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The University of Texas at Austin
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CALVER reveals that traditional voting mechanisms in LLMs can be misleading, achieving over 42% accuracy in identifying valid causal answers where others fail.
Bayesian mixture-of-experts models can achieve robust density and parameter estimation with adaptive expert selection, fundamentally reshaping our approach to complex probabilistic modeling.
