Equilibrium Reasoners learn task-conditioned attractors in latent dynamics to support scalable iterative reasoning, raising Sudoku-Extreme accuracy from 2.6% to over 99% via up to 40,000 equivalent layers.
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Attractor Models solve for fixed points in transformer embeddings using implicit differentiation to enable stable iterative refinement, delivering better perplexity, accuracy, and efficiency than standard or looped transformers.
Spherical flows on S^{d-1} with vMF noise reduce the continuity equation to a scalar ODE in cosine similarity, yielding posterior-weighted marginal velocity and score that enable ODE and predictor-corrector sampling for categorical sequences, with the posterior trained by cross-entropy and empirical
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