Fast, Slow, and Tool-augmented Thinking for LLMs: A Review

• URL: http://arxiv.org/abs/2508.12265v1
• Date: Sun, 17 Aug 2025 07:20:32 GMT
• Title: Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
• Authors: Xinda Jia, Jinpeng Li, Zezhong Wang, Jingjing Li, Xingshan Zeng, Yasheng Wang, Weinan Zhang, Yong Yu, Weiwen Liu,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains.<n>Effective reasoning in real-world tasks requires adapting the reasoning strategy to the demands of the problem.<n>We propose a novel taxonomy of LLM reasoning strategies along two knowledge boundaries.
• Score: 57.16858582049339
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

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