A framework proves that broad recalibrated leakage is undetectable from predictions alone without an external discrimination ceiling, while near-label leaks produce a detectable unit-purity signature yielding a prior-free test.
DeGroot and Stephen E
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6representative citing papers
Individually calibrated predictors become collectively miscalibrated under Brier-optimal strategic responses with positive belief correlations, but VCG aggregation restores dominant-strategy incentive compatibility and near-optimal performance.
RubricRefine is a training-free pre-execution method that creates rubrics to score and fix inter-tool contract violations in agent code, reaching 0.86 average on M3ToolEval across seven models with zero executions and lower latency.
Calibration error tracks curvature via shared margin-dependent exponential tails; a margin-aware objective improves out-of-sample calibration across optimizers.
AI model failures on complex tasks become increasingly incoherent with longer reasoning chains, making consistent misalignment less likely than chaotic errors as capabilities scale.
Verbalized confidence from small LMs enables cost-effective cascade routing for automated educational scoring, matching large-model accuracy at 76% lower cost when discrimination is strong.
citing papers explorer
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A prior-free blind detection of information leakage from model predictions
A framework proves that broad recalibrated leakage is undetectable from predictions alone without an external discrimination ceiling, while near-label leaks produce a detectable unit-purity signature yielding a prior-free test.
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RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinement
RubricRefine is a training-free pre-execution method that creates rubrics to score and fix inter-tool contract violations in agent code, reaching 0.86 average on M3ToolEval across seven models with zero executions and lower latency.
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Too Sharp, Too Sure: When Calibration Follows Curvature
Calibration error tracks curvature via shared margin-dependent exponential tails; a margin-aware objective improves out-of-sample calibration across optimizers.
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The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?
AI model failures on complex tasks become increasingly incoherent with longer reasoning chains, making consistent misalignment less likely than chaotic errors as capabilities scale.
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Do Small Language Models Know When They're Wrong? Confidence-Based Cascade Scoring for Educational Assessment
Verbalized confidence from small LMs enables cost-effective cascade routing for automated educational scoring, matching large-model accuracy at 76% lower cost when discrimination is strong.