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Next-token prediction has driven remarkable progress in large language models, yet a growing body of evidence suggests that they can struggle on problems governed by complex global constraints. In this work, we focus on this regime and ask whether some of these limitations arise from the inference interface induced by...
We propose an instantiation of flow matching that relies on a time-independent velocity field (an \emph{autonomous flow}) to exactly map between two distributions, so long as the target is singular, i.e.\ supported on a lower-dimensional data manifold. We also show that the one-step generative map associated with this...
Equilibrium Forcing is introduced, a simplified framework for video denoising generative models without noise level conditioning that pioneers modular training- and inference-time designs for noise-unconditional generation that decouple learning the denoising field from sampling.
This work introduces Explorative Modeling, a new paradigm that instead factors the training loop, exploring K candidate matches between model generations and data, and training on the best, so predictions commit to modes rather than blurring them, and establishes XMs as both a new pretraining axis for existing generati...
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