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On Spatial Reasoning and Perspective Transformation in Language Models

Authors Haotong Zhang , Ian Pratt-Hartmann

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
  • Computing methodologies → Spatial and physical reasoning
Keywords
  • Spatial Reasoning
  • Language Models
  • Perspective Transformation
  • Spatial Cognition

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Abstract

In this paper, we investigate the performance of language models on spatial reasoning with textually presented data, with particular focus on the ability to switch between different perspectives (route vs. survey) - an important component of human spatial reasoning. Our methodology involves the construction of large, automatically labelled corpora, as opposed to crowd-sourced, human-annotated datasets; this approach focuses attention on the language models' command of underlying geometrical principles, rather than their access to background knowledge and commonsense rules-of-thumb. Results reveal that language models can acquire basic spatial reasoning ability after finetuning on targeted tasks, though they do not fully capture the underlying rules. Furthermore, language models show significant differences from humans in the perspective-transformation task, exhibiting distinct patterns of performance across perspective conditions.

Cite As Get BibTex

Haotong Zhang and Ian Pratt-Hartmann. On Spatial Reasoning and Perspective Transformation in Language Models. In 17th International Conference on Spatial Information Theory (COSIT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 393, pp. 6:1-6:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.COSIT.2026.6

Author Details

Haotong Zhang
  • Department of Computer Science, University of Manchester, UK
Ian Pratt-Hartmann
  • Department of Computer Science, University of Manchester, UK
  • Instytut Informatyki, Uniwersytet Opolski, Opole, Poland

Funding

  • Zhang, Haotong: Supported by the University of Manchester.
  • Pratt-Hartmann, Ian: Supported by the Polish NCN, grant number 2025/57/B/ST6/04170.

Acknowledgements

We would like to acknowledge the use of the Computational Shared Facility at the University of Manchester. We also thank the anonymous reviewers for their helpful feedback.

Supplementary Materials

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