Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual
  Simulations
Authors: name: Linjie Li, name: Mahtab Bigverdi, name: Jiawei Gu, name: Zixian Ma, name: Yinuo Yang, name: Ziang Li, name: Yejin Choi, name: Ranjay Krishna

Spatial cognition is essential for human intelligence, enabling
problem-solving through visual simulations rather than solely relying on verbal
reasoning. However, existing AI benchmarks primarily assess verbal reasoning,
neglecting the complexities of non-verbal, multi-step visual simulation. We
introduce STARE(Spatial Transformations and Reasoning Evaluation), a benchmark
designed to rigorously evaluate multimodal large language models on tasks
better solved through multi-step visual simulation. STARE features 4K tasks
spanning foundational geometric transformations (2D and 3D), integrated spatial
reasoning (cube net folding and tangram puzzles), and real-world spatial
reasoning (perspective and temporal reasoning), reflecting practical cognitive
challenges like object assembly, mechanical diagram interpretation, and
everyday spatial navigation. Our evaluations show that models excel at
reasoning over simpler 2D transformations, but perform close to random chance
on more complex tasks like 3D cube net folding and tangram puzzles that require
multi-step visual simulations. Humans achieve near-perfect accuracy but take
considerable time (up to 28.9s) on complex tasks, significantly speeding up
(down by 7.5 seconds on average) with intermediate visual simulations. In
contrast, models exhibit inconsistent performance gains from visual
simulations, improving on most tasks but declining in specific cases like
tangram puzzles (GPT-4o, o1) and cube net folding (Claude-3.5, Gemini-2.0
Flash), indicating that models may not know how to effectively leverage
intermediate visual information.