Recent discussions and research highlight the limitations of Large Language Models (LLMs) in reasoning and mathematical tasks. A new benchmark developed by researchers, including DCasBol and thefillm, exposes that most LLMs, including OpenAI's o1-mini, start erring after just two operations in sequential reasoning tasks. Apple's latest publication supports these findings, indicating that LLMs rely more on sophisticated pattern matching rather than genuine reasoning. Smaller LLMs particularly struggle with complex mathematical reasoning due to their inability to detect and fix errors. However, a teacher-student framework and hierarchical thought templates have been proposed to enhance the reasoning capabilities of smaller models.