The field of financial forecasting and decision-making is witnessing significant advancements with the integration of innovative machine learning models and techniques. Researchers are exploring the application of transformer-based approaches, such as Informer, to improve option pricing accuracy and adaptability. Ensemble learning methods are also being investigated for their potential to enhance predictive accuracy and robustness in financial forecasting. Furthermore, the development of regulation knowledge-enhanced large language models, like RKEFino1, is addressing critical accuracy and compliance challenges in digital regulatory reporting. Another notable trend is the use of imitation learning frameworks, such as flowOE, to optimize execution strategies in dynamic financial markets. Noteworthy papers include Applying Informer for Option Pricing, which demonstrates the effectiveness of Informer in option pricing, and FlowOE, which proposes a novel imitation learning framework for optimal execution. Additionally, FinanceReasoning is introduced as a benchmark for evaluating the reasoning capabilities of large reasoning models in financial numerical reasoning problems. Overall, these advancements are contributing to more accurate and robust financial forecasting and decision-making systems.