Large language models have seen significant improvements in complex task handling with the introduction of Chain-of-Thought reasoning. However, when these models make mistakes, current interaction methods often require regenerating another response, which may also be erroneous, or manually identifying the faulty step, a process that can be time-consuming and inefficient. Researchers have proposed a new human-AI interaction method called Deep Interaction, designed to streamline the correction process for large reasoning models1. This approach aims to enhance the efficiency of human-AI collaboration by allowing for more precise error correction and reducing the need for repeated response generation. By improving the interaction between humans and AI models, Deep Interaction has the potential to increase the reliability and accuracy of large language models. This development matters to practitioners because it can lead to more effective and trustworthy AI systems, ultimately impacting various aspects of society, from technology and policy to security and workforce dynamics.