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We focus on algorithmic resilience -- the maximum number of faulty nodes an algorithm can tolerate -- and present algorithms and impossibility results whose resilience depend on the accuracy of the predictor. As our first main result, we bring a complete characterization of the consistency--robustness trade-offs in both the non-authenticated and authenticated settings: for $n$ nodes and a parameter $\\alpha \\in [0, 1]$, we present algorithms that tolerate up to $\\alpha \\cdot n$ faulty nodes when the predictor is correct (consistency), and up to $\\frac{1-\\alpha}{2} \\cdot n - 1$ faulty nodes when the predictor is arbitrarily wrong (robustness); in the authenticated setting the robustness bound improves to $(1-\\alpha) \\cdot n - 1$. 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