Author

Nathan Hu

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#machine learning Preprint Sep 2026

Matryoshka attribution: Learning to attribute language model outputs to representations and weights

MAttr is viewed as a successful formulation of interpretability into a learnable objective that can be tackled with gradient descent, and is encouraged to encourage future work along these lines.

Aryaman Arora, Kirill Acharya, Nathan Hu et al. · 1 citation

#artificial intelligence Preprint Sep 2026

Verbalizing Subliminal Learning Effects Using Text Optimization

This work uses text optimization to detect subliminal learning effects and describes them as legible prompts and presents SALVE (Search-Aided Latent Verbalization) as a method to proactively detect subliminal learning effects.

Nathan Hu, Sanmi Koyejo, Christopher Potts · 1 citation