The first notebook focuses on citation analysis, employing fuzzy logic distillation with large language models (LLMs) like Grok and Claude. It follows a coherent structure, starting with data loading, moving through preprocessing, performing LLM-driven analysis, and concluding with visualization. The code is well-organized, featuring modular functions for data handling, text analysis with LLMs, and visualization using tools like SHAP for feature importance. This clarity makes it easy to follow and replicate. However, the inclusion of a Deep Double Q-Network (DDQN) implementation feels incomplete, appearing more as a placeholder than a fully integrated component. While the causal inference and visualization aspects function effectively, the DDQN’s unclear role slightly undermines the notebook’s overall functionality. For users interested in citation analysis, the notebook delivers a solid foundation, but the RL component’s lack of integration could confuse those expecting a comprehensive Reinforcement Learning (RL) application.