mlsanityData to 7 Oct 2026

Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs

arXiv 2602.16512 ↗First posted 18 Feb 2026cs.AIcited by 0

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models. However, most existing schemes require users to define static, problem-specific reasoning structures that lack adaptability to dynamic or unseen problem types. Additionally, these schemes are often under-optimized in terms of hyperparameters, prompts, runtime, and prompting cost. To address these limitations, we introduce Framework of Thoughts (FoT)--a general-purpose foundation framework for building and optimizing dynamic reasoning schemes. FoT comes with built-in features for hyperparameter tuning, prompt optimization, parallel execution, and intelligent caching, unlocking the latent performance potential of reasoning schemes. We demonstrate FoT's capabilities by implementing three popular schemes--Tree of Thoughts, Graph of Thoughts, and ProbTree--within FoT. We empirically show that FoT enables significantly faster execution, reduces costs, and achieves better task scores through optimization. We release our codebase to facilitate the development of future dynamic and efficient reasoning schemes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 23 of its 69 references that are in the map. First posted on arXiv 18 Feb 2026.

Builds on

23 of 69 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024Demystifying Chains, Trees, and Graphs of Thoughts25 Jan
  2. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Models18 Aug
  3. 2023The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models24 May
  4. 2022Large Language Models are Zero-Shot Reasoners24 May
  5. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Models21 May
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  7. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 16

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  3. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  4. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  5. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  6. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  7. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  8. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
  9. 2025GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningcited by 449
  10. 2023Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performancecited by 143
  11. 2024Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoningcited by 110
  12. 2024Self-Discover: Large Language Models Self-Compose Reasoning Structurescited by 108
4 more
  1. 2023Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generationcited by 103
  2. 2022Compositional Semantic Parsing with Large Language Modelscited by 95
  3. 2023Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questionscited by 19
  4. 2025Adaptive Graph of Thoughts: Test-Time Adaptive Reasoning Unifying Chain, Tree, and Graph Structurescited by 16

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  1. 2023Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
  2. 2025Thought calibration: Efficient and confident test-time scaling
  3. 2025Dynamic Parallel Tree Search for Efficient LLM Reasoning
  4. 2024Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
  5. 2025ThinkSwitcher: When to Think Hard, When to Think Fast
  6. 2024Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
  7. 2026Beyond Fixed Budgets: Characterizing the Inelasticity and Limitations of Tree-of-Thought Reasoning Strategies
  8. 2025ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization