# Do Latent Tokens Think? A Causal and Adversarial Analysis of Chain-of-Continuous-Thought

Authors: Yuyi Zhang, Boyu Tang, Tianjie Ju, Sufeng Duan, Gongshen Liu.

Tests COCONUT with latent-state interventions and deliberately biased examples on MMLU and HotpotQA. Finds weak sensitivity to steering and dependence on dataset shortcuts in the evaluated models, challenging whether their intermediate states faithfully carry the reasoning.

First public: 2025-12-25.
ArXiv initial submission: 2025-12-25.

[Original paper](https://arxiv.org/abs/2512.21711) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2512.21711/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2512.21711/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2512.21711/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2512.21711/record.json)

## 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.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

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.