# Can Large Language Models Reason about the Region Connection Calculus?

arXiv: https://arxiv.org/abs/2411.19589 · HTML: https://mlsanity.com/paper/2411.19589/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 11 of 11)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [The Prompt Report: A Systematic Survey of Prompt Engineering Techniques](https://mlsanity.com/paper/2406.06608/) (2024)
- [Evaluating Spatial Understanding of Large Language Models](https://mlsanity.com/paper/2310.14540/) (2023)
- [How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models](https://mlsanity.com/paper/2209.01390/) (2022)
- [Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMs](https://mlsanity.com/paper/2304.11164/) (2023)
- [Human-in-the-Loop through Chain-of-Thought](https://mlsanity.com/paper/2306.07932/) (2023)
- [Evaluating the Ability of Large Language Models to Reason about Cardinal Directions](https://mlsanity.com/paper/2406.16528/) (2024)
- [An Evaluation of ChatGPT-4's Qualitative Spatial Reasoning Capabilities in RCC-8](https://mlsanity.com/paper/2309.15577/) (2023)

## Built on by (most cited first, 5 of 5)

- [Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations](https://mlsanity.com/paper/2505.17136/) (2025)
- [Large Language and Reasoning Models are Shallow Disjunctive Reasoners](https://mlsanity.com/paper/2503.23487/) (2025)
- [Evaluating the Ability of Large Language Models to Reason about Cardinal Directions, Revisited](https://mlsanity.com/paper/2507.12059/) (2025)
- [QSTRBench: a New Benchmark to Evaluate the Ability of Language Models to Reason with Qualitative Spatial and Temporal Calculi](https://mlsanity.com/paper/2605.18380/) (2026)
- [LMM Modality Transfer: A Pre-requisite for Autonomous GIS Agents](https://mlsanity.com/paper/2608.06948/) (2026)

## Abstract (arXiv)

Qualitative Spatial Reasoning is a well explored area of Knowledge Representation and Reasoning and has multiple applications ranging from Geographical Information Systems to Robotics and Computer Vision. Recently, many claims have been made for the reasoning capabilities of Large Language Models (LLMs). Here, we investigate the extent to which a set of representative LLMs can perform classical qualitative spatial reasoning tasks on the mereotopological Region Connection Calculus, RCC-8. We conduct three pairs of experiments (reconstruction of composition tables, alignment to human composition preferences, conceptual neighbourhood reconstruction) using state-of-the-art LLMs; in each pair one experiment uses eponymous relations and one, anonymous relations (to test the extent to which the LLM relies on knowledge about the relation names obtained during training). All instances are repeated 30 times to measure the stochasticity of the LLMs.