Unverified50% confidenceFactExact time
由于LLM输出具有非确定性，Temporal的重放机制在处理AI活动时记录实际的API响应结果而非重新执行调用
1
Sources
50%
Confidence
Long-term
Relevance
8/5/2026
First Seen
Sources
Related Claims
UnverifiedLLM outputs are probabilistic—the same specification may produce drastically different behaviors across different runs—and therefore declarative specifications for AI Agents more closely resemble constraint declarations rather than state declarations.73% 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.71% similarPartially VerifiedIn LLM 0.32a0, response.reply() supports automatically executing tool calls and returning the results to the model, simplifying the implementation of multi-turn Agent loops.70% similarUnverifiedThe core idea of ReAct is to have LLMs perform explicit reasoning before generating actions, and to feed execution results back to the model as observations to form a closed loop.69% 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67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/691080API
curl https://kongchang.com/api/v1/knowledge/claims/691080MCP
get_claim(id=691080)