Title: SpinBench: Perspective and Rotation as a Lens on Spatial Reasoning in VLMs
Primary area: datasets and benchmarks
Keywords: Spatial reasoning, VLMs, benchmark

Abstract: We present SpinBench, a cognitively grounded diagnostic benchmark for evaluating spatial reasoning in vision language models (VLMs). 
SpinBench is designed around the core challenge of spatial reasoning: perspective taking, the ability to reason about how scenes and object relations change under viewpoint transformation. 
Since perspective taking requires multiple cognitive capabilities, such as recognizing objects across views, relative positions grounding, and mentally simulating transformations, SpinBench introduces a set of fine-grained diagnostic categories. 
Our categories target translation, rotation, object relative pose, and viewpoint change, and are progressively structured so that single-object simpler tasks scaffold toward the most demanding multi-object perspective-taking setting.  We evaluate 37 state-of-the-art VLMs, both proprietary and open source. 
Results reveal systematic weaknesses: strong egocentric bias, poor rotational understanding, and inconsistencies under symmetrical and syntactic reformulations.
Scaling analysis shows both smooth improvements and emergent capabilities. 
While human subjects achieve high accuracy (91.2\%), task difficulty as measured by human response time shows strong correlation with VLM accuracy, indicating that SpinBench captures spatial reasoning challenges shared across humans and VLMs.
Together, our findings highlight the need for structured, cognitively inspired diagnostic tools to advance spatial reasoning in multimodal foundation models. 
Our website can be found [here](https://sites.google.com/view/spinbench/).

TL;DR: We propose SpinBench, a cognitively inspired benchmark that decomposes perspective taking into fine-grained tasks, which reveals systematic weaknesses in 37 VLMs and highlights the need for structured diagnostics to advance spatial reasoning.