A NeurIPS 2026 Workshop · Sydney, Australia
Beyond next-token prediction — exploring the theory, algorithms, and applications of discrete diffusion and flow models for parallel, non-causal generation.
01 Overview
Autoregressive models produce their output one element at a time, in a fixed order. The recipe is remarkably general, and it has carried the field a long way. However, it also ties the order of computation to the order of the result, leaving parallelism, revision, and control hard to reach. Discrete diffusion and flow models take a different route. They generate by iterative denoising: refining many positions at once, and revisiting earlier choices as a sample takes shape.
That shift brings a different set of computational properties — non-causal, parallel, and controllable — along with open questions spanning mathematics, algorithms, and engineering, and a growing body of work that combines the two paradigms rather than choosing between them. This workshop joins two conversations: the theory and algorithms that make discrete generative models work, and the applications and systems that put them to use.
Discrete diffusion and flow models at the intersection of generative modeling, optimal transport, and stochastic optimal control.
Novel applications and scalable systems that exploit non-causal, parallel generation.
02 Scope
This workshop considers, but is not limited to, the following topics.
03 Call for Papers
We invite submissions on any topic within the workshop's scope, across both tracks, from theory and algorithms to applications and systems. All submissions are handled through OpenReview.
Accepted papers are presented as posters, with a selection of contributed orals. A Best Paper Award recognizes outstanding work.
04 Important Dates
All deadlines are 23:59 AoE. Dates are indicative and subject to change.
05 Schedule
A full day of invited talks, contributed orals, two poster sessions, and a panel. Speaker slot assignments are being finalized.
Tentative program · times shown in the local conference time zone. Invited-talk slots will be mapped to speakers closer to the event.
06 Invited Speakers
All six speakers are confirmed. Talk titles will be announced closer to the workshop.
Associate Professor of Computer Science at Stanford, where his group advances generative modeling, probabilistic inference, and diffusion methods. His recent work pushes diffusion language models toward production-scale text generation.
Member of the technical staff at OpenAI, working on next-generation language models for scientific discovery. His research spans reinforcement learning, test-time steering, and post-training of diffusion and discrete generative models, with applications to protein and drug design.
Research scientist at Meta Superintelligence Labs, previously at Google DeepMind. He works on generative modeling and probabilistic inference — masked diffusion, gradient estimation, and sampling — and on sequence models that encode long-range dependencies efficiently.
Research scientist at Meta Superintelligence Labs, where he builds generative models with scientific structure. His work on Schrödinger bridges and adjoint-based diffusion samplers brings optimal-transport and control priors to diffusion, with applications from image restoration to protein folding and particle physics.
Assistant Professor of Computer Science at UCLA, where he leads the Machine Intelligence (MINT) group, and co-founder and CTO of Inception, which builds ultra-fast parallel diffusion LLMs. His research sits at the intersection of generative models, reinforcement learning, and scientific discovery.
Research Director at NVIDIA Research, leading the Fundamental Generative AI Research (GenAIR) team. His work spans diffusion and flow models, discrete generative models, and accelerated sampling, applied to images, video, weather, proteins, and molecules.
07 Organizers
General enquiries: bento-neurips@googlegroups.com
Associate Professor at KAIST working on 3D vision, computer graphics, and generative models.
mhsung@kaist.ac.kr
PhD student at KAIST, working on diffusion models and generative AI.
jh27kim@kaist.ac.kr
Professor at Georgia Tech working on Generative AI such as diffusion models.
mtao@gatech.edu
Assistant Professor at the University of Pennsylvania developing generative models for biological and therapeutic design.
pranam@engineering.upenn.edu
Undergraduate Researcher at the University of Pennsylvania researching generative models for biomolecular design.
sophtang@engineering.upenn.edu
Research Scientist at Cerebras Systems working on large-scale training, scaling laws, and efficient deep learning.
nolan@cerebras.net
Researcher at MBZUAI focused on discrete diffusion language models and efficient generative modeling.
subbham@gmail.com08 Sponsors & Support
Sponsorship helps us fund travel grants and broaden participation for students and early-career researchers. We gratefully acknowledge the organizations supporting this workshop.
Interested in supporting the workshop? bento-neurips@googlegroups.com
Join us at NeurIPS 2026 to push generation beyond next-token prediction. Submissions open July 20 and close September 5, 2026.
Submit on OpenReviewSubmissions open Jul 20, 2026 at NeurIPS.cc/2026/Workshop/BeNTo · bento-neurips@googlegroups.com