Next Thoughts Are Distributions: Generative Autoregressive Reasoning in the Latent Space

Natural-language chain-of-thought makes reasoning observable, but it forces computation through a discrete and verbose interface. Continuous thoughts provide a more compact substrate, yet existing methods commonly predict them with mean squared error, yielding a unimodal Gaussian model. This is a poor fit for reasoning: the same context admits multiple valid strategies, and their average is useful for none. In this paper, we introduce Autoregressive Continuous Thought, a framework that models continuous next-thought prediction as a generative problem. A causal transformer provides the reasoning context, while a conditional diffusion head samples the next continuous thought from a multimodal distribution. Our approach preserves the autoregressive structure of language models while avoiding the mode collapse of regression-based continuous reasoning. Experiments on mathematical reasoning, planning, and algorithmic tasks demonstrate that our method improves accuracy, condenses reasoning traces, and produces diverse latent reasoning paths.