arXiv:2508.12265v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world tasks requires adapting the reasoning strategy to the demands of the problem, ranging from fast, intuitive responses to deliberate, step-by-step reasoning and tool-augmented thinking. Drawing inspiration from cognitive psychology, we propose a novel taxonomy of LLM reasoning strategies along two knowledge boundaries: a fast/slow boundary separating intuitive from deliberative processes, and an inte
The paper is published as LLMs become increasingly capable, making the differentiation and optimization of their reasoning strategies a critical area of research for practical applications.
A strategic reader should care because improving LLM reasoning directly impacts the scope and reliability of AI agents and automated systems across industries.
The proposed taxonomy provides a structured framework for understanding and developing more adaptive and efficient LLM reasoning, moving beyond 'one-size-fits-all' approaches.
- · AI developers
- · Enterprise software
- · LLM researchers
- · AI-driven automation
- · Inefficient LLM architectures
- · Monolithic AI solutions
This research provides a framework for designing LLMs that can dynamically choose optimal reasoning strategies based on task complexity.
Improved, adaptive LLM reasoning will lead to more robust and reliable AI agents capable of handling a wider range of real-world problems.
The enhanced capabilities of these agents could accelerate the automation of complex white-collar tasks, further collapsing SaaS layers and reshaping professional services.
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