concept

Tree of Thoughts

Also known as: ToT, Tree of Thoughts, Tree-of-Thought

Facts (31)

Sources

Grounding LLM Reasoning with Knowledge Graphs - arXiv arxiv.org arXiv Dec 4, 2025 21 facts

claimTree of Thoughts (ToT) and Graph of Thoughts (GoT) reasoning methods exhibit exponential growth in computational complexity due to their branching structures.

claimTree of Thoughts (ToT) and Graph of Thoughts (GoT) reasoning strategies exhibit more 'answer found but not returned' error cases than Chain of Thought (CoT), suggesting better retrieval capabilities but occasional failures in synthesis.

claimWithin the Tree of Thoughts (ToT) framework, the 'Select' state evaluator generally yields slightly better results than the 'Score' state evaluator, particularly in the context of agent performance.

claimThe framework proposed in 'Grounding LLM Reasoning with Knowledge Graphs' incorporates multiple reasoning strategies, specifically Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT).

claimTree-of-Thought (ToT) generalizes Chain-of-Thought by modeling the reasoning process as a tree, enabling simultaneous exploration of multiple reasoning paths.

procedureThe authors implement two versions of heuristic functions for Tree-of-Thought (ToT) to select top states: (1) Selection, where the LLM directly chooses the top states to proceed, discarding others.

claimThe Tree of Thought (ToT) reasoning strategy incurs increased inference time as a trade-off for its performance improvements, highlighting the cost of inference-time reasoning interventions.

claimIn Tree-of-Thought (ToT), candidate thoughts are evaluated by a heuristic scoring function that guides the selection and pruning of branches.

perspectiveThe higher computational complexity of Graph-of-Thought (GoT) does not consistently translate to improved accuracy compared to Tree-of-Thought (ToT), suggesting diminishing returns for the increased cost.

claimThe framework evaluates three reasoning strategies: Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT).

measurementThe Tree of Thought (ToT) reasoning strategy achieved performance improvements of 54.74% in agent performance and 11.74% in exploration mode compared to the Chain of Thought (CoT) baseline.

procedureThe experimental implementation extends the Agent and Automatic Graph Exploration methods with three reasoning strategies during inference: Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT).

claimThe Tree of Thoughts (ToT) strategy demonstrated superior performance by effectively exploring multiple reasoning paths, showcasing the benefits of inference-time interventions that diversify reasoning trajectories.

procedureThe framework for grounding LLM reasoning in knowledge graphs integrates each reasoning step with structured graph retrieval and combines strategies like Chain of Thought (CoT), Tree of Thoughts (ToT), and Graph of Thoughts (GoT) with adaptive graph search.

claimThe Graph of Thought (GoT) reasoning strategy did not outperform the Tree of Thought (ToT) strategy in the reported experiments.

claimThe Graph of Thoughts (GoT) strategy did not significantly outperform the Tree of Thoughts (ToT) strategy, suggesting that merging divergent reasoning paths remains a challenging intervention design problem.

procedureFor Tree-of-Thought (ToT) and Graph-of-Thought (GoT) reasoning strategies, the evaluation includes the impact of stepwise decision-making using two State Evaluation methods: Selection and Score.

claimIn the Tree of Thoughts (ToT) reasoning strategy, performance shows a slight upward trend as tree width increases, with a more pronounced performance difference observed when moving from one branch to two branches compared to Chain of Thought (CoT).

procedureThe method in 'Grounding LLM Reasoning with Knowledge Graphs' combines reasoning strategies (Chain-of-Thought, Tree-of-Thought, Graph-of-Thought) with two graph interaction methods: an agent to navigate the graph, and an automatic graph exploration mechanism based on generated text.

claimThe Tree of Thought (ToT) reasoning strategy enhances reasoning accuracy by using branching interventions to explore multiple candidate paths, particularly when coupled with evaluators that prune unpromising trajectories.

claimTree-of-Thought (ToT) reasoning introduces exponential growth in computational cost with respect to depth due to its exploration of branches and selection of continuations per level.

The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org arXiv Jul 11, 2024 4 facts

claimLLM-based Agentic Architectures (LAAs) utilize advanced reasoning mechanisms such as Chain-of-Thought (CoT) and Tree-of-Thoughts (ToT) to solve complex problems by analogizing human reasoning steps.

referenceShunyu Yao et al. introduced the 'Tree of Thoughts' framework, which enables deliberate problem solving using large language models.

claimTree-of-Thought (ToT) prompting extends the Chain-of-Thought approach by allowing large language models to explore multiple reasoning paths simultaneously within a tree structure.

claimAutomating code generation, optimizing hybrid Program-of-Thought (PoT)/Chain-of-Thought (CoT)/Tree-of-Thought (ToT) models, incorporating self-verification and self-correction, and adopting PoT into domain-specific applications like logical deduction and scientific discovery can significantly advance the capabilities of LLM-empowered Autonomous Agents.

The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org arXiv 2 facts

claimTree-of-Thought (ToT) prompting allows LLMs to explore multiple reasoning paths simultaneously in a tree structure.

claimChain-of-Thought (CoT) and Tree-of-Thoughts (ToT) reasoning mechanisms mitigate the limitations of token-level constraints in Large Language Models (LLMs).

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv May 20, 2024 2 facts

claimPrompt engineering techniques, including Chain of Thought (CoT), Tree of Thought (ToT), Graph of Thoughts (GoT), and ReAct (Reason and Act), have demonstrated significant improvements in the reasoning abilities and task-specific actions of Large Language Models.

Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org arXiv 2 facts

referenceYao et al. (2023a) authored the paper titled 'Tree of thoughts: Deliberate problem solving with large language models', published as arXiv:2305.10601.

claimMethods like chain of thoughts and tree of thoughts prompting can act as sanity checks to examine the deceptive nature of Large Language Models (Connor Leahy 2023; Yao et al. 2023a).