Nick Cammarata Challenges Superposition Explanations in Neural Network Interpretability
推翻一切！Nick Cammarata 万字长文宣告，AI 不是黑箱，99% 神经元清晰可见
What happened
Nick Cammarata’s Neural Networks Are Already Clean argues that improved observation tools reveal coherent neuron functions, reporting clear interpretations for 99%–100% of final-layer neurons in tested modern vision models. He reinterprets classic examples of apparently unrelated features as geometric detection or recursive generalization. Neel Nanda counters that these findings do not resolve representational superposition in language models and defends SAE applications. Cammarata describes the proposal as a working hypothesis, acknowledging gaps in language-model training evidence, multimodal coverage, and causal validation.
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