# LLM Latent Reasoning as Chain of Superposition

Authors: Jingcheng Deng, Liang Pang, Zihao Wei, Shicheng Xu, Zenghao Duan, Kun Xu, Yang Song, Huawei Shen, Xueqi Cheng.

Models latent reasoning as a sequence of superposed token distributions and trains the process with supervised fine-tuning.

First public: 2025-10-17.
ArXiv initial submission: 2025-10-17.

[Original paper](https://arxiv.org/abs/2510.15522) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2510.15522/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2510.15522/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2510.15522/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2510.15522/record.json)
[Code](https://github.com/DJC-GO-SOLO/Latent-SFT)

## Approaches

- [Continuous-state reasoning](https://latent-implicit-thinking.github.io/topics/continuous-states/): Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.