EMNLP paper: diffusion models form stable attractors for never-seen test samples
LucaAmb · x · 2026-09-08
An EMNLP 2026 main-track paper by Dima Krotov and collaborators studies how memory and novelty (creativity) coexist in language diffusion models (LDMs):
- Beyond memorizing training data, the diffusion dynamics can error-correct perturbations of test samples it never saw during training
- As training set size grows, unseen test samples also become stable attractors of the generative dynamics; token recovery improves on unseen examples while degrading on training ones
- The behavior closely mirrors Dense Associative Memory models as memory count increases, suggesting a unified dynamical account of memorization vs. generalization
- Collaboration between IBM and RPI; project page is public
Related event: EMNLP Paper Shows Language Diffusion Models Are Associative Memories(2 posts)→
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