One potential limitation of the binarization approach is the loss of detailed information and representation capacity due to the extreme discretization of weights to 1-bit. This can lead to a degradation in performance, especially in tasks that require intricate details and fine-grained features. To address this limitation in future research, techniques can be developed to enhance the information retention in binarized models. This may involve exploring more sophisticated binarization methods that preserve essential details or incorporating additional mechanisms to compensate for the loss of information. Additionally, research can focus on optimizing the training process further to mitigate the impact of binarization on model performance. By addressing these limitations, future studies can improve the accuracy and effectiveness of binarized models in various applications.