title: Everything of Thoughts: Defying the Law of Penrose Triangle for Thought
    Generation
summary: 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.
paper_authors._id: 654c3f255255ee867109d7a5
paper_authors.name: Ruomeng Ding
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-09T09:41:50.061Z
paper_authors._id: 654875f69295970f87ba1600
paper_authors.avatarUrl: /avatars/ee4623842d6240467838b764692dd771.svg
paper_authors.fullname: Ruomeng Ding
paper_authors.type: user
paper_authors.user: Czardas
paper_authors._id: 654c3f255255ee867109d7a6
paper_authors.name: Chaoyun Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-10T07:58:22.464Z
paper_authors._id: 654dbac9938fbf1e696be8aa
paper_authors.avatarUrl: /avatars/b3c4035c48169c1bfb04a439fce3499f.svg
paper_authors.fullname: Chaoyun Zhang
paper_authors.type: user
paper_authors.user: vyokky
paper_authors._id: 654c3f255255ee867109d7a7
paper_authors.name: Lu Wang
paper_authors._id: 654c3f255255ee867109d7a8
paper_authors.name: Yong Xu
paper_authors._id: 654c3f255255ee867109d7a9
paper_authors.name: Minghua Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-09T09:42:56.598Z
paper_authors._id: 653b52098abd634b83f4fd38
paper_authors.avatarUrl: /avatars/c9c5023b030e94c1b1abfb7a1c1dfaf3.svg
paper_authors.fullname: MingHua Ma
paper_authors.type: user
paper_authors.user: Gezelligheid520
paper_authors._id: 654c3f255255ee867109d7aa
paper_authors.name: Wei Zhang
paper_authors._id: 654c3f255255ee867109d7ab
paper_authors.name: Si Qin
paper_authors._id: 654c3f255255ee867109d7ac
paper_authors.name: Saravan Rajmohan
paper_authors._id: 654c3f255255ee867109d7ad
paper_authors.name: Qingwei Lin
paper_authors._id: 654c3f255255ee867109d7ae
paper_authors.name: Dongmei Zhang
paper_title: Everything of Thoughts: Defying the Law of Penrose Triangle for Thought
    Generation
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) thoughts performance efficiency flexibility Everything of Thoughts (XoT) Penrose triangle pretrained learning Monte Carlo Tree Search (MCTS) external domain knowledge MCTS-LLM collaborative thought revision framework high-quality comprehensive cognitive mappings unconstrained thinking