arXiv:2512.11784v2 Announce Type: replace Abstract: Softmax attention is a central component of transformer architectures, yet its nonlinear structure poses significant challenges for theoretical analysis. We develop a unified, measure-based framework for studying single-layer softmax attention under both finite and infinite prompts. For i.i.d. Gaussian inputs, we lean on the fact that the softmax operator converges in the infinite-prompt limit to a linear operator acting on the underlying input-token measure. Building on this insight, we establish non-asymptotic concentration bounds for the o
The paper represents a theoretical breakthrough in understanding softmax attention, a core component of transformer AI models, which is crucial as transformer architectures continue to dominate AI research and development.
This theoretical analysis simplifies the understanding of complex AI model behavior, potentially enabling more efficient and scalable transformer designs for various AI applications.
The ability to model softmax as linear attention in certain regimes could lead to more predictable, efficient, and potentially generalizable AI models, improving theoretical analysis and practical implementation.
- · AI researchers
- · Large language model developers
- · Cloud AI providers
- · AI-driven software platforms
- · Developers of less efficient transformer architectures
Improved theoretical understanding of transformer models.
Development of more robust and scalable AI models with better performance characteristics.
Acceleration of AI research and industrial application, potentially leading to more advanced AI agents and broader AI capabilities.
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Read at arXiv cs.LG