Expired50% confidenceFactExact time
In non-deterministic LLM systems, even when using the same prompt and parameters, outputs can differ due to the model's internal sampling mechanisms (temperature, top-p, etc.), API version updates, or floating-point arithmetic precision differences.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
Related Claims
UnverifiedThe temperature parameter design in the language model sampling process means that the same prompt may produce completely different outputs across different calls, so retrying itself does not guarantee convergence.77% similarUnverifiedLLM 的输出非确定性根源于自回归生成机制,即使输入相同,Temperature、Top-p 等采样参数也会导致不同输出77% similarVerifiedThe inconsistency of LLMs is closely related to the model's temperature parameter and stochastic sampling process, and multiple calls to the same model with the same question may produce different answers.77% similarVerifiedThe output of LLMs is inherently non-deterministic; even with the same Prompt as input, the model may return results with different formats, content, or even logic at different times.76% similarUnverifiedLLM网关面临泄漏抽象问题,模型能力的本质差异会泄漏到上层,如system prompt格式、temperature参数语义、function calling调用规范差异76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/50176API
curl https://kongchang.com/api/v1/knowledge/claims/50176MCP
get_claim(id=50176)