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cs.CV2026
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
Xingyu Zhu, Junfeng Fang, Shuo Wang +4
Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine-tuning on annotated dat…
cs.CV2026
Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigation
Xingyu Zhu, Kesen Zhao, Liang Yi +4
Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language reasoning, yet they remain vulnerable to hallucination, where generated content deviat…