arXiv AI September 3, 2026 By Canjie Liu, Jiawen Kang, Jinbo Wen, Zishao Zhong

RVSD: Retrieval Vision Sparse Decoding for Mitigating Visual Hallucinations in Large Vision-Language Models

The paper introduces RVSD, a training‑free, plug‑and‑play decoding framework that combines token sparsification with Semantic‑Space Visual Retrieval (SSVR) to reduce visual hallucinations in large vision‑language models. RVSD employs a semantics‑directed token selection strategy to prune redundant tokens while preserving essential visual information, and uses SSVR to perform on‑demand cross‑modal retrieval within a shared semantic space. Experiments show that RVSD achieves state‑of‑the‑art performance in mitigating visual hallucinations while maintaining strong suppression in long‑context generation.

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