AIBullisharXiv – CS AI · 14h ago7/10
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E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing
Researchers introduce e-valuator, a method that applies sequential hypothesis testing to convert AI verifier scores into statistically reliable decision rules for evaluating agent trajectories. The framework provides provable false alarm rate control and enables early termination of problematic sequences, offering a model-agnostic approach to improving the reliability of agentic AI systems.