Unverified50% confidenceFactExact time
When evaluation_steps is not passed, DeepEval's G-Eval metric automatically generates scoring steps based on criteria, causing the same response to potentially get opposite conclusions on re-runs, producing drift.
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Long-term
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10/13/2026
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VerifiedError cascading refers to small judgment deviations in early steps being progressively amplified as the reasoning chain extends, ultimately producing results far from what was intended.68% similarUnverifiedEvaluation sets suffer from representational bias, as they can only cover situations the builders anticipated, meaning a model that performs perfectly on the eval set may still fail on real traffic after launch.68% similarUnverifiedWhen measuring prediction error after fractional integration, there is an exact 1/L leading-order error for forecasting from a finite context of length L.67% similarUnverifiedWhen the guidance signal deviates from the model's own sampling distribution, the policy gradient estimate incurs bias.66% similarUnverifiedThe test conclusions show that what truly differentiates models is not visual perception but step-by-step simulation capability — judging right from wrong is relatively easy, but locating which step went wrong in a multi-step state transition is a higher-order challenge for a model's internal state management.66% similar
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