The paper argues that diffusion models, originally developed for image synthesis, implicitly perform concept formation similar to the Cobweb cognitive model. Both models build hierarchical density structures using Gaussian prototypes, treat categorization as score‑following to reduce uncertainty, and exhibit a basic level of abstraction. The authors demonstrate this correspondence by extracting a diffusion hierarchy from MNIST and Fashion‑MNIST data and comparing its basic level to that of Cobweb, suggesting diffusion models can serve as a continuous, scalable instantiation of concept formation.
arXiv:2607. 08041v1 Announce Type: new Abstract: How diffusion models circumvent the curse of dimensionality to learn complex distributions over high dimensional spaces from a finite training set, instead of memorizing it, remains a fundamental mystery.
How diffusion models circumvent the curse of dimensionality to learn complex distributions over high dimensional spaces from a finite training set, instead of memorizing it, remains a fundamental mystery. To address this, we introduce analytically tractable Bayesian information restricted diffusion (BIRD) models, in which each pixel observes restricted information about noisy data.
arXiv:2512. 20666v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models have attracted significant attention for their ability to generate diverse, high-fidelity images.
The paper studies how hierarchical correlations in data can be learned by a dense Hopfield network with polynomial activation. It analytically derives conditions for each level of a hierarchical memory model to be locally stable, meaning they correspond to local energy minima. Using prototype reconstruction as a minimal generalization test, the authors show that only a quasi‑polynomial amount of information is needed to generalize beyond specific memories or groups, and they observe a similar phase diagram for Fashion‑MNIST data.
arXiv:2605. 00273v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation.
arXiv:2608. 14172v1 Announce Type: cross Abstract: Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.
arXiv:2602. 09651v2 Announce Type: replace-cross Abstract: Diffusion models do not recover semantic structure uniformly over time.
arXiv:2608. 19067v1 Announce Type: cross Abstract: The empirical success of diffusion models in generative modelling has motivated theoretical work, including quantitative error bounds and qualitative analyses that characterise the different phases of denoising.
arXiv:2609.09909v1 Announce Type: new Abstract: Although text-to-image diffusion models generally exhibit strong prompt-following ability, we identify a persistent and previously underexplored failur...
arXiv:2601. 22651v2 Announce Type: replace-cross Abstract: Training-data attribution for vision generative models aims to identify which training data influenced a given output.
arXiv:2310. 05264v5 Announce Type: replace Abstract: In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility": given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs.
arXiv:2609.39648v1 Announce Type: cross Abstract: Diffusion models are typically viewed as stochastic processes that transform noise into data. We take a complementary perspective: a diffusion model...