Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
arXiv:2602.16512v2 Announce Type: replace 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 fra
The proliferation of static prompting schemes like Chain of Thought has highlighted the need for more dynamic and optimized reasoning frameworks as LLMs become more sophisticated.
This framework addresses key limitations in current large language model prompting, potentially leading to more adaptable, efficient, and powerful AI reasoning capabilities.
The development of 'Framework of Thoughts' introduces a general-purpose, dynamic, and optimized approach to LLM reasoning, moving beyond static, problem-specific prompts.
- · AI developers
- · LLM-powered applications
- · prompt engineering industry
- · AI research institutions
- · developers relying solely on static prompting
- · less adaptable AI frameworks
This could lead to significantly more robust and generalized AI agents capable of handling a wider array of unseen problems.
Improved AI reasoning will accelerate automation in knowledge work, further collapsing complex workflows and SaaS layers.
More efficient AI reasoning could reduce computational overhead and energy consumption for advanced AI tasks, alleviating future energy bottlenecks for computing.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI