The recent advancements in the field of Vision-Language Pretraining (VLP) and Large Vision-Language Models (LVLMs) have shown a significant shift towards enhancing fine-grained understanding and complex visual reasoning. Researchers are increasingly focusing on developing models that can capture nuanced distinctions between visual and linguistic features, which is crucial for tasks requiring detailed perception. Techniques such as Negative Augmented Samples (NAS) and Visual-Aware Retrieval-Augmented Prompting (VRAP) have been introduced to address the limitations of existing models in handling unseen objects and capturing fine-grained relationships in complex scenes. Additionally, the integration of retrieval-augmented tags and contrastive learning has been shown to improve the accuracy and efficiency of multimodal reasoning. The introduction of novel benchmarks like CoMT and VGCure highlights the need for models that can perform multi-modal reasoning and fundamental visual graph understanding. These developments not only advance the capabilities of LVLMs in solving complex computing problems but also have implications for pedagogy and assessment practices. Notably, the use of large multimodal models to solve visual graph and tree data structure problems has been explored, demonstrating the potential of these models in educational settings. Overall, the field is moving towards more sophisticated models that can handle intricate visual and linguistic tasks with greater precision and efficiency.