Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions.

使用预训练增强学习和蒙特卡罗树搜索，引入了一种称为 “Everything of Thoughts”（XoT）的新思维启发方法，以提升大型语言模型（LLMs）的能力并使其能够高效地推广到未见过的问题。这种方法利用 MCTS-LLM 协作思维修订框架自主产生高质量的综合认知映射，同时赋予 LLMs 进行无约束思考的能力，从而对具有多个解决方案的问题进行灵活的认知映射。