Neural Networks Are Already Clean: Decoding the Ecology of Circuits
A provocative working hypothesis challenges the notion of neural networks as chaotic black boxes, revealing an unexpectedly clean internal circuit ecology. By mapping how microscopic structures drive generalization, the research bridges the gap between raw compute and mechanistic interpretability. This paradigm shift offers developers a clearer lens to audit, debug, and steer complex AI behaviors with unprecedented precision.