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arXiv — cs.AI

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

arXiv:2609.28859v1 Announce Type: new Abstract: Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality standard. Yet using AI judgments for formal statistical inference is…

arXiv — cs.AIBy: Dae Woong (David), Ham, Xuejun Zhao, Stefanus Jasin, Fenghua YangSeptember 25, 2026 (Sep 25)
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  • arXiv:2609.28859v1 Announce Type: new Abstract: Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality standard
  • Yet using AI judgments for formal statistical inference is…
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Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation

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