Diffusion Models and Concept Formation
Humans organize knowledge into a taxonomy of concepts with nested levels of abstraction and a \emph{basic level} at which people recognize and name objects with the least cognitive effort. Cobweb is a classic cognitive account of this ability, an incremental learner that builds a probabilistic concept hierarchy by maximizing category utility. We argue that diffusion models, although designed for image synthesis, implicitly perform the same computation. The noisy marginals of a diffusion model are Gaussian smoothings of the data distribution, and the modes of these marginals form a hierarchy that corresponds to a Cobweb tree of probabilistic prototypes in four respects. Both are hierarchic...
| Authors | Zekun Wang, Karthik Singaravadivelan, Christopher J. MacLellan |
|---|---|
| Primary category | cs.AI |
| Published | 11 September 2026 |
| Last updated at source | 21 September 2026 |
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