Neural Networks Demonstrated to Be Mechanistically Interpretable

The article argues that neural networks are more interpretable than previously believed, challenging the notion of inherent complexity through superposition and polysemanticity. It suggests that issues like polysemanticity stem from architectural flaws and tool limitations, not intrinsic network design.

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