Softmax Attention on Gaussian Mixtures: Linear When It Can, Selective When It Must

arXiv:2610.11798 · stat.ML, cs.LG · Submitted 2026-10-08 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Gradient Flow — 15× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Clustering — 7× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Degeneracy — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Associative Memory — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Training Dynamics of Softmax Self-Attention: Fast Global Convergence via Preconditioning

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