| --- |
| license: apache-2.0 |
| task_categories: |
| - other |
| --- |
| |
| # EqR-data |
|
|
| This repository contains the datasets used in the paper [Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning](https://huggingface.co/papers/2605.21488). |
|
|
| The datasets are designed to evaluate scalable test-time reasoning in iterative latent models, specifically focused on learning task-conditioned attractors. |
|
|
| ## Dataset Details |
|
|
| The repository includes data for two main reasoning tasks: |
| - **Sudoku-Extreme**: A set of challenging Sudoku puzzles designed to test the limits of iterative reasoning models, following the setup from [HRM](https://github.com/sapientinc/HRM). |
| - **Maze-Unique**: A dataset of 30x30 mazes where each maze has a unique solution path and specific length constraints. |
|
|
| ## Links |
|
|
| - **Paper**: [Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning](https://huggingface.co/papers/2605.21488) |
| - **GitHub**: [locuslab/eqr](https://github.com/locuslab/eqr) |
| - **Project Page**: [X (Twitter) Thread](https://x.com/huskydogewoof/status/2057641657580064941?s=20) |
|
|
| ## Usage |
|
|
| The datasets can be downloaded using the scripts provided in the official GitHub repository: |
|
|
| ```bash |
| git clone https://github.com/locuslab/eqr |
| cd eqr |
| bash scripts/download_artifacts.sh |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{huang2026equilibrium, |
| title={Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning}, |
| author={Huang, Benhao and Geng, Zhengyang and Kolter, Zico}, |
| journal={arXiv preprint arXiv:2605.21488}, |
| year={2026} |
| } |
| ``` |
