Addressing the discrepancies between predictions and actual outcomes often
aids individuals in expanding their thought processes and engaging in
reflection, thereby facilitating reasoning in the correct direction. In this
paper, we introduce $\textbf{PreAct}$, an agent framework that integrates
$\textbf{pre}$diction with $\textbf{rea}$soning and $\textbf{act}$ion.
Leveraging the information provided by predictions, a large language model
(LLM) based agent can offer more diversified and strategically oriented
reasoning, which in turn leads to more effective actions that help the agent
complete complex tasks. Our experiments demonstrate that PreAct outperforms the
ReAct approach in accomplishing complex tasks and that PreAct can be
co-enhanced when combined with Reflexion methods. We prompt the model with
different numbers of historical predictions and find that historical
predictions have a sustained positive effect on LLM planning. The differences
in single-step reasoning between PreAct and ReAct show that PreAct indeed
offers advantages in terms of diversity and strategic directivity over ReAct.

本文介绍了一种名为 PreAct 的代理框架，该框架将预测、推理和行动相结合，通过利用预测信息，基于大型语言模型的代理能够提供更多样化和战略性导向的推理，从而使代理执行复杂任务更加有效。实验结果表明，PreAct 在完成复杂任务方面优于 ReAct 方法，而与反思方法相结合可以进一步增强 PreAct 的性能。同时，我们还发现历史预测对于基于大型语言模型的规划具有持续的积极影响。PreAct 和 ReAct 之间的单步推理差异表明 PreAct 确实在多样性和战略方向上具有优势。