# Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers

Authors: Harsh Kohli, Srinivasan Parthasarathy, Huan Sun, Yuekun Yao.

Trains transformers to answer multi-hop knowledge queries without writing intermediate steps. Tests whether repeated shared layers help combine unfamiliar facts and solve longer chains, and examines when extra inference loops help or cause overthinking.

First public: 2026-04-09.
ArXiv initial submission: 2026-04-09.

[Original paper](https://arxiv.org/abs/2604.07822) · [PDF](https://arxiv.org/pdf/2604.07822) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2604.07822/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2604.07822/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2604.07822/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2604.07822/record.json)

## Approaches

- [Recurrent reasoning](https://latent-implicit-thinking.github.io/topics/recurrent-reasoning/): Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
- [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.

Reading-list version 0.15.2; literature checked through 2026-11-03.