Recent research has introduced several innovative frameworks aimed at enhancing the reasoning capabilities of large language models (LLMs). One notable framework, Multimodal Visualization-of-Thought (MVoT), allows AI models to process and generate visual representations alongside text, effectively enabling a dual-mode of reasoning. This advancement builds upon traditional Chain-of-Thought prompting, enhancing the models' ability to think visually. Additionally, Meta has released a new framework known as Meta Chain-of-Thought (Meta-CoT), which aims to improve AI reasoning by modeling reasoning paths through process supervision and synthetic data. These developments signal a shift towards more advanced, human-like reasoning in AI applications.