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NeurIPS 2026ParisTechBrownU TorontoBasis Research InstituteMila, Université de MontréalPhysics-informed ML & PDEs

Wasserstein residuals: Learning Gradient Flows from Population Dynamics

A residual-based continuity loss for Wasserstein gradient flows yields a simulation-free stitching method robust to sparse observations and state-of-the-art on trajectory inference benchmarks.

Markus Heinonen, Yair Shenfeld, Ricardo Baptista, Daniel Waxman and 3 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 11 of 20 reviewers recommend it
lenient 3/5
medium 7/10
strict 1/5
76%Highly rated
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NeurIPS 2026SpotlightNew YorkBasisSakana AI3D generation

Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes

Dream-Cubed introduces a billion-scale Minecraft voxel dataset and diffusion models that generate interactive 3D worlds directly from block tokens with inpainting and outpainting support.

Tim Merino, Sam Earle, Ryunosuke Iwai, Julian Togelius and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 2/5