The field of research automation is experiencing a significant shift with the integration of Large Language Models (LLMs). Recent developments have demonstrated the potential of LLMs to automate complex tasks, reducing the need for manual intervention and increasing efficiency. LLMs are being applied in various domains, including electronic design automation, patient outcomes labeling, and materials discovery, to name a few. The use of LLMs is enabling the automation of tasks such as transistor sizing, literature review, and data analysis, allowing researchers to focus on higher-level tasks. Noteworthy papers in this area include EEsizer, which proposes an LLM-based AI agent for sizing of analog and mixed signal circuits, and EpidemIQs, which introduces a multi-agent LLM framework for epidemic modeling and analysis. Overall, the integration of LLMs in research automation is advancing the field by reducing costs, increasing accuracy, and enhancing accessibility to advanced modeling tools.