How Much Noise is Enough: On Privacy, Security, and Accuracy Trade-Offs in Differentially Private Federated Learning
Adhishree Kathikar, Kyle Chard, Nathaniel Hudson
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Preprint
Aug 2026
Interpreting Language Model Hidden States at Scale
OmniLens is presented, which applies a single lens family to any model-width activation, whether residual stream, attention, or MLP, and combines two independent scaling techniques, which reproduces key published results at substantially lower cost.
Jordan Pettyjohn, Mansi Sakarvadia, Nathaniel Hudson et al.
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