Unverified60% confidenceFactExact time
The output of LLMs is inherently stochastic, and identical inputs may produce entirely different results across different calls.
2
Sources
60%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
UnverifiedEven with identical inputs, different random seeds or sampling parameters can lead to entirely different outputs from large models.78% 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.76% similarUnverifiedDue to the probabilistic output characteristics of LLMs, even the most rigorous prompts always carry a very small probability of out-of-bounds behavior, which will be triggered in production environments.76% similarUnverifiedLLM outputs are inherently stochastic, and when multiple agent interaction paths are layered on top of each other, the state space expands exponentially, making exhaustive testing almost impossible to cover all potential failure scenarios.74% similarVerifiedLLM calls have inherent uncertainty: the same input may produce different outputs, token consumption and latency fluctuate significantly, and the reasoning path of an Agent is difficult to predict.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/536670API
curl https://kongchang.com/api/v1/knowledge/claims/536670MCP
get_claim(id=536670)