Multimodal large language models are prone to producing overconfident predictions and hallucination-like outputs when visual evidence is weak or ambiguous. To address this issue, researchers have proposed a retrieval-augmented reliability-aware inference approach, which aims to mitigate visual hallucinations in these systems. This method focuses on improving the reliability of multimodal representations, particularly in situations where visual and language cues are inconsistent. By incorporating retrieval mechanisms into the inference process, the model can better assess the confidence of its predictions and reduce the likelihood of hallucinations. The proposed approach has significant implications for the development of more robust and trustworthy multimodal systems, as it can help to help to prevent the spread of misinformation and improve the overall performance of these models. This matters to practitioners because it can help to establish more reliable AI systems, reducing potential security risks and errors, according to recent research1.