Author

Minjing Dong

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Preprint Sep 2026

Region-Level Policy Optimization for Fine-grained MLLM Perception

Fine-grained visual perception in MLLMs is commonly improved by raising the resolution, but the added visual tokens inflate vision-encoding and language-model prefilling costs. We show that the two operations underlying fine-grained perception, localizing the region of interest (RoI) and recognizing its content, have d...

Yuheng Shi, Xiao-Huan Pei, Min-Jing Dong et al. · 0 citations

Preprint Aug 2026

RoRA: Role-Oriented Regional Allocation for Visual Token Pruning in MLLMs

RoRA is a training-free framework that casts visual token pruning as role-oriented regional evidence allocation, and consistently outperforms strong training-free baselines across LLaVA and Qwen-VL families, retaining most of the unpruned accuracy even at aggressive pruning ratios.

Qiyanhui Lu, Han Wu, Rong-Jia Xu et al. · 1 citation