Recent research has unveiled significant advancements in the understanding and evaluation of Large Language Models (LLMs). A new theoretical framework models LLMs as finite-state Markov chains, providing insights into their probabilistic and memorization-influenced reasoning capabilities, including Chain of Thought and next-token-prediction in solving shift-cipher problems. Additionally, CodeMMLU, a comprehensive multiple-choice question-answering benchmark, has been introduced to evaluate code understanding in LLMs, covering over 10,000 questions across diverse domains and programming languages. This benchmark reveals limitations in state-of-the-art models' code comprehension. Furthermore, TurtleBench offers a dynamic evaluation approach, focusing on reasoning over knowledge recall, addressing the shortcomings of existing static datasets.