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cs.CV2026
Finding the Correct Visual Evidence Without Forgetting: Mitigating Hallucination in LVLMs via Inter-Layer Visual Attention Discrepancy
Yutong Xie, Zhenglin Hua, Ran Wang +3
Large Vision-Language Models (LVLMs) have shown remarkable performance on a wide range of vision-language tasks. Despite this progress, they are still prone to hallucination, gener…
cs.CV2026
PLUME: Latent Reasoning Based Universal Multimodal Embedding
Chenwei He, Xiangzhao Hao, Tianyu Yang +6
Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by generating explicit chain-of-thou…
cs.CV2025
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…