I'm working on a conceptual framework for AI efficiency called The Monopoint Tension Theory. It uses non-Euclidean, hyperbolic geometry to treat correlated data vectors as a single dilated topological point. The goal is to optimize neural networks using tension-based metrics instead of traditional back propagation, significantly reducing energy costs. I'd love to chat about the math needed to test this.
