Stage-adaptive Token Selection for Efficient Omni-modal LLMs
The unified audio-visual understanding capability of omni-modal large language models (om-LLMs) comes at the high cost of processing tens of thousands of visual and audio tokens throughout the LLM. To reduce the cost, training-free methods for om-LLM token selection are being actively developed, operating either pre-LLM or inner-LLM. Recognizing that the non-textual token redundancy varies across stages and LLM layers, we propose in this paper a novel stage-adaptive token selection (SEATS) method that operates in both pre-LLM and inner-LLM stages. Given an overall token budget measured by the token retention rate (TRR), SEATS performs top-down token budget allocation in an input-dependent...
| Authors | Zijie Xin, Jie Yang, Ruixiang Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 May 2026 |
| Last updated at source | 27 September 2026 |
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