Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915448689262592 |
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| author | Jia, Xinda Li, Jinpeng Wang, Zezhong Li, Jingjing Zeng, Xingshan Wang, Yasheng Zhang, Weinan Yu, Yong Liu, Weiwen |
| author_facet | Jia, Xinda Li, Jinpeng Wang, Zezhong Li, Jingjing Zeng, Xingshan Wang, Yasheng Zhang, Weinan Yu, Yong Liu, Weiwen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Jia, Xinda Li, Jinpeng Wang, Zezhong Li, Jingjing Zeng, Xingshan Wang, Yasheng Zhang, Weinan Yu, Yong Liu, Weiwen Computation and Language 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. |
| title | Fast, Slow, and Tool-augmented Thinking for LLMs: A Review |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.12265 |