AoT包括四个主要组成部分： 1）将复杂问题分解为可理解的子问题，同时考虑它们的相互关系和单独解决的容易程度； 2）以连续和不间断的方式为这些子问题提出连贯的解决方案；

可以看到aot和tot的方法的区别是，tot会对节点进行剪枝，然后推理过程是一个树的结构；aot利用示例的方式模仿dfs或者bfs，能够激发llm的能力（论文说在gpt4上有很好的效果，估计对llm本身的能力要求比较高），得到更好的结果。

如静态编译的代码程序在执行前全部被翻译为机器码，这种 AOT（Ahead of time），即提前编译的方式，AOT 更适合移动、嵌入式深度学习应用。在 MLIR + TensorFLow 框架中目前支持 AOT 和 JIT 的编译方式，不过在 AI 领域，目前 AOT 的典型代表有：

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r/attackontitan: A subreddit dedicated to Attack on Titan (進撃の巨人) manga and anime series.

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