Unverified50% confidenceSolutionExact time
A mature evaluation pipeline continuously monitors features such as the intent distribution, length distribution, and tool-call frequency of online traffic, and sets alert thresholds, triggering evaluation set updates or resampling when drift exceeds an acceptable range.
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50%
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Long-term
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10/12/2026
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UnverifiedTesting for model drift requires establishing a continuous statistical monitoring pipeline, periodically comparing the distribution characteristics of production data and training data, and setting performance metric alert thresholds.78% similarUnverifiedThe starting point of the evaluation pipeline is continuously collecting traces of online interactions, including user input, model reasoning steps, tool-call parameters and return values, final output, and runtime metrics such as latency and token consumption.77% similarUnverifiedSpecific means of continuous monitoring include output distribution drift detection, abnormal behavior log auditing, human-in-the-loop verification, and rule-based or model-based safety guardrails.74% similarUnverifiedThe test-time compute pipeline employs an iterative loop mechanism of generation, verification, and refinement.73% similarUnverifiedEvaluation can be divided into online evaluation (real-time monitoring in a production environment) and offline evaluation (batch testing on a fixed test set), with the latter being the foundation for rapid iteration.70% similar
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