One Pass Is Not Enough: Recursive Latent Refinement for Generative Models

ArXi:2605.15309v1 Announce Type: new Despite remarkable progress, image generation is far from solved. The dominant metric, FID, conflates sample fidelity with mode coverage and is close to being saturated. Yet a model can still exhibit mode collapse while achieving a low FID, since a handful of sharp, near-duplicate images can outscore a model that faithfully covers the full data distribution. We argue that precision and recall are essential complements to FID, and that because FID is already saturated, the meaningful goal is to improve diversity and coverage.