The recent advancements in the field of visualization and data communication are significantly enhancing the efficiency and effectiveness of information processing and interpretation. Researchers are increasingly focusing on developing frameworks that leverage machine learning and reinforcement learning to optimize the computational efficiency of visualization models, enabling their application to large datasets without compromising on accuracy. The integration of vision-language models is also being explored to automate the creation of effective visual communication designs, addressing the limitations of traditional generative models in simplifying complex ideas into clear visuals. Additionally, collaborative frameworks are being proposed to improve the quality and efficiency of visual information transmission, leveraging advanced segmentation networks and large-language models to generate comprehensive visual analytics. Interactive visual tools are being developed to assist viewers in online mental health communities, enhancing the support-seeking experience through simplified and engaging interfaces. The robustness of vision-language models in medical tasks is being systematically evaluated, with a focus on real-world data shifts and semantic evaluation to ensure safe deployment. Furthermore, the impact of guidance in data visualization systems for domain experts is being studied to optimize the communication of insights. The design and implementation of notional machines for educational purposes are also advancing, providing a coherent mental model for complex computational concepts. Lastly, the development of lightweight diagramming languages for formal methods is being grounded in cognitive science to enhance the effectiveness of visual reasoning.