LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?

arXiv:2503.19990v4 Announce Type: replace Abstract: Many real-world applications of spatial intelligence, such as robotic control, autonomous driving, and automated assembly, require spatial reasoning across multiple sequential steps. However, the extent to which current Multimodal Large Language Models (MLLMs) possess this capability remains largely unexplored. Inspired by LEGO construction, a recreational activity that critically relies on multi-step spatial reasoning, we introduce LEGO-Puzzles: a benchmark designed to systematically evaluate the spatial reasoning capabilities of MLLMs from basic spatial understanding to multi-step planning. LEGO-Puzzles contains two task sets. The Elementary set covers 11 visual question-answering (VQA) tasks with 1,100 carefully curated samples to test elementary spatial reasoning skills that are cruical for LEGO assembly. The Planning set directly requires the model to generate a step-by-step plan for assembling a target LEGO structure, where the tasks are organized into subsets with different planning horizons ranging up to 8. Our evaluation of 29 state-of-the-art MLLMs shows that even the strongest models struggle with elementary reasoning tasks in LEGO construction, falling at least 20% behind human performance. The planning accuracy also quickly drops to 0% as the number of planning steps increases, whereas our human participants solve all the tasks perfectly. Switching the output format from multiple choice to image generation degrades model performance even further, leading to zero accuracy even for planning 3 steps. Overall, LEGO-Puzzles reveals critical limitations in current MLLMs' spatial reasoning capabilities and highlights the need for substantial advances.

Source: arXiv cs.AI | 2026-08-11