Unverified50% confidenceFactExact time
The study searched for balanced thresholds across 30 preset instructional scenarios, with results showing that the optimal balanced threshold varies greatly across different models and scenarios.
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10/4/2026
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UnverifiedThirty is an empirical rough threshold; the actual critical point depends on the verbosity of tool descriptions, tool discriminability, and the model's context-handling capacity.65% similarUnverified不同任务类型的涌现阈值存在巨大差异，某些推理能力在模型规模跨越临界点时突然涌现，而标准代码补全呈现平滑饱和曲线65% similarUnverifiedWhen the underlying KT model changes (e.g., migrating from BKT to DKT) or instructional priorities are adjusted, the mastery threshold must be recalibrated.62% similarVerifiedDrift detection involves a trade-off: setting the threshold too sensitively leads to frequent false alarms and unnecessary retraining, while setting it too loosely may allow model performance to continuously degrade without being detected.61% similarVerifiedOn convergent tasks, there is a clear performance saturation phenomenon after models reach a certain scale, where continued increases in parameter count or training data yield rapidly diminishing marginal contributions to accuracy.61% similar
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