self-consistency sampling

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Paper Extends Condorcet's Jury Theorem to Panels of AI Advisers

A new arXiv paper examines how Condorcet's jury theorem applies when the same question is posed to several AI models, as happens in self-consistency sampling and LLM-as-a-judge setups. The theorem holds that adding independent, competent voters makes a majority more reliable, but the author argues this breaks down for AI advisers. The work introduces a latent-dimension framing to characterize when aggregating multiple model outputs actually improves accuracy.

llm-as-a-judge/self-consistency/ensembles/research