3 citations · 3 across the 12 of their papers we have counts for
4 papers · 1 filter
Cosine Misleads: Auxiliary Losses Reshape Vision Language Models, Not Their Latents
XiuYu Zhang, Junfeng Fang, Zhenkai Liang
Latent visual reasoning (LVR) inserts supervised latent tokens between perception and answer generation in vision-language models (VLMs). The field uses alignment between these lat…
UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs
Houcheng Jiang, Jiajun Fu, Junfeng Fang +4
Multimodal large language models are increasingly expected to perform thinking with images, yet existing visual latent reasoning methods still rely on explicit textual chain-of-tho…
MIRL: Mutual Information-Guided Reinforcement Learning for Vision-Language Models
Yin Zhang, Jiaxuan Zhao, Zonghan Wu +5
Vision-Language Models (VLMs) frequently suffer from visual perception errors and hallucinations that compromise answer accuracy in complex reasoning tasks. Reinforcement Learning…
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation
Zhenglin Hua, Jinghan He, Zijun Yao +4
Large vision-language models (LVLMs) have achieved remarkable performance on multimodal tasks. However, they still suffer from hallucinations, generating text inconsistent with vis…