Verified65% confidenceFactExact time
The non-determinism of LLMs stems from sampling strategies; the Temperature parameter controls the randomness of the output distribution, while Top-p and Top-k further constrain the range of candidate tokens.
3
Sources
65%
Confidence
Long-term
Relevance
7/8/2026
First Seen
Sources
Context Warp Drive: A Deep Dive into Deterministic Context Folding for AI Agents
hackernewshackernews7/7/2026
Related Claims
UnverifiedLLM在不同温度参数、上下文长度、随机种子下可能对同一攻击payload给出截然不同的响应，因此LLM安全测试的通过标准本质上是统计性的80% similarUnverifiedTop-p（核采样）通过限定候选token的累积概率范围来控制输出多样性，与Temperature配合使用可实现更精细的输出调节78% similarVerifiedTemperature controls the flatness of the sampling distribution; a higher value tends toward selecting lower-probability words, while Top-p dynamically narrows the candidate vocabulary set through a cumulative probability threshold.78% similarUnverifiedSummaries generated by LLMs are probabilistic; the same input may produce different results at different temperature settings, posing a reproducibility risk.77% similarUnverifiedCommon sampling strategies include top-k sampling (retaining only the k tokens with the highest probability), top-p sampling (dynamically selecting the minimum set of tokens whose cumulative probability just exceeds a threshold p), and the temperature parameter (controlling the sharpness of the distribution by scaling the logits).76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/289575API
curl https://kongchang.com/api/v1/knowledge/claims/289575MCP
get_claim(id=289575)