Unverified75% confidenceOpinionTime unknown
LLM的错误往往不是随机分布的，当AI在某个架构假设上出现偏差时，后续所有基于该假设的决策都会系统性地偏离正确方向
1
Sources
75%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
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Related Claims
Unverified绝对偏差通常可通过标定来补偿，而重复性差意味着误差随机不可预测，很难通过校准解决74% similarUnverifiedAgent链式调用多步之后，每次LLM输出的细微差异导致误差逐步放大，最终结果偏离预期73% similarUnverifiedLLMs may make incorrect judgments due to hallucinations, prompt injection, or contextual misunderstanding, and without independent verification mechanisms, faulty reasoning can directly translate into erroneous production operations73% similarUnverifiedTotal prediction error can be decomposed into bias² + variance + irreducible error.73% similar
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