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cs.CY2026
Personalized Recommendations Without Inducing Congestion: Mitigating Disparities in the NYC High School Match
Erica Chiang, Kenny Peng, Rebecca Lichtenstein +7
Algorithmic recommendations can help participants navigate large matching markets. For example, recommendations for school and college choices may reduce information frictions and…
cs.AI2026
A Testable Theory of Atomic Features
Kenny Peng, Jon Kleinberg, Nikhil Garg
We develop and test a theory of language model representations in which there exist atomic features. Our main theoretical insight is that in such a model, sparse dictionaries (e.g.…
cs.LG2026
How Many Features Can a Language Model Store Under the Linear Representation Hypothesis?
Nikhil Garg, Jon Kleinberg, Kenny Peng
We introduce a mathematical framework for the linear representation hypothesis (LRH), which asserts that intermediate layers of language models store features linearly. We separate…