Unverified50% confidenceFactExact time
LLM在对话中依赖上下文动态推理能临场识别异常，但生成的代码脚本固化后逻辑分支是静态的，无法感知未见过的数据模式，这被称为分布偏移（Distribution Shift）
1
Sources
50%
Confidence
Long-term
Relevance
7/18/2026
First Seen
Sources
Related Claims
UnverifiedLLM在对话中能利用上下文动态推理识别异常，但生成的静态代码脚本无法感知未见过的数据模式，可能导致静默失效85% similarUnverifiedLLM生成的代码是静态逻辑树，当生产数据出现新异常模式时会静默按错误逻辑执行，形成无声失效（Silent Failure）74% similarUnverifiedTransformer-based LLMs perform an independent forward-pass computation for each inference, with model weights fixed during the inference phase; what is referred to as context memory is essentially the re-injection of historical conversation text into the prompt.72% similarUnverifiedDuring each inference, an LLM only processes the token sequence of the current input and does not retain persistent state across sessions (statelessness).72% similarVerifiedThe output of LLMs is inherently non-deterministic; even with the same Prompt as input, the model may return results with different formats, content, or even logic at different times.72% similar
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