Mechanistic Interpretability of Deep Neural Networks: Methods, Frameworks, and Perspectives

arXiv:2611.13868v1 Announce Type: cross Abstract: Deep neural networks underpin fundamental tasks in a wide range of signal domains, such as image, audio, and language processing. However, their internal mechanisms are notably difficult to interpret. Internal activations are structured signals transformed across layers that may encode a multitude of interdependent relationships, as individual neurons frequently represent multiple underlying concepts. Mechanistic interpretability aims to uncover the internal mechanisms of neural networks by identifying components whose activations are causally

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