Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
- Published
- Source
- arXiv
- Paper number
- 199
- Field
- Machine Learning
- arXiv ID
- 2605.21488
Key points
- Maze-Unique: the authors found that task ambiguity is poison for attractor learning, because datasets with multiple valid solutions, such as different shortest paths in a maze, make it hard for the model to form stable attractors. They created a maze variant with a unique path and showed that EqR scales to solving complex 30x30 mazes with high confidence.
- Scale generalization: at test time, simply increasing the number of iterations lets the model generalize well beyond the complexity seen during training.
- Self-diagnosis: the fixed-point residual provides an intrinsic confidence score that does not require an external verifier.
Paper links
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