| license: apache-2.0 | |
| pipeline_tag: other | |
| # Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning | |
| This repository contains the models presented in the paper [Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning](https://huggingface.co/papers/2605.21488). | |
| - **Code:** [GitHub Repository](https://github.com/locuslab/eqr) | |
| - **Project Page:** [X/Twitter Thread](https://x.com/huskydogewoof/status/2057641657580064941?s=20) | |
| Equilibrium Reasoners (EqR) enable test-time scaling without external verifiers or task-specific priors by learning task-conditioned attractors. This approach allows neural networks to adaptively allocate test-time compute based on task difficulty by scaling internal dynamics along two axes: depth (iterations) and breadth (stochastic trajectories). | |
| ## 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} | |
| } | |
| ``` |