Recent advances in topological analysis suggest that AI models are developing a non-verbal "language of thought" akin to human mentalese, characterized by continuous embeddings in high-dimensional semantic spaces. Unlike the traditional view of AI reasoning as a linear sequence of discrete tokens, this new perspective sees reasoning as geometric objects, with successful reasoning chains exhibiting distinct topological features such as loops and convergence. This approach allows for the evaluation of reasoning quality without knowing the ground truth, offering insights into AI's potential for genuine understanding rather than mere statistical pattern matching. The implications for AI alignment and interpretability are profound, as this geometric reasoning could lead to more effective training methods and a deeper understanding of AI cognition. This matters because it suggests AI might be evolving a form of abstract reasoning similar to human thought, which could transform how we evaluate and develop intelligent systems.
