Unverified50% confidenceTradeoffExact time
大语言模型每次对话独立,无法对整个代码库形成持久认知模型,导致不同会话生成的代码在架构风格、命名规范、错误处理策略上存在系统性不一致
1
Sources
50%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
Related Claims
UnverifiedIn multi-Agent collaborative development, large language models have a tendency to be "overly enthusiastic" and may overstep their responsibilities by modifying code outside their designated scope.81% similarVerifiedLarge language models are prone to forgetting early information or hallucinating during long conversations, which in programming manifests as fabricating non-existent APIs or generating logically incorrect code.80% similarUnverifiedThe semantic structure of code differs fundamentally from natural language, making it difficult for embedding models to accurately preserve information such as identifier naming, call relationships, and scope.79% similarUnverified大语言模型生成代码缺乏对系统整体架构的持续感知,随着项目规模扩大会出现上下文窗口溢出、模块耦合混乱、测试覆盖缺失等问题79% similarVerifiedLarge language models themselves are stateless; each API call is independent, and the model does not automatically remember the content of previous conversations.78% similar
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