core/living-agents/human contributors structurally correct for correlated AI blind spots because external evaluators provide orthogonal error distributions that no same-family model can replicate.md

@@ -31,7 +31,7 @@ Kim et al. (ICML 2025, "Correlated Errors in Large Language Models") evaluated 3
 - Error correlation is highest for models sharing the **same base architecture**
 - As models get more accurate, their errors **converge** — the better they get, the more their mistakes overlap
 
-This means our existing claim — [[all agents running the same model family creates correlated blind spots that adversarial review cannot catch because the evaluator shares the proposers training biases]] — is now empirically confirmed at scale. When a proposer agent makes an error, there is a ~60% chance that an evaluator agent from the same model family makes the same error — meaning roughly 6 out of 10 shared errors pass through review undetected.
+This means our existing claim — [[all agents running the same model family creates correlated blind spots that adversarial review cannot catch because the evaluator shares the proposers training biases]] — is now empirically confirmed at scale. When both a proposer and evaluator from the same family err, ~60% of those errors are shared — meaning the evaluator cannot catch them because it makes the same mistake. The errors that slip through review are precisely the ones where shared training produces shared blind spots.
 
 ## Same-family evaluation has a structural self-preference bias