A paper by Kempner Institute researchers has received an Outstanding Paper Honorable Mention at ICML 2026, the 43rd International Conference on Machine Learning, held July 6–11, 2026, in Seoul, South Korea.
The paper’s authors are Kempner Research Fellow Binxu Wang; Jacob A. Zavatone-Veth, a Junior Fellow of the Harvard Society of Fellows and an affiliate of the Harvard Center for Brain Science; and Kempner associate faculty member Cengiz Pehlevan, associate professor of applied mathematics at the Harvard John A. Paulson School of Engineering & Applied Sciences (SEAS).
The authors were recognized with an Outstanding Paper Honorable Mention for “A Random Matrix Perspective on the Consistency of Diffusion Models,” which was one of nine papers selected for outstanding paper awards or honorable mentions. These awards recognize technical depth, novelty, and potential for impact in the field. This year, ICML received more than 23,900 submissions and accepted just over 6,300 papers.
Research explains why diffusion models can produce consistent outputs
The paper introduces a mathematical framework explaining a striking phenomenon: diffusion models trained on entirely different, non-overlapping slices of a dataset can still generate nearly identical images when handed the same noise seed. Diffusion models build images by denoising — repeatedly stripping away noise while adding detail until a sample resembles the training data — so it is far from obvious why two models that never saw a single shared example should converge on the same output.
