Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
Xinda Jia , Jinpeng Li , Zezhong Wang , Jingjing Li , Xingshan Zeng , Yasheng Wang , Weinan Zhang , Yong Yu , Weiwen Liu
Large Language Models (LLMs) have demonstrated significant progress in reasoning across diverse domains. However, effective reasoning in real-world tasks requires adapting both the computational depth and the knowledge source to the demands of the problem, ranging from fast, intuitive responses to deliberate, step-by-step reasoning and from purely internal reasoning to externally tool-augmented processes. Drawing inspiration from cognitive psychology, we propose a novel taxonomy of LLM reasoning strategies along two orthogonal knowledge boundaries: (1) a fast/slow boundary separating intuitive from deliberative processes, and (2) an internal/external boundary distinguishing reasoning grounded in the model’s parameters from reasoning augmented by external tools. We systematically survey recent work on adaptive reasoning in LLMs and categorize methods based on key decision factors. We conclude by highlighting open challenges and future directions toward more adaptive, efficient, and reliable LLMs.
Natural Language Processing / Artificial Intelligence / Machine Learning
The Author(s) 2026.
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