The field of financial applications is witnessing a significant shift towards the use of large language models (LLMs) for tasks such as fund investment benchmarking, alpha factor mining, and algorithmic trading. Researchers are working to overcome the limitations of traditional approaches, including the reliance on historical back-testing and the lack of interpretability in automated methods. A key direction in this field is the development of frameworks that integrate LLMs with other techniques, such as Monte Carlo Tree Search, to improve the efficiency and effectiveness of financial decision-making. Another important trend is the move towards openness and collaboration, with initiatives such as open-source platforms for algorithmic trading. Noteworthy papers in this area include: Navigating the Alpha Jungle, which introduces a novel framework for formulaic factor mining using LLMs and MCTS, demonstrating superior predictive accuracy and interpretability. PLUTUS Open Source, which presents an initiative to create a transparent and inclusive ecosystem for algorithmic trading through reproducibility, standardization, and shared infrastructure.