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If the claim holds, it flips the field's usual superposition pessimism — and that's precisely why the reaction is so split. Direct circuit-reading would move safety auditing beyond black-box probing, making deception detection far more concrete. But 'clear function' labels are easy to assign and hard to verify; without causal tests like ablation studies, interpretability risks sliding into storytelling. The divide among researchers is itself the telling part: there's still no shared standard for what counts as understanding a model. On our view, the debate matters more than the essay — its outcome decides whether labs can ever certify model behavior rather than just probe it.
Is AI interpretability close to a breakthrough? A new essay has researchers divided
Neural networks may be much easier to read than researchers assumed, with about 90 percent of neurons serving a clear function. That would be big news for AI safety research, since deception or dangerous behavior could be spotted directly in a model's circuits. Res…

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