Goodfire argues 'Neural Networks Are Already Clean' in new interpretability essay
themgt · hn · 2026-10-16
Goodfire, an AI interpretability company, published an essay arguing that neural network internal representations may be far more orderly — 'clean' — than commonly assumed. The piece is currently drawing active debate on Hacker News, touching a core question in interpretability research: whether model internals are entangled messes or comprehensible structure, with direct implications for alignment and mechanistic interpretability work.
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