The field of large language models (LLMs) is rapidly evolving, with significant advancements in education and finance. Researchers are exploring the potential of LLMs to enhance automated scoring, improve university admission prediction, and provide personalized financial guidance. The use of multi-agent frameworks, structured component recognition, and commonsense reasoning is becoming increasingly popular in these applications. Notably, small proprietary models are being developed to surpass large language models in specific domains, such as financial transaction understanding. The importance of model selection, architecture, and vendor is also being emphasized. Overall, the field is moving towards more interpretable, reliable, and scalable solutions. Noteworthy papers include: AutoSCORE, which proposes a multi-agent LLM framework for automated scoring, enhancing interpretability and robustness. Fin-Ally, which introduces a unified model that integrates commonsense reasoning and human-like conversational dynamics for personalized financial guidance. Better with Less, which demonstrates the potential of small proprietary models to surpass large language models in financial transaction understanding.