Exploring the Future: Could Large Language Models (LLMs) Reason in Pure Vector Space?

This fascinating discussion highlights a thrilling frontier in AI research: moving beyond text-based Chain of Thought to unlock intuitive, vector-based reasoning within Large Language Models (LLMs). By exploring how models might process logic internally using high-dimensional Embeddings before outputting natural language, we could see massive leaps in processing speed and compression. It opens up an incredible opportunity to build AI that thinks faster while maintaining the accessibility of natural language outputs.