Add dataset card for EqR-data

README.md

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+---
+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}
+}
+```