The paper introduces a finite-calibration regime map and Finite-Calibration Panel Selection selector, finding scalar aggregation wins on most real benchmark-budget combinations while joint tables help when interactions are present.
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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
VERDI derives three structural confidence signals from decomposed LLM verification traces and calibrates them with Platt-scaled logistic regression to achieve AUROC 0.72-0.91 on GPT models and 0.56-0.70 on Qwen models where log-probabilities fail.
Calibrating the full set of LLM judges with labeled data halves calibration error versus top-5 accuracy selection on RewardBench2 and outperforms on four benchmarks.
Temporal difference calibration aligns uncertainty estimates in vision-language-action models with their value functions for better sequential performance.
citing papers explorer
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A Finite-Calibration Regime Map for LLM Judge Panels
The paper introduces a finite-calibration regime map and Finite-Calibration Panel Selection selector, finding scalar aggregation wins on most real benchmark-budget combinations while joint tables help when interactions are present.
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VERDI: Single-Call Confidence Estimation for Verification-Based LLM Judges via Decomposed Inference
VERDI derives three structural confidence signals from decomposed LLM verification traces and calibrates them with Platt-scaled logistic regression to achieve AUROC 0.72-0.91 on GPT models and 0.56-0.70 on Qwen models where log-probabilities fail.
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Calibrate, Don't Curate: Label-Efficient Estimation from Noisy LLM Judges
Calibrating the full set of LLM judges with labeled data halves calibration error versus top-5 accuracy selection on RewardBench2 and outperforms on four benchmarks.
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Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models
Temporal difference calibration aligns uncertainty estimates in vision-language-action models with their value functions for better sequential performance.