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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.