Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a crucial aspect of human cognition, remain relatively unexplored. Human possess a remarkable ability to create mental images of unseen objects and actions through a process known as the Mind's Eye, enabling the imagination of the unseen world. Inspired by this cognitive capacity, we propose Visualization-of-Thought (VoT) prompting. VoT aims to elicit spatial reasoning of LLMs by visualizing their reasoning traces, thereby guiding subsequent reasoning steps. We employed VoT for multi-hop spatial reasoning tasks, including natural language navigation, visual navigation, and visual tiling in 2D grid worlds. Experimental results demonstrated that VoT significantly enhances the spatial reasoning abilities of LLMs. Notably, VoT outperformed existing multimodal large language models (MLLMs) in these tasks. While VoT works surprisingly well on LLMs, the ability to generate mental images to facilitate spatial reasoning resembles the mind's eye process, suggesting its potential viability in MLLMs. Please find the dataset and codes at https://microsoft.github.io/visualization-of-thought
Key facts. Cited by 65 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 22 of its 50 references that are in the map. First posted on arXiv 4 Apr 2024.
Builds on
22 of 50 references.
Closest ancestors · 2
Same direction, newest first
- 2022Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning30 Oct
- 2022StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts18 Apr
Other references · 20
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- 2018Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noisecited by 4
- 2022A Symbolic Representation of Human Posture for Interpretable Learning and Reasoningcited by 1
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Citing papers per year: 2024: 6; 2025: 38; 2026: 21.
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