LLMs Improve Spatial Reasoning by Switching to Symbolic Representations.

Human reasoning often involves switching between different modalities, such as language and visual aids, especially when tackling complex problems. This research explores whether Large Language Models (LLMs) can benefit from a similar approach, specifically by converting textual-spatial problems into geometric representations like grids or layouts. The study introduces a novel switching metric that assesses when it's beneficial for an LLM to shift from purely language-based reasoning to a more structured, symbolic modality. The findings indicate that this modality switching can dramatically enhance LLM performance, with improvements of up to 42% in spatial reasoning tasks. This highlights the critical role of modality choice in the effectiveness of AI reasoning systems. The proposed method offers a foundational step towards enabling LLMs to intelligently select the most appropriate representation for a given problem, mirroring human cognitive flexibility.