Definition
Latent Space Geometry refers to the structural properties of a learned latent space — how smoothly, regularly, and semantically meaningfully points are arranged — as opposed to merely whether a decoder can reconstruct inputs from those points. A latent space can achieve near-perfect reconstruction while still being poorly organized for generation, meaning a diffusion model traversing it will produce incoherent outputs; shaping this geometry, not just reconstruction fidelity, is therefore the key lever for high-quality generative modeling.