Stage-adaptive Token Selection for Efficient Omni-modal LLMs

Hugging Face Daily Papers Papers
token-selection efficiency omni-modal large-language-models inference-speedup cross-modal-fusion

Summary

SEATS is a training-free, stage-adaptive token selection method that reduces computational overhead in omni-modal LLMs by progressively pruning redundant visual and audio tokens, achieving a 9.3x FLOPs reduction and 4.8x prefill speedup while preserving 96.3% performance.

Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequences interleaved at the window level. However, processing these dense non-textual tokens throughout the LLM incurs substantial computational overhead. Although training-free token selection can reduce this cost, existing methods either focus on visual-only inputs or prune om-LLM tokens only before the LLM with fixed per-modality ratios, failing to capture how cross-modal token importance evolves across layers. To address this limitation, we first analyze the layer-wise token dependency of om-LLMs. We find that visual and audio dependencies follow a block-wise pattern and gradually weaken with depth, indicating that many late-layer non-textual tokens become redundant after cross-modal fusion. Motivated by this observation, we propose SEATS, a training-free, stage-adaptive token selection method for efficient om-LLM inference. Before the LLM, SEATS removes spatiotemporal redundancy via attention-weighted diversity selection. Inside the LLM, it progressively prunes tokens across blocks and dynamically allocates the retention budget from temporal windows to modalities using query relevance scores. In late layers, it removes all remaining non-textual tokens once cross-modal fusion is complete. Experiments on Qwen2.5-Omni and Qwen3-Omni demonstrate that SEATS effectively improves inference efficiency. Retaining only 10% of visual and audio tokens, it achieves a 9.3x FLOPs reduction and a 4.8x prefill speedup while preserving 96.3% of the original performance.
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Cached at: 05/20/26, 02:38 PM

Paper page - Stage-adaptive Token Selection for Efficient Omni-modal LLMs

Source: https://huggingface.co/papers/2605.20035

Abstract

SEATS is a training-free, stage-adaptive token selection method that reduces computational overhead in om-LLMs by progressively pruning redundant visual and audio tokens during both pre-LLM and LLM stages.

Omni-modal large language models (om-LLMs) achieve unifiedaudio-visual understandingby encoding video and audio intotemporally aligned token sequencesinterleaved at the window level. However, processing these dense non-textual tokens throughout the LLM incurs substantialcomputational overhead. Although training-freetoken selectioncan reduce this cost, existing methods either focus on visual-only inputs or prune om-LLM tokens only before the LLM with fixed per-modality ratios, failing to capture howcross-modal token importanceevolves across layers. To address this limitation, we first analyze thelayer-wise token dependencyofom-LLMs. We find that visual and audio dependencies follow a block-wise pattern and gradually weaken with depth, indicating that many late-layer non-textual tokens become redundant aftercross-modal fusion. Motivated by this observation, we propose SEATS, a training-free, stage-adaptivetoken selectionmethod for efficient om-LLM inference. Before the LLM, SEATS removes spatiotemporal redundancy viaattention-weighted diversity selection. Inside the LLM, it progressively prunes tokens across blocks and dynamically allocates the retention budget from temporal windows to modalities usingquery relevance scores. In late layers, it removes all remaining non-textual tokens oncecross-modal fusionis complete. Experiments on Qwen2.5-Omni and Qwen3-Omni demonstrate that SEATS effectively improves inference efficiency. Retaining only 10% of visual and audio tokens, it achieves a 9.3xFLOPs reductionand a 4.8xprefill speedupwhile preserving 96.3% of the original performance.

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