Limits of Spatial Imagery Reasoning in Frontier LLM Models
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet they struggle with spatial tasks that require mental simulation, such as mental rotation. This paper investigates whether equipping an LLM with an external ``Imagery Module'' -- a tool capable of rendering and rotating 3D models -- can bridge this gap, functioning as a ``cognitive prosthetic.'' We conducted experiments using a dual-module architecture in which a reasoning module (an MLLM) interacts with an imagery module on 3D model rotation tasks. Performance was lower than expected, with accuracy reaching at most 62.5%. Further investigation suggests that even when the burden of maintaining and manipul...
| Authors | Sergio Y. Hayashi, Nina S. T. Hirata |
|---|---|
| Primary category | cs.CV |
| Published | 25 March 2026 |
| Last updated at source | 31 May 2026 |
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