The recent developments in the application of Large Language Models (LLMs) to scientific workflows and literature analysis have shown promising yet challenging results. LLMs are being increasingly explored for their potential in automating and enhancing various aspects of scientific research, including configuring and explaining scientific workflows, conducting literature reviews, and generating academic texts. However, the current capabilities of LLMs in these areas are mixed, with notable struggles in tasks requiring deep domain-specific knowledge and nuanced understanding. For instance, LLMs often face difficulties in accurately generating references and maintaining factual consistency in literature reviews. Additionally, while LLMs can imitate certain aspects of human writing processes, they still fall short in producing texts that are cohesive and coherent, particularly when addressing sensitive topics. Despite these challenges, the introduction of frameworks like CEKER for literature analysis and the Chain-of-MetaWriting method for text generation represent significant strides towards leveraging LLMs for more efficient and scalable research processes. These innovations highlight the need for continued refinement and adaptation of LLMs to better align with the complexities of scientific and academic tasks.