Seeking the Unfamiliar but Memorable: Conceptual Creativity as Meta-Learning

What does it mean to create a new concept, rather than retrieve a familiar one? Diffusion models sampled many times at the same prompt vary only within the narrow stylistic envelope set by a fixed prompt embedding. We propose an operational definition of creativity as the generation of concepts that are initially unfamiliar yet rapidly learnable to an adaptive observer, and formalize it as a bilevel optimization between a Creator that generates and an Appraiser that adapts: the Appraiser's improvement under a brief inner-loop adaptation provides the reward signal that the Creator maximizes. We call this framework CAMEL (Creator-Appraiser Meta-Learning), and instantiate it on MNIST with an autoencoder Appraiser and on natural images with a CLIP Appraiser based on a low-rank adapter over the text projection. CAMEL produces outputs that lie outside the basin reachable by vanilla diffusion sampling at any sample count, while remaining recognizable as the prompt's class to an off-the-shelf observer.