Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation

arXiv:2609.28859 · cs.AI, cs.IT, math.IT, stat.ME · Submitted 2026-09-24 · Read on arXiv

cs.AI, cs.IT, math.IT, stat.ME

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Verification — 21× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Probability Mass Function — 6× in this paper
• Accuracy — 5× in this paper · explained in 1 episode
• Calibration — 5× in this paper · explained in 7 episodes
• LLM judge — 3× in this paper · explained in 2 episodes
• Sequential Cost-Aware Likelihood-Guided Escalation — 3× in this paper
• Adaptivity — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Additivity — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes

Sources

• Cost-Optimal Active AI Model Evaluation
• Optimized Labeling Resource Allocation for Prediction-Assisted Inference via OPAL
• Semi-Supervised Hypothesis Testing by Betting on Predictions

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