Interp researchers push back: 'networks are already clean' claim rests on dropout artifact
aryaman2020 · x · 2026-10-16
Interpretability researchers are cautioning against the narrative that neural networks are "already clean." fjzzq2002 argues (a) ConvNeXt's cleaner last layer mostly comes from removing dropout—experiments show dropout before the classifier breaks up class-detector neurons while accuracy stays at 70–73%; (b) "recursive generalization" is interesting but ill-defined and unfalsifiable; (c) even with agents improving autointerp, understanding all neurons is far from enough.
aryaman2020 echoed the call for caution, and livgorton added that the original piece risks leaving readers thinking a central interp idea rested on a mistake and has been overturned—something the evidence doesn't support.
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