paper-with-me

Papers

A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models

2026-01-24 · Zhengqing Zang, Yuqi Ding, Yanmei Gu, Changkai Song, Zhengkai Yang, Guoping Du, Junbo Zhao, Haobo Wang arxiv

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.

📄 PDF Abstract BibTeX arXiv:2601.17426

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases

2023-06-21 · Risako Ando, Takanobu Morishita, Hirohiko Abe, Koji Mineshima 외

This paper investigates whether current large language models exhibit biases in logical reasoning, similar to humans. Specifically, we focus on syllogistic reasoning, a well-studied form of inference in the cognitive sci…

Logical Reasoning

Exploring the Landscape of Relational Syllogistic Logics

2018-09-03 · Alex Kruckman, Lawrence S. Moss

This paper explores relational syllogistic logics, a family of logical systems related to reasoning about relations in extensions of the classical syllogistic. These are all decidable logical systems. We prove completene…

An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making

2026-01-01 · Tiansi Dong, Henry He, Pietro Liò, Mateja Jamnik arxiv

This paper compares three methodological categories of neural reasoning: LLM reasoning, supervised learning-based reasoning, and explicit model-based reasoning. LLMs remain unreliable and struggle with simple decision-ma…

Data-driven Machine Learning Cannot Reach Symbolic-level Logical Reasoning -- The Limit of the Scaling Law

2026-06-24 · Tiansi Dong, Mateja Jamnik, Pietro Liò arxiv

Sphere neural networks have achieved symbolic level syllogistic reasoning without training data, raising the question of where the limit of the scaling law for logical reasoning lies, i.e., whether data-driven machine le…

Logical Reasoning

Understanding Syllogistic Reasoning in LLMs from Formal and Natural Language Perspectives

2025-12-14 · Aheli Poddar, Saptarshi Sahoo, Sujata Ghosh arxiv

We study syllogistic reasoning in LLMs from the logical and natural language perspectives. In process, we explore fundamental reasoning capabilities of the LLMs and the direction this research is moving forward. To aid i…

Natural Language Understanding