Verified65% confidenceFactExact time
LLM 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.
3
Sources
65%
Confidence
Long-term
Relevance
9/5/2026
First Seen
Sources
Related Entities
Related Claims
Unverified由于LLM的输出带有不确定性,Agent场景下无法保证两次调用生成的参数完全一致,这使得幂等键的生成和匹配比传统系统更复杂79% similarVerifiedLLM outputs are probabilistic, carry hallucination risks, and may misjudge context and invoke tools that should not be invoked.77% similarUnverifiedLLM推理带来了泛化能力,却也引入了不确定性和额外的延迟成本,浏览器Agent与传统RPA在企业场景中往往是互补而非替代关系76% similarUnverifiedThe output of large model applications is non-deterministic—the same input may yield different results—making it necessary to establish an observable logging and evaluation system to locate problems.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/862206API
curl https://kongchang.com/api/v1/knowledge/claims/862206MCP
get_claim(id=862206)