| 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} | |
| } | |
| ``` |