---
license: mit
pipeline_tag: text-generation
---

# Solve the Loop: Attractor Models for Language and Reasoning

Attractor Models are a family of models that use a backbone module to propose output embeddings, followed by an attractor module that refines them by solving for a fixed point using implicit differentiation. This architecture allows for iterative refinement with constant training memory and adaptive inference-time computation.

[**Project Page**](https://attractor-models.github.io/) | [**Paper (arXiv:2605.12466)**](https://arxiv.org/abs/2605.12466) | [**GitHub**](https://github.com/jacobfa/Attractor)

## Introduction
Attractor Models offer a promising alternative to feed-forward computation by iteratively refining latent representations. In language modeling, Attractor Models deliver a Pareto improvement over standard Transformers, improving perplexity and downstream accuracy while reducing training cost. This repository contains the **Attractor-370M** model.

## Sample Usage

To use this model, you need to install the `attractor` package from the [official repository](https://github.com/jacobfa/Attractor):

```bash
git clone https://github.com/jacobfa/Attractor
cd Attractor
pip install -e .
```

Then, you can construct the model in Python:

```python
from attractor.models.attractor import Attractor, AttractorConfig

# Loading the configuration for the 370M model
config = AttractorConfig.from_name("attractor-medium-370m")
model = config.construct_model()
```

## Citation

```bibtex
@article{feinashley2026attractor,
  title={Solve the Loop: Attractor Models for Language and Reasoning},
  author={Fein-Ashley, Jacob and Rashidinejad, Paria},
  year={2026},
  url={https://arxiv.org/abs/2605.12466}
}
```