Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
arXiv:2602.16512v1 Announce Type: new Abstract: 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
arXiv:2602.16512v1 Announce Type: new Abstract: 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.
Executive Summary
The article introduces Framework of Thoughts (FoT), a general-purpose foundation framework for building and optimizing dynamic reasoning schemes in large language models. FoT addresses limitations of existing prompting schemes by incorporating hyperparameter tuning, prompt optimization, parallel execution, and intelligent caching. The authors demonstrate FoT's capabilities by implementing three popular schemes and show significant improvements in execution speed, cost reduction, and task scores. The codebase is released to facilitate future development. FoT's adaptability and optimization features make it a valuable contribution to the field, particularly in applications where dynamic reasoning is crucial.
Key Points
- ▸ FoT is a general-purpose foundation framework for dynamic reasoning schemes
- ▸ FoT addresses limitations of existing prompting schemes
- ▸ FoT significantly improves execution speed, cost reduction, and task scores
Merits
Strength in Adaptability
FoT's adaptability to dynamic and unseen problem types addresses a significant limitation of existing schemes, making it a valuable contribution to the field.
Optimization Features
FoT's built-in features for hyperparameter tuning, prompt optimization, parallel execution, and intelligent caching unlock the latent performance potential of reasoning schemes.
Demerits
Limited Explanation of Theoretical Foundations
The article could benefit from a more detailed explanation of the theoretical foundations of FoT and how it relates to existing work in the field.
Lack of Extensive Comparative Analysis
The authors primarily focus on demonstrating FoT's capabilities rather than conducting an extensive comparative analysis with existing schemes.
Expert Commentary
While FoT is a significant contribution to the field, its limitations, such as the lack of extensive comparative analysis and detailed explanation of theoretical foundations, should be addressed in future work. The authors' focus on demonstrating FoT's capabilities rather than providing an in-depth analysis of its theoretical underpinnings is a notable omission. Nevertheless, FoT's adaptability and optimization features make it a valuable tool for developers and researchers working on large language models. As the field continues to evolve, it is likely that FoT will become a standard framework for building and optimizing dynamic reasoning schemes.
Recommendations
- ✓ Future work should focus on providing a more detailed explanation of the theoretical foundations of FoT and conducting an extensive comparative analysis with existing schemes.
- ✓ Researchers and developers should explore the potential applications of FoT in various domains, particularly in areas such as natural language processing and decision-making.
