StepPrune: Adaptive Sequential Visual Token Selection across Multimodal Large Language Models
Visual prefixes account for a major portion of the per-layer computation in multimodal large language models (MLLMs), making visual-token pruning a direct approach to accelerating inference. Existing top-K methods typically evaluate tokens independently and apply a uniform budget to all inputs, overlooking both selection-dependent interactions and variations in visual complexity across samples. In contrast, we propose StepPrune, which formulates visual-token pruning as an adaptive sequential decision process. Conditioned on previously selected tokens and textual context, StepPrune progressively constructs the retained subset and automatically determines its size through a learned STOP act...
| Authors | Hansen Zhang, Landi He, Mingde Yao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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