Antagonistic consensus for LLM agents: theory, simulation, and empirical anchoring under correlated errors.
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Updated
May 20, 2026 - Python
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correlated-errors
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Measure error correlation in an LLM judge panel or voter ensemble, report the effective number of independent votes, and correct the majority vote for correlated voters — including the panel shape where it cannot. Closed-form, label-free, numpy only.
evaluation calibration ensemble self-consistency conditional-independence majority-voting effective-sample-size overconfidence llm llm-as-judge judge-panel correlated-errors design-effect error-correlation dependence-aware-aggregation intra-model-bias agreement-confidence cross-family-correlation
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Oct 12, 2026 - Python
A diverse 6-LLM judge committee (4 labs, 3 countries) has correlated errors: ~1.9 effective independent judges of 6 (mean pairwise error-correlation ρ≈0.42) on RAGTruth. Provider diversity does not buy error-independence. Verdicts + one-command recompute included.
consensus ai-safety model-ensemble llm llm-evaluation hallucination-detection llm-as-judge correlated-errors ragtruth
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Updated
Aug 19, 2026 - Python
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