Iterative reasoning models work by learning task-specific attractors in their latent space; scaling test-time compute (more iterations and parallel paths) improves performance on hard problems without needing external verifiers.
This paper explains how AI models can solve hard problems by iteratively refining internal states, like a brain thinking through steps. The key insight is that models learn to create 'attractors'—stable patterns that pull the model toward correct answers.