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Introduces fixed-point flows, a self-conditioned flow language model that treats self-conditioning as a fixed-point iteration, enabling distillation into a few-step flow map language model (FMLM⋆) that outperforms prior work on OpenWebText.
This technical report investigates draft-conditioned latent refinement for non-autoregressive text generation, showing that good latent geometry does not guarantee good decoding and emphasizing decoder recoverability as a key evaluation metric.
Introduces Discrete Stochastic Localization (DSL), a continuous-state diffusion framework for non-autoregressive text generation that uses unit-sphere token embeddings and a timestep-invariant denoiser, achieving better distributional faithfulness than masked discrete diffusion models on OpenWebText.