Paper coming soon. Check back or search on Google Scholar.
Search on Scholar →Token compression in OmniLLMs is typically posed as a single saliency-ranking problem: score each multimodal token, keep the top-K. We argue this abstraction is mis-specified. The same attention score simultaneously decides two things: how much retained capacity each modality receives, and which tokens within a modality are kept. A shared top-K rule therefore inherits this audio-favoring allocation prior, spending retained capacity on audio before video tokens have a chance to compete. We propose Macer, a training-free compressor that first assigns explicit audio and video budgets, then performs allocation-normalized ranking within each modality at modality-specific shallow layers. Macer significantly reduces token cost while preserving accuracy across audio-grounded, audio-video joint, visual-dominant, and video-centric benchmarks. At 25% retention, Macer preserves 98.7% of full-token performance on Qwen2.5-Omni-7B and 97.3% on Qwen2.5-Omni-3B.