# Attention Routing Stabilizes Early: Working-Set Inference for Recurrent Language Models

Authors: Ke Wan, Chen Chen.

Studies when a recurrent language model settles on which context tokens to attend to. Reuses that selected working set in later loops while still recomputing attention weights and hidden-state updates.

First public: 2026-09-23.
ArXiv initial submission: 2026-09-23.

[Original paper](https://arxiv.org/abs/2609.27373) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.27373/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.27373/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.27373/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.27373/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.

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.