Model disagreement helps spot risky inputs by highlighting cases where predictions vary the most. Tracking ensemble variance or margin shows how uncertain the system is on specific data points
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Disagreement among AI models may sound counterintuitive, but it can actually flag silent hallucinations—errors that slip past unnoticed
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When AI models disagree, it creates a built-in checkpoint that can catch silent hallucinations—those subtle errors that slip past unnoticed
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