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.
Training a mean-field Transformer under L2 regularization induces an escape from attention-driven token clustering in later layers after initial clustering.
citing papers explorer
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Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
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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Solve the Loop: Attractor Models for Language and Reasoning
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.
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Training-Induced Escape from Token Clustering in a Mean-Field Formulation of Transformers
Training a mean-field Transformer under L2 regularization induces an escape from attention-driven token clustering in later layers after initial clustering.