Unverified50% confidenceFactExact time
LLM 在处理复杂任务时容易出现幻觉和逻辑漂移，这与其自回归逐 token 预测机制有关，早期错误会被后续内容放大
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Related Claims
UnverifiedSemantic agents with LLMs at their core understand task intent rather than memorizing operation paths, theoretically enabling a significant improvement in cross-version robustness.77% similarUnverifiedLLM在需求描述模糊时容易过度设计，倾向于生成复杂的企业级架构方案，这源于其基于条件概率的文本续写特性而非工程判断76% similarUnverifiedLLM在涉及精确数值计算、多步逻辑推导时容易产生形式正确但内容错误的幻觉输出76% similarPartially VerifiedLLMs have a hallucination characteristic, potentially generating code that appears plausible but actually contains logical errors or functional defects with confidence, without proactively flagging it.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/490112API
curl https://kongchang.com/api/v1/knowledge/claims/490112MCP
get_claim(id=490112)