Harnessing the Universal Geometry of Embeddings

Neural representations learned by diverse architectures frequently converge toward shared geometric configurations. This paper investigates the universal geometric structures governing latent embedding spaces, examining how intrinsic symmetries and manifold properties can be systematically exploited. By formalizing these shared spatial distributions, the work points toward unified methods for cross-model alignment and semantic transfer without the need for expensive remapping or extensive auxiliary training.