Imagine chatting with an AI that responds as you type, almost like a human conversation. That's the promise of LiveMind, a new framework designed to drastically reduce the latency of large language models (LLMs). Current LLMs wait for you to finish your entire input before processing and responding. This "waiting" period, especially for complex questions requiring multi-step reasoning, creates a delay that hinders seamless human-AI interaction. LiveMind addresses this bottleneck by allowing LLMs to process input *as it streams*, character by character, like a person listening while you speak. It leverages periods when the model would typically be idle—while you’re typing or speaking—to perform preliminary inferences in the background. By the time you finish your input, LiveMind has already done most of the heavy lifting, enabling a near-instantaneous final response. The research demonstrates impressive speed gains—up to 6.3x faster on challenging question-answering tasks—without compromising accuracy. LiveMind’s innovative approach includes a clever "inference memory" that stores intermediate reasoning steps. The framework efficiently segments your input, typically by sentence or clause, capturing meaningful chunks of information for the model to process incrementally. The framework was tested with various leading LLMs, including both open-source and commercial models, showing consistent performance improvements. Even more impressively, LiveMind allows for "collaborative inference," where a powerful, larger LLM can perform the background reasoning, while a smaller, faster model focuses on generating the quick final output. This synergy boosts the performance of the smaller model while maintaining impressive speed. This research opens exciting doors for real-time, interactive AI applications, from chatbots that are truly conversational to real-time translation and voice assistants that keep pace with our thoughts. While the research primarily focused on text-based interactions, extending this approach to audio input holds immense potential. Imagine a voice assistant that anticipates your needs even before you finish your sentence, making human-computer interaction more intuitive than ever. As research continues, addressing the increased computational costs associated with finer-grained input segmentation will be key to unlocking LiveMind’s full potential. LiveMind’s ability to process streaming inputs, combined with the model-collaboration approach, represents a fundamental shift toward more natural, responsive, and human-centered AI interactions. It’s a significant step towards making AI feel less like a machine and more like a true conversational partner.