Stepwise Token Selection for Efficient Multimodal Large Language Models
In multimodal large language models (MLLMs), inference cost is largely dominated by the visual token prefix rather than the language backbone, making token reduction a key factor for improving efficiency. Existing approaches typically assign independent importance scores to visual tokens and retain a fixed number of top-ranked tokens, implicitly assuming token independence and a uniform compression ratio across inputs. In this work, we reformulate visual token pruning as a sequential decision-making process. Specifically, we introduce a pointer-style selection mechanism that iteratively chooses informative tokens, conditioning each decision on previously selected ones, and dynamically det...
| Authors | Landi He, Shawn Young, Lijian Xu |
|---|---|
| Primary category | cs.CV |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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