# CopyLoop: Fast Looped Transformer Decoding via Context Copying and Shallow Screening

Authors: Wenyu Du, Stephen Chung.

Speeds up looped language models by copying candidate token spans from the context, screening them at a shallow depth, and verifying them through the remaining loops. Overlaps verification with new proposals and evaluates the approach on code and mathematical reasoning.

First public: 2026-10-28.
ArXiv initial submission: 2026-10-28.

[Original paper](https://arxiv.org/abs/2610.38822) · [PDF](https://arxiv.org/pdf/2610.38822) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.38822/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.38822/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.38822/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.38822/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.

Reading-list version 0.12.0; literature checked through 2026-11-02.