Unverified50% confidenceFactExact time
ML-KEM 的安全性建立在模格上的学习带误差（Module-LWE）问题之上，具有最坏情形到平均情形的归约证明
1
Sources
50%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
后量子密码学Python实战：ML-KEM密钥封装机制详解
hackernewshackernews7/4/2026
Related Claims
UnverifiedLLM outputs are probabilistic, carry hallucination risks, and may misjudge context and invoke tools that should not be invoked.62% similarUnverifiedLLM applications face unique challenges including output non-determinism, subjectivity in evaluation, unpredictable costs, and security risks.61% similarUnverifiedLLM在面对开放性问题时倾向于输出训练数据中频率最高的安全表达，即听起来正确但信息密度极低的通用结论61% similar
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