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When multiple LLMs share similar pre-training data or alignment biases, majority voting may fail to correct shared biases, which is why the study additionally introduced independent human blind-test validation.
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9/25/2026
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UnverifiedWithout a unified testing environment specification, the comparability of acceleration data is limited, and voting results may be subject to sampling bias.79% similarUnverifiedThe statistical basis for the credibility of majority voting, the Condorcet Jury Theorem, requires that voters be mutually independent, and the error correlations among LLM reviewers violate this premise.75% similarUnverifiedarXiv paper 2609.22512 points out that treating the consensus of multiple LLM reviewers as strong evidence presupposes that the errors of each reviewer are mutually independent, whereas in reality LLM reviewers make the same errors due to similar training data and evaluation approaches, causing consensus to exaggerate evidential strength.74% similarUnverifiedClone copies from the same model share nearly identical weights, training data, and inductive biases, making their errors highly correlated and preventing systematic biases from being eliminated through averaging.70% similar
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