung. 2026-07-23. Out of Sight, Still in Mind: Token Compression for Omni-LLMs. https://arxiv.org/abs/2607.21179

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