[2602.01047] Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.01047 (cs) [Submitted on 1 Feb 2026 (v1), last revised 24 Mar 2026 (this version, v3)] Title:Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance Authors:Xinrong Chen, Xu Chu, Yingmin Qiu, Hengyuan Zhang, Jing Xiong, Shiyu Tang, Shuai Liu, Shaokang Yang, Cheng Yang, Hayden Kwok-Hay So, Ngai Wong View a PDF of the paper titled Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance, by Xinrong Chen and 10 other authors View PDF HTML (experimental) Abstract:Large Vision-Language Models (LVLMs) can reason from image-text inputs and perform well in various multimodal tasks. Despite this success, they are affected by language priors and often produce hallucinations. Hallucinations denote generated content that is grammatically and syntactically coherent, yet bears no match or direct relevance to visual input. To address this problem, we propose Residual Decoding (ResDec). It is a novel training-free method that uses historical information to aid decoding. The method relies on the internal implicit reasoning mechanism and token logits evolution mechanism of LVLMs to correct biases. Extensive experiments demonstrate that ResDec effectively suppresses hallucinations induced by language priors, significantly improves visual grounding, and reduces object hallucinations. In addition to mitigating hal...