Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
Paper • 2605.21488 • Published • 7
pad_id int64 | ignore_label_id int64 | blank_identifier_id int64 | vocab_size int64 | seq_len int64 | num_puzzle_identifiers int64 | total_groups int64 | mean_puzzle_examples float64 | sets list | total_samples int64 |
|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 6 | 900 | 1 | 1,000 | 1 | [
"all"
] | 1,000 |
0 | 0 | 0 | 11 | 81 | 1 | 1,000 | 1 | [
"all"
] | null |
EqR-data
This repository contains the datasets used in the paper Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning.
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.
- 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
- GitHub: locuslab/eqr
- Project Page: X (Twitter) Thread
Usage
The datasets can be downloaded using the scripts provided in the official GitHub repository:
git clone https://github.com/locuslab/eqr
cd eqr
bash scripts/download_artifacts.sh
Citation
@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}
}
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