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New LEGO-Puzzles benchmark reveals MLLMs struggle with spatial reasoning

A new benchmark called LEGO-Puzzles has been introduced to evaluate the multi-step spatial reasoning capabilities of Multimodal Large Language Models (MLLMs). The benchmark, inspired by LEGO construction, includes tasks for elementary spatial understanding and step-by-step planning for assembling LEGO structures. Evaluations of 29 state-of-the-art MLLMs revealed significant limitations, with even the best models performing substantially below human accuracy on elementary tasks and failing to plan beyond a few steps. AI

IMPACT Highlights critical limitations in current MLLMs' spatial reasoning, indicating a need for significant advancements in multimodal AI.

RANK_REASON The cluster contains a new academic paper introducing a benchmark for evaluating AI capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New LEGO-Puzzles benchmark reveals MLLMs struggle with spatial reasoning

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kexian Tang, Junyao Gao, Yanhong Zeng, Haodong Duan, Yanan Sun, Zhening Xing, Wenran Liu, Kai Chen, Kaifeng Lyu ·

    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 Multimod…