The field of financial network analysis and corporate credit scoring is witnessing significant developments with the integration of large language models and machine learning techniques. Researchers are exploring the application of these models to construct credit networks, analyze their structures, and determine optimal strategies for financial operations. The use of large language models is also being investigated for corporate credit scoring, combining structured financial data with advanced machine learning to improve predictive reliability and interpretability. Furthermore, the creation of curated multimodal datasets and panel datasets is providing valuable resources for researchers and practitioners to analyze and forecast stock movements and corporate tax avoidance. Noteworthy papers include: A Large Language Model for Corporate Credit Scoring, which introduces a framework that combines structured financial data with advanced machine learning to improve predictive reliability and interpretability. CSMD: Curated Multimodal Dataset for Chinese Stock Analysis, which proposes a multimodal dataset curated specifically for analyzing the Chinese stock market.