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

• URL: http://arxiv.org/abs/2503.19990v1
• Date: Tue, 25 Mar 2025 18:21:07 GMT
• Title: LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?
• Authors: Kexian Tang, Junyao Gao, Yanhong Zeng, Haodong Duan, Yanan Sun, Zhening Xing, Wenran Liu, Kaifeng Lyu, Kai Chen,
• Abstract summary: LEGO-Puzzles consists of 1,100 carefully curated visual question-answering (VQA) samples spanning 11 distinct tasks.<n>Even the most powerful MLLMs can answer only about half of the test cases, whereas human participants achieve over 90% accuracy.<n>In addition to VQA tasks, we evaluate MLLMs' abilities to generate LEGO images following assembly illustrations.
• Score: 23.90259639381836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \textbf{LEGO-Puzzles}, a scalable benchmark designed to evaluate both \textbf{spatial understanding} and \textbf{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.

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