The field of research automation is witnessing significant advancements with the integration of large language models (LLMs). Recent developments indicate a shift towards leveraging LLMs for automating complex tasks such as workflow generation, schema discovery, and causal loop diagram creation. These innovations have the potential to transform the research landscape by improving efficiency, reducing manual effort, and enhancing the quality of research outputs. Noteworthy papers in this area include one that introduces a method for automating the translation of dynamic hypotheses into causal loop diagrams using LLMs with curated prompting techniques, achieving results comparable to expert-built diagrams. Another paper presents a framework for generating structured workflow outputs from sketch images using vision-language models, demonstrating improved performance over large vision-language models. Additionally, a survey highlights the potential of LLM-based scientific agents to automate critical tasks in scientific research, driving breakthroughs and advancing discovery.