Unverified50% confidenceTradeoffExact time
Model drift can cause downstream parsing logic in production systems that rely on LLMs to fail silently—no errors are reported but the output deviates from expectations.
1
Sources
50%
Confidence
Long-term
Relevance
9/30/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedThe 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.77% similarUnverifiedFor applications that rely on structured output (such as JSON Schema) or function calling, behavioral drift in the new model may trigger parsing errors or business logic anomalies.72% similarUnverifiedLLM 在处理复杂任务时容易出现幻觉和逻辑漂移,这与其自回归逐 token 预测机制有关,早期错误会被后续内容放大72% similarUnverifiedLLM在推理阶段参数是冻结的,模型不会因用户交互而更新权重,因此本质上是无状态的72% similarUnverified对于确定性逻辑故障(模型输出缺陷)应搭建自纠错闭环,把结构化报错信息回传给大模型重新生成参数72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/961751API
curl https://kongchang.com/api/v1/knowledge/claims/961751MCP
get_claim(id=961751)