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Model disagreement helps catch tricky or risky inputs by showing where predictions diverge. Using metrics like ensemble variance or margin highlights uncertain cases
We’ve decided to keep Claude Pro and Perplexity Pro alongside Suprmind to leverage their unique strengths in orchestration versus aggregation
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When models disagree, it signals uncertain or risky inputs worth a closer look. Tracking metrics like ensemble variance or prediction margin helps spot these cases
When models disagree on predictions, it often signals risky or unclear inputs. By tracking metrics like entropy or ensemble variance, we can spot these tricky cases
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Disagreement among AI models may sound counterintuitive, but it can actually flag silent hallucinations—errors that slip past unnoticed