Goodfire’s Nick Cammarata argues agentic analysis resolves familiar examples of neural polysemanticity
[AINews] Agents disprove Polysemanticity; greatly improve Interpretability & Alignment
What happened
Latent Space highlights Goodfire’s Nick Cammarata’s working theory that neural networks are already largely interpretable, with further agentic analysis resolving many familiar examples of polysemanticity and superposition. Cammarata argues that most neurons are legible, contrasting with roughly 2% in his 2023 OpenAI effort. The roundup also reports objections concerning weak activations, neuron simulation and neglected non-maximal activations, alongside a reminder that the paper itself is not yet available.
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