Next Thoughts Are Distributions: Generative Autoregressive Reasoning in the Latent Space
Reasoning problems often admit multiple valid ways to proceed. Continuous reasoning promises to move computation beyond language tokens into a more compact latent space, but representing several plausible ways to think next remains difficult. We introduce Autoregressive Thought Flow (ATF), which models the next continuous thought as a multimodal distribution. A causal autoregressive model performs the reasoning computation, while a lightweight diffusion head generates a plausible next thought from the resulting condition. The sampled thought is fed back into the model, allowing continuous reasoning to unfold for a variable number of steps while preserving the pretrained backbone. Across m...
| Authors | Yang Li, Yi Wang, Shiyuan Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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