# SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens

Authors: Yinhan He, Wendy Zheng, Yaochen Zhu, Zaiyi Zheng, Lin Su, Sriram Vasudevan, Qi Guo, Liangjie Hong, Jundong Li.

Compresses textual reasoning into semantically aligned implicit tokens intended to preserve useful intermediate computation with fewer generated steps.

First public: 2025-10-28.
ArXiv initial submission: 2025-10-28.

[Original paper](https://arxiv.org/abs/2510.24940) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2510.24940/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2510.24940/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2510.24940/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2510.24940/record.json)
[Code](https://github.com/YinhanHe123/SemCoT)

## Approaches

- [Internalized reasoning](https://latent-implicit-thinking.github.io/topics/internalized-reasoning/): Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.
- [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.