# Exploring Recurrent-Depth Language Models for Multitask Clinical Text Processing

Authors: Xi Yang, Guodong Liu, Cheng Peng, Kaleb E. Smith, Yingying Zhang, Xing He, Yi Guo, Jiang Bian, Haoteng Tang, Yonghui Wu.

Jointly fine-tunes looped and hierarchical recurrent language models on nine clinical text datasets and tests transfer to four more. Compares direct-answer performance with standard transformers while noting that the model families also differ in pretraining.

First public: 2026-10-20.
ArXiv initial submission: 2026-10-20.

[Original paper](https://arxiv.org/abs/2610.27559) · [PDF](https://arxiv.org/pdf/2610.27559) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.27559/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.27559/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.27559/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.27559/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 0.11.0; literature checked through 2026-10-26.