Add model card for Equilibrium Reasoners

This PR adds a model card for the paper [Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning](https://huggingface.co/papers/2605.21488). It includes the license, pipeline tag, relevant links to the paper and code, and the citation.

README.md

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