Yanlin Liu — accepted AI-conference papers

2026

RAR: Reversing Visual Attention Re-Sinking for Unlocking Potential in Multimodal Large Language Models

Zhehan Kan, Xin Li, Yanlin Liu, Xiaochen Yang, Xinghua Jiang, Yinsong Liu +4

ICLR 2026poster

Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet they frequently exhibit suboptimal output layers, where intermediate decoder layers outperform the final ones, signaling underutilized model capacity. In this work, we delve into the root causes a…

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2026

UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm

Zhehan Kan, Xinghua Jiang, Yanlin Liu, Xiaochen Yang, Zhixiang Wei, Shifeng Liu +7

CVPR 2026

Despite remarkable advancements in multimodal large language models (MLLMs), their fine-grained visual understanding is constrained by a primary reliance on sparse textual supervision. Existing efforts to introduce visual supervision typically do so during post-training, when visual representations

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