SpatialViz-Bench: Automatically Generated Spatial Visualization Reasoning Tasks for MLLMs

• URL: http://arxiv.org/abs/2507.07610v2
• Date: Mon, 14 Jul 2025 07:38:44 GMT
• Title: SpatialViz-Bench: Automatically Generated Spatial Visualization Reasoning Tasks for MLLMs
• Authors: Siting Wang, Luoyang Sun, Cheng Deng, Kun Shao, Minnan Pei, Zheng Tian, Haifeng Zhang, Jun Wang,
• Abstract summary: SpatialViz-Bench is a comprehensive benchmark for spatial visualization with 12 tasks across 4 sub-abilities, comprising 1,180 automatically generated problems.<n>Our evaluation of 33 state-of-the-art MLLMs reveals wide performance variations and uncovers counter-intuitive findings.
• Score: 43.82781630267406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans can directly imagine and manipulate visual images in their minds, a capability known as spatial visualization. While multi-modal Large Language Models (MLLMs) support imagination-based reasoning, spatial visualization remains insufficiently evaluated, typically embedded within broader mathematical and logical assessments. Existing evaluations often rely on IQ tests or math competitions that may overlap with training data, compromising assessment reliability. To this end, we introduce SpatialViz-Bench, a comprehensive multi-modal benchmark for spatial visualization with 12 tasks across 4 sub-abilities, comprising 1,180 automatically generated problems. Our evaluation of 33 state-of-the-art MLLMs not only reveals wide performance variations and demonstrates the benchmark's strong discriminative power, but also uncovers counter-intuitive findings: models exhibit unexpected behaviors by showing difficulty perception that misaligns with human intuition, displaying dramatic 2D-to-3D performance cliffs, and defaulting to formula derivation despite spatial tasks requiring visualization alone. SpatialVizBench empirically demonstrates that state-of-the-art MLLMs continue to exhibit deficiencies in spatial visualization tasks, thereby addressing a significant lacuna in the field. The benchmark is publicly available.

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