eNTK eigenanalysis surfaces features in trained neural networks

Researchers have demonstrated that analyzing the empirical Neural Tangent Kernel (eNTK) can reveal feature directions within trained neural networks. This method was tested on a 1-layer MLP and a 1-layer Transformer, showing that the top eigenspaces of the eNTK align with ground-truth or interpretable features. For a pretrained language model, Gemma-3-270M, eNTK eigendirections aligned with grammatical features better than PCA on model activations, suggesting eNTK eigenanalysis as a tool for mechanistic interpretability. AI

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IMPACT Introduces a novel technique for understanding internal model representations, potentially aiding in interpretability research.

RANK_REASON Academic paper detailing a new method for analyzing neural network features. [lever_c_demoted from research: ic=1 ai=1.0]

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• Gemma-3-270M
• MLP
• Transformer
• PCA

• paper
• other