Unverified50% confidenceFactExact time
The plugin mechanism is used to compensate for the inherent limitation that a large model's knowledge base is pre-trained, static, and has a cutoff date—the model cannot answer questions about events after the training cutoff and must call search-type plugins for real-time retrieval.
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50%
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Long-term
Relevance
9/30/2026
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Related Claims
UnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.78% similarUnverifiedThe knowledge of large language models is cut off at the point in time of the training data, and they cannot directly interact with external systems; the plugin mechanism addresses this fundamental limitation by calling external APIs.77% similarUnverified大模型在训练时学习的是静态知识模式，难以感知真实业务场景中界面元素属性值动态变化的运行时不确定性76% similarVerifiedCurrent large models are essentially static—once training is complete, the weights are frozen and knowledge remains at the training cutoff date.75% similarUnverified实时在线学习要求智能体在连续流动的时间中同步完成感知、决策与参数更新，与大语言模型训练-推理分离的范式形成对比75% similar
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