A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models
Abstract
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.
Keywords
Cite
@article{arxiv.2601.17426,
title = {A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models},
author = {Zhengqing Zang and Yuqi Ding and Yanmei Gu and Changkai Song and Zhengkai Yang and Guoping Du and Junbo Zhao and Haobo Wang},
journal= {arXiv preprint arXiv:2601.17426},
year = {2026}
}