# Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization

Authors: Boshi Wang, Xiang Yue, Yu Su, Huan Sun.

Mechanistically studies transformers that learn implicit reasoning after grokking, focusing on how internal algorithms support out-of-distribution generalization.

First public: 2024-05-23.
ArXiv initial submission: 2024-05-23.

[Original paper](https://arxiv.org/abs/2405.15071) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2405.15071/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2405.15071/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2405.15071/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2405.15071/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.