Imaginative Generative AI: Crossing the Entropy Wall into Worlds Beyond Imitation
Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator nor defines how generation should extend beyond the diversity of the data itself. We introduce Imaginative Generative AI (IGA), a framework that makes diversity part of the target-distribution design problem: among distributions close to a reference, IGA selects one whose spectral diversity reaches a prescribed level. Diversity is measured by the von Neumann entropy of the generated distribution's kernel covariance operator in a fixed representation space, providing a reference-free representation-guided measure of how broadly probability m...
| Authors | Farzan Farnia, Hossein Goli, Amin Gohari |
|---|---|
| Primary category | cs.LG |
| Published | 10 August 2026 |
| Last updated at source | 11 August 2026 |
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