[AINews] Agents disprove Polysemanticity; greatly improve Interpretability & Alignment
Goodfire's Nick Cammarata argues agentic analysis dissolves polysemanticity, making neural networks already interpretable
“Neural Networks Are Already Clean”
Goodfire researcher Nick Cammarata published an essay arguing that known cases of polysemanticity and superposition—long-standing obstacles to AI interpretability—simply fall apart under further agentic analysis, suggesting neural networks are inherently cleaner than assumed. This directly challenges core assumptions in mechanistic interpretability research and has positive implications for AI alignment. Community reaction included a reported 'global lowering of P(doom),' signaling this is viewed as a meaningful safety-positive development.