Author

Nikhil Garg

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#artificial intelligence Jun 2026

Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift

This work proposes a test-time adaptation (TTA) approach that adapts to distribution shifts by leveraging homogeneity among unlabeled samples observed at inference time and establishes that TTA is a promising framework for AI text detection in the wild.

Ke Ren, Manish Raghavan, Nikhil Garg · arXiv.org · 1 citation · ⚡1

#artificial intelligence Preprint Oct 2026

A Testable Theory of Atomic Features

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., SAEs) of increasing size recover an increasing prefix of the most prevalent atoms in the training data. This"recovery principle"yields thr...

Kenny Peng, Jon M. Kleinberg, Nikhil Garg · 0 citations