Unverified60% confidenceFactExact time
Summaries generated by LLMs are probabilistic; the same input may produce different results at different temperature settings, posing a reproducibility risk.
2
Sources
60%
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81% similarUnverifiedLLM judge results are affected by factors such as temperature, prompt wording, and model version updates, causing the same set of test cases to potentially produce different conclusions when run at different times.78% similarVerifiedThe 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.77% similarUnverifiedAn LLM outputs a probability distribution when generating each word, and the higher the temperature the more random the sampling; even if two assistants recall the same information sources, different temperature settings can lead to different wording or even a different order of companies mentioned.75% similarVerifiedLLM outputs are probabilistic, carry hallucination risks, and may misjudge context and invoke tools that should not be invoked.75% similar
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