Unverified50% confidenceFactExact time
The model's confidence is calibrated, trained via reinforcement learning with strict scoring rules, statistically aligning output probabilities with actual accuracy.
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9/25/2026
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UnverifiedResult validation requires establishing objective quantitative standards—such as classification accuracy, reply compliance, and manual correction ratio—and calculating statistics over a sufficient sample size.78% similarUnverifiedDuring model selection, it is necessary to confirm that the similarity objective function used during model training is consistent with the metric used during retrieval; otherwise, systematic bias will occur.77% similarUnverifiedFrom checkpoint testing to official release, a model typically still needs to go through processes such as fine-tuning optimization, safety hardening, inference efficiency optimization, and API adaptation.77% similarUnverifiedA benchmarking mechanism should be established that uses standard questions with known answers to continuously verify the accuracy of Agent outputs.76% similarUnverifiedThe verification and calibration components are critical for making a model's confidence levels trustworthy.76% similar
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