The field of scientific research is witnessing a significant shift towards AI-driven approaches, with a focus on harnessing the power of large language models (LLMs) to enhance various aspects of the research process. One notable trend is the use of LLMs to improve academic writing, with studies investigating the impact of AI-assisted generative revisions on research manuscripts and the potential for LLMs to drive convergence in academic writing styles. Another area of research is the application of LLMs to literature reviews, where they can be used to extract qualitative insights from large volumes of research content and identify critical research gaps. Furthermore, there is a growing interest in using AI models to analyze and visualize scientific literature, with the development of frameworks such as Science Hierarchography, which aims to organize scientific literature into a hierarchical structure. In the realm of weather forecasting, researchers are exploring the potential of AI models to improve prediction accuracy and efficiency. This includes the development of new paradigms such as the use of multimodal transformers to integrate observational data from different perspectives, as well as the application of neural networks to accelerate radiative transfer modeling. Noteworthy papers in this area include the introduction of a hybrid framework for literature reviews that combines traditional bibliometric methods with LLMs, and the development of a novel approach to identifying individual contributions in scientific papers using author-specific LaTeX macros as writing signatures. The paper on Science Hierarchography is also noteworthy, as it demonstrates the potential of LLMs to organize scientific literature into a hierarchical structure and provide insights into the density of activity in various scientific subfields.