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Paper Offers Formal Analysis of Mechanistic Interpretability Limits
A new arXiv preprint examines mechanistic interpretability from a formal, theoretical angle, questioning how much this approach can reveal about large language models. The work focuses on interpretable replacement networks, which are trained as stand-ins for frontier models so researchers can study their behavior. It argues that structural constraints may cap what such analyses can uncover about model internals.