A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911396495622144 |
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| author | Zang, Zhengqing Ding, Yuqi Gu, Yanmei Song, Changkai Yang, Zhengkai Du, Guoping Zhao, Junbo Wang, Haobo |
| author_facet | Zang, Zhengqing Ding, Yuqi Gu, Yanmei Song, Changkai Yang, Zhengkai Du, Guoping Zhao, Junbo Wang, Haobo |
| contents | Human logic has gradually shifted from intuition-driven inference to rigorous formal systems. Motivated by recent advances in large language models (LLMs), we explore whether LLMs exhibit a similar evolution in the underlying logical framework. Using existential import as a probe, we for evaluate syllogism under traditional and modern logic. Through extensive experiments of testing SOTA LLMs on a new syllogism dataset, we have some interesting findings: (i) Model size scaling promotes the shift toward modern logic; (ii) Thinking serves as an efficient accelerator beyond parameter scaling; (iii) the Base model plays a crucial role in determining how easily and stably this shift can emerge. Beyond these core factors, we conduct additional experiments for in-depth analysis of properties of current LLMs on syllogistic reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17426 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models Zang, Zhengqing Ding, Yuqi Gu, Yanmei Song, Changkai Yang, Zhengkai Du, Guoping Zhao, Junbo Wang, Haobo Artificial Intelligence Logic in Computer Science Human logic has gradually shifted from intuition-driven inference to rigorous formal systems. Motivated by recent advances in large language models (LLMs), we explore whether LLMs exhibit a similar evolution in the underlying logical framework. Using existential import as a probe, we for evaluate syllogism under traditional and modern logic. Through extensive experiments of testing SOTA LLMs on a new syllogism dataset, we have some interesting findings: (i) Model size scaling promotes the shift toward modern logic; (ii) Thinking serves as an efficient accelerator beyond parameter scaling; (iii) the Base model plays a crucial role in determining how easily and stably this shift can emerge. Beyond these core factors, we conduct additional experiments for in-depth analysis of properties of current LLMs on syllogistic reasoning. |
| title | A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2601.17426 |