Author

Zhangquan Chen

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Preprint Sep 2026

EviViT: Evidence-Adaptive Vision Transformers for Fine-Grained Perception

Fine-grained visual perception enables vision-language models to distinguish subtle attributes and ground their answers in visual evidence. In high-resolution scenes, processing the whole image at greater resolution spends visual tokens on irrelevant content, while isolated crops can lose the context needed to interpre...

Yao-Xin Niu, Zhangquan Chen, Yang Zhang et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Evidence-RL: Towards Evidence-intensive Visual Reasoning

Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context. Existing perception-aware post-training methods encourage image use through global perturbations or attention proxies, but they do not test whether a sampled answer caus...

Haojie Huang, Xin-Lei Yu, Cheng-Ming Xu et al. · 2 citations

#machine learning Preprint Sep 2026

Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification

While Reinforcement Learning (RL) effectively incentivizes reasoning in Large Language Models, current pipelines are hindered by training instability and rapid entropy collapse. These limitations often stem from"Rollout Silencing"and low-quality gradient signals in standard sampling procedures. In this work, we propose...

Yi-Meng Ye, Shuang Chen, Wen-Xuan Huang et al. · 0 citations