Unverified50% confidenceFactExact time
The output of large language models inherently appears plausible (fluency bias), meaning that even when the reasoning chain is wrong, the generated natural language remains grammatically correct and natural in tone, rendering traditional monitoring approaches based on output format or exception capture ineffective.
1
Sources
50%
Confidence
Long-term
Relevance
8/8/2026
First Seen
Sources
AI Agent 95%成功率陷阱:为什么一次错误预约就该阻止发布
Related Claims
VerifiedCurrent large language models exhibit a pattern in the systems programming domain of being strong on high-frequency knowledge and weak on long-tail knowledge, tending to make reasonable inferences rather than strictly following specifications for edge-case semantics and version-specific compatibility behavior.77% similarVerifiedTypical characteristics of text output by mainstream large language models include well-structured sentence patterns, clear paragraph organization, frequent use of emphatic vocabulary, and explicit logical chains — a phenomenon researchers refer to as "stylistic bias."75% similarUnverified大语言模型本质上是概率性的,相同的输入可能产生不同的输出,这与传统软件的确定性行为截然不同73% similarUnverifiedCurrent large language models lack real-world perception interfaces and causal reasoning capabilities, and are essentially doing pattern matching rather than intent understanding72% similarUnverifiedLarge language models have inherent non-determinism — even with identical prompt inputs, the model may return results that differ in format, content, or even logic at different times.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/706910API
curl https://kongchang.com/api/v1/knowledge/claims/706910MCP
get_claim(id=706910)