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Sander Dieleman's post on flow maps frames diffusion model distillation as learning to compute the integral of the velocity field directly, rather than stepping along tangent directions. The reformulation unifies 20+ recent papers under three consistency constraints and explains why single-step sampling is achievable without sacrificing bijectivity.
Read more →Fourteen researchers across Berkeley, MIT, Harvard, and EPFL published a 41-page manifesto arguing that a scientific theory of deep learning is not just desirable but already forming. They call it "learning mechanics" and point to five converging research threads — solvable models, tractable limits, empirical laws, hyperparameter theories, and universal behaviors — that together look something like what statistical mechanics looked like before it became statistical mechanics.
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