#  Harvard AI, Math, and Statistics Seminar: Claire Boyer  

 



####  calendar\_today Date and Time 

 **October 9, 2026** 

 10:30AM - 11:30AM EDT 

####  pin\_drop Location 

 **Maxwell-Dworkin 134A/B**  



 

 



 

Our upcoming event for the Harvard AI, Math, and Statistics Seminars is scheduled for Friday, October 9th from 10:30 – 11:30pm (ET) and will be an in-person presentation at Maxwell-Dworkin, Room 134 a/b. This week's speaker will be **Claire Boyer, Professor at Paris-Saclay university in the Laboratoire de Mathématiques d'Orsay**

**How attention learns structure from data?**

Transformer architectures have demonstrated remarkable empirical success, yet their ability to extract statistical structure from data remains only partially understood. In this talk, I will present recent work shedding light on attention mechanisms through a statistical and operator-theoretic lens.

Based on large-prompt asymptotics, one can show that softmax attention converges to a linear operator acting on the input-token distribution under Gaussian assumptions. This regime enables a precise analysis of both the outputs and the training dynamics using concentration arguments, revealing a surprising bridge between nonlinear softmax attention and tractable linear models.

These results suggest that attention can be understood as a flexible statistical operator that adapts to the underlying data distribution, providing a unifying framework to study its role in representation learning and in-context inference. I will conclude by discussing the connections between transformer architectures and principal component analysis, using this setting to illustrate the power of concentration methods in high-dimensional statistics.



 

 



 

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