Large Language Models (LLMs) have made incredible strides, generating human-like text and even creating art. But beneath the surface, a fundamental challenge remains: true reasoning. While LLMs excel at mimicking human language, they often struggle with logical problems and mathematical reasoning, relying more on pattern recognition than genuine understanding. New research explores a novel approach to bridge this reasoning gap: using the power of argumentation. The "Critical-Questions-of-Thought" (CQoT) technique introduces a structured way to challenge an LLM's thinking process. Inspired by the way humans debate and refine ideas, CQoT presents the LLM with a series of critical questions, forcing it to examine the basis of its reasoning. These questions probe for logical fallacies, unsupported premises, and inconsistencies, pushing the model to justify its conclusions. The results are promising. Across a range of LLMs, from open-source models like Llama to proprietary giants like GPT-4, the CQoT method demonstrably improves performance on reasoning and math tasks. By prompting the models to think critically about *how* they arrived at an answer, CQoT helps them correct errors and produce more accurate and logically sound results. This innovative approach, however, isn't without its limitations. The process can be time-consuming, as the back-and-forth questioning adds to the computational overhead. There are also open questions about its scalability with smaller models. Furthermore, while CQoT helps refine existing knowledge within the model, it doesn't magically imbue the LLM with entirely new reasoning capabilities. The future of CQoT lies in integrating it with other emerging techniques, such as test-time training, to further unlock the reasoning potential of LLMs. This research opens exciting avenues for building more robust and reliable AI systems, capable not just of mimicking human language, but also of thinking critically and solving complex problems with genuine understanding.