# Spatial Reasoning via Modality Switching Between Language and Symbolic Representations

arXiv: https://arxiv.org/abs/2606.31285 · HTML: https://mlsanity.com/paper/2606.31285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 27 of its 57 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Placed in

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

## Builds on (most cited first, 27 of 27)

- [RouteLLM: Learning to Route LLMs with Preference Data](https://mlsanity.com/paper/2406.18665/) (2024)
- [AdaptThink: Reasoning Models Can Learn When to Think](https://mlsanity.com/paper/2505.13417/) (2025)
- [RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics](https://mlsanity.com/paper/2411.16537/) (2024)
- [Vision Language Models in Autonomous Driving: A Survey and Outlook](https://mlsanity.com/paper/2310.14414/) (2023)
- [Thinkless: LLM Learns When to Think](https://mlsanity.com/paper/2505.13379/) (2025)
- [AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning](https://mlsanity.com/paper/2505.11896/) (2025)
- [SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning](https://mlsanity.com/paper/2104.05832/) (2021)
- [StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts](https://mlsanity.com/paper/2204.08292/) (2022)
- [SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and Chain-of-Thought for Embodied Task Planning](https://mlsanity.com/paper/2501.10074/) (2025)
- [Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text](https://mlsanity.com/paper/2307.07696/) (2023)
- [Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning](https://mlsanity.com/paper/2210.16952/) (2022)
- [Advancing Spatial Reasoning in Large Language Models: An In-Depth Evaluation and Enhancement Using the StepGame Benchmark](https://mlsanity.com/paper/2401.03991/) (2024)
- [Spatial Reasoning in Multimodal Large Language Models: A Survey of Tasks, Benchmarks and Methods](https://mlsanity.com/paper/2511.15722/) (2025)
- [Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMs](https://mlsanity.com/paper/2304.11164/) (2023)
- [DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search](https://mlsanity.com/paper/2410.03864/) (2024)
- [NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization](https://mlsanity.com/paper/2412.15588/) (2024)
- [CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance](https://mlsanity.com/paper/2502.04350/) (2025)
- [An Evaluation of ChatGPT-4's Qualitative Spatial Reasoning Capabilities in RCC-8](https://mlsanity.com/paper/2309.15577/) (2023)
- [LExT: Towards Evaluating Trustworthiness of Natural Language Explanations](https://mlsanity.com/paper/2504.06227/) (2025)
- [Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data](https://mlsanity.com/paper/2409.12437/) (2024)
- [Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning](https://mlsanity.com/paper/2310.16731/) (2023)
- [Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents](https://mlsanity.com/paper/2508.07642/) (2025)
- [Neuro-symbolic Training for Reasoning over Spatial Language](https://mlsanity.com/paper/2406.13828/) (2024)
- [DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text](https://mlsanity.com/paper/2310.12557/) (2023)
- [An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning](https://mlsanity.com/paper/2503.05439/) (2025)
- [Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis](https://mlsanity.com/paper/2509.07122/) (2025)
- [SATURN: Symbolic Spatial Reasoning for Multi-Perspective Grounding](https://mlsanity.com/paper/2606.22694/) (2026)

## Built on by (most cited first, 1 of 1)

- [Evidence Before Sampling: Interpretable Implicit Negative Candidate Discovery for Recommendation](https://mlsanity.com/paper/2610.07708/) (2026)

## Abstract (arXiv)

Human reasoning is inherently multimodal: when problems become difficult, we rarely think in words alone. We often externalize our reasoning by sketching diagrams or drawing grids to understand the underlying conceptual structure and avoid mistakes. Building on this premise, our research investigates: (a) whether grounding multi-hop textual-spatial stories into geometry-aware modalities, such as layouts or grids, improves reasoning compared to natural language-based inference; and (b) whether a model can decide when to rely on natural language reasoning and when to switch to a structured modality. We address these questions by introducing a switching metric based on trustworthiness and complexity signals, which estimates when grounding a spatial story into structure is likely to improve performance. This takes a first step toward principled modality selection in Large Language Model (LLM) reasoning. Across our settings, switching from natural language-based reasoning to a grid-based representation improves LLM performance by up to 42%, highlighting the importance of modality choice in shaping reasoning outcomes.