Large vision-language models have achieved remarkable success in vision-language tasks. However, they remain prone to Visual Hallucinations (VHs), undermining their reliability in real-world applications. Existing solutions typically require curated datasets, additional training, or multi-round decoding, resulting in considerable efficiency overhead. In this paper, we propose **RVSD** (**R**etrieval **V**ision **S**parse **D**ecoding), a training-free and plug-and-play decoding framework that, for the first time, unifies token sparsification and **Semantic-Space Visual Retrieval** (SSVR) within a single decoding pass. Within RVSD, we introduce a **semantics-directed token selection** strategy that selectively sparsifies redundant tokens while preserving critical visual information. We further propose the SSVR mechanism, which reformulates visual compensation as an on-demand cross-modal retrieval process within a shared semantic space. Extensive experiments demonstrate that RVSD achieves state-of-the-art performance in mitigating VHs while maintaining robust suppression capabilities under long-context generation settings. Our code is available at https://github.com/canjie-liu/RVSD.