LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?
Authors: name: Kexian Tang, name: Junyao Gao, name: Yanhong Zeng, name: Haodong Duan, name: Yanan Sun, name: Zhening Xing, name: Wenran Liu, name: Kaifeng Lyu, name: Kai Chen

Multi-step spatial reasoning entails understanding and reasoning about
spatial relationships across multiple sequential steps, which is crucial for
tackling complex real-world applications, such as robotic manipulation,
autonomous navigation, and automated assembly. To assess how well current
Multimodal Large Language Models (MLLMs) have acquired this fundamental
capability, we introduce LEGO-Puzzles, a scalable benchmark designed
to evaluate both spatial understanding and sequential
reasoning in MLLMs through LEGO-based tasks. LEGO-Puzzles consists of 1,100
carefully curated visual question-answering (VQA) samples spanning 11 distinct
tasks, ranging from basic spatial understanding to complex multi-step
reasoning. Based on LEGO-Puzzles, we conduct a comprehensive evaluation of
state-of-the-art MLLMs and uncover significant limitations in their spatial
reasoning capabilities: even the most powerful MLLMs can answer only about half
of the test cases, whereas human participants achieve over 90\% accuracy. In
addition to VQA tasks, we evaluate MLLMs' abilities to generate LEGO images
following assembly illustrations. Our experiments show that only
Gemini-2.0-Flash and GPT-4o exhibit a limited ability to follow these
instructions, while other MLLMs either replicate the input image or generate
completely irrelevant outputs. Overall, LEGO-Puzzles exposes critical
deficiencies in existing MLLMs' spatial understanding and sequential reasoning
capabilities, and underscores the need for further advancements in multimodal
spatial reasoning.