提出通用动态推理框架，让大模型自动优化思考路径。
Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
- 构建可自适应调整的思考结构，支持链式、树状与图式推理。
- 实测推理速度更快、成本更低，任务得分提升显著。
- 内置超参调优与智能缓存，适合研究者快速开发新推理方法。
链式思维、树状思维和图式思维等提示策略能显著提升大语言模型的推理能力。然而，现有方案通常需要用户手动设计静态、问题特定的推理结构，难以适应动态或未知的问题类型。此外，这些方案在超参数、提示词、运行时间和提示成本方面普遍缺乏优化。为解决上述问题，我们提出「思维框架」（Framework of Thoughts, FoT）——一种通用基础框架，用于构建和优化动态推理策略。FoT 内置超参数调优、提示优化、并行执行和智能缓存功能，释放推理策略的潜在性能。我们在 FoT 中实现了三种主流方案：树状思维、图式思维和 ProbTree。实验证明，FoT 能显著加快执行速度、降低计算成本，并取得更优的任务得分。代码已开源，以促进未来高效动态推理方法的发展。
原文摘要 · Abstract (English)
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.
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