# Detecting Hidden Chain-of-Thought in Large Language Models with Linguistic, Behavioral, and Mechanistic Indicators

Authors: Armaan Singh, Ryan Trinh Le, Jasmine Kaur, Abdullah Sultan, Edward Lue Chee Lip, Kiran Nijjer, Adnan Ahmed, Vasu Sharma.

Compares behavior under neutral, explicit-CoT, and no-CoT prompts using linguistic and internal-state measurements. Its score measures resemblance to CoT behavior, not direct evidence of a hidden reasoning trace; calibration controls expose important confounds.

First public: 2026-08-30.
ArXiv initial submission: 2026-08-30.

[Original paper](https://arxiv.org/abs/2608.29956) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2608.29956/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2608.29956/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2608.29956/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2608.29956/record.json)

## Approaches

- [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.