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This paper investigates how different formal knowledge representation notations affect language models' syllogistic reasoning, extending FOLIO and P-FOLIO datasets and introducing the CLGC library for generating syllogisms.
This paper investigates whether diagrammatic representations like Euler and linear diagrams improve LLM reasoning on syllogistic tasks, finding limited benefit compared to natural language or logical notation.
The paper argues that data-driven machine learning systems, including GPT-5, cannot achieve symbolic-level logical reasoning through scaling alone, due to inherent limitations in distinguishing logical structures from statistical regularities.