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About this paper
Zhimin Hu, Chen Jiang, Hanbo Xie, N.Apurva Ratan Murty, Sashank Varma
The authors propose a recursive framework for mechanistic interpretability that distinguishes observing information in a neural network from explaining how it produces behavior. It asks researchers to examine measurement, explanatory objects, information use, computational roles, dependencies, and invariance. A mouse motor-cortex study illustrates these questions; the framework is conceptual, and model mechanisms require independent evidence for claims about the world.
Summary by alphaXiv.
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