Asymptotic Signal Subspace Recovery in Softmax Attention Models
Attention mechanisms have demonstrated remarkable empirical success in identifying relevant information from large collections of tokens, yet the theoretical principles underlying this behavior remain poorly understood. We study a stylized softmax-attention model in which a query vector is learned by stochastic gradient ascent from a collection of informative and nuisance tokens. Exploiting the symmetry of the model, we derive a population objective and characterize the limiting ordinary differential equation governing the learning dynamics. Using tools from stochastic approximation and dynamical systems theory, we establish a rigorous connection between the stochastic learning algorithm ...
| Authors | Lan V. Truong |
|---|---|
| Primary category | cs.LG |
| Published | 21 June 2026 |
| Last updated at source | 23 June 2026 |
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