ReTrace: Visual Backtracking for Efficient Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) encode images into dense visual token sequences, creating substantial attention and KV-cache overhead. Existing visual token compression methods reduce this cost by pruning low-saliency tokens in shallow layers, but this practice couples temporary computational deactivation with irreversible information deletion. We show that this coupling breaks an intrinsic inference pattern in MLLMs. Through layer-wise attention analysis, we identify \textbf{Visual Backtracking}: visual pivots remain spatially stable, yet in middle layers they actively retrieve context from tokens that appeared unimportant earlier. Thus, early low-saliency tokens can be inactive...
| Authors | Guangzong Si, Dong Wang, Yifan Yu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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