LLM judges display per-document transitivity violations in 33-67% of cases despite low aggregate rates, while conformal prediction set widths serve as reliable indicators of document-level difficulty with cross-judge agreement.
Algorithmic Learning in a Random World
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ACP-UCB1 achieves logarithmic upper-quantile regret in stochastic bandits by combining adaptive conformal quantile estimates with UCB-style optimism.
citing papers explorer
-
Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations
LLM judges display per-document transitivity violations in 33-67% of cases despite low aggregate rates, while conformal prediction set widths serve as reliable indicators of document-level difficulty with cross-judge agreement.
-
Conformal-Style Quantile Analyses for Stochastic Bandits
ACP-UCB1 achieves logarithmic upper-quantile regret in stochastic bandits by combining adaptive conformal quantile estimates with UCB-style optimism.