The Universal Corrector LURE, developed collaboratively by researchers from universities such as UNC Chapel Hill and Stanford, has been released with the aim of addressing the issue of object hallucinations in multi-modal large models. LURE reduces these hallucinations by statistically analyzing key factors such as object co-occurrence, uncertainty, and object position, thereby improving the general object hallucination evaluation metrics. This tool is expected to have a positive impact in AI applications, providing more accurate outputs.