Author

Jufeng Yang

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Preprint Oct 2026

Look Back, Think Ahead: Visual Memory on Demand for Efficient Multimodal Reasoning

Processing long visual token sequences from high-resolution images makes multi-step reasoning computationally expensive for multimodal Large Language Models (MLLMs). Existing one-shot pruning and aggregation methods compress visual tokens into a fixed context before decoding. However, visual evidence needs can shift as...

Yi-Cheng Xue, Han Wu, Ju-Feng Yang 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