{"ID":5935870,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03050","arxiv_id":"2607.03050","title":"OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models","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. Existing audio-visual token compression methods often rely on unimodal guidance, overlooking the temporal locality of query-relevant evidence in audio-visual inputs and implicitly assuming that the two modalities share a temporally aligned information density distribution. We propose \\textbf{OmniFocus}, a training-free query-guided token compression method for OmniLLMs that performs independent importance estimation for video and audio, enabling a modality-symmetric compression design that preserves modality-specific salient evidence while maintaining audio-visual alignment, thereby mitigating the modality bias issue that can arise from unimodal-guided compression. Experiments on the Qwen2.5-Omni model family across four audio-visual benchmarks show that OmniFocus maintains strong compressed performance at low token retention ratios and outperforms existing baselines on several major benchmark scores at 25\\% token retention. On DailyOmni with Qwen2.5-Omni-7B at 25\\% token retention, OmniFocus maintains 59.40 accuracy while delivering up to 1.38$\\times$ prefill speedup relative to the full-token baseline, highlighting a favorable practical accuracy-efficiency trade-off.","short_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. Existing audio-visual token compression methods often rely on unimodal guidance, overl...","url_abs":"https://arxiv.org/abs/2607.03050","url_pdf":"https://arxiv.org/pdf/2607.03050v1","authors":"[\"Shijie Cao\",\"Qingyu Zhang\",\"Boxi Yu\",\"Yuzhong Zhang\",\"Boxi Cao\",\"Yaojie Lu\",\"Hongyu Lin\",\"Xianpei Han\",\"Le Sun\"]","published":"2026-07-03T07:41:00Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CV\",\"cs.SD\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}