Attention in Space: Functional Roles of VLM Heads for Spatial Reasoning

Despite remarkable advances in large Vision-Language Models (VLMs), spatial reasoning remains a persistent challenge. In this work, we investigate how VLMs represent and perform spatial reasoning through a mechanistic interpretability lens. We introduce CogVSR, a structured diagnostic framework that decomposes complex spatial reasoning tasks into compositional, step-by-step subquestions designed to simulate human-like reasoning, each aligned with specific cognitive functions such as spatial perception. This enables fine-grained analysis of how spatial reasoning is organized within model internals. Building on CogVSR, we develop a probing framework to identify and characterize attention heads specialized for these functions. Our analysis across diverse VLM families reveals that these functional heads are universally sparse, vary in number and distribution across functions. Interestingly, spatially specialized heads are fewer than those for other cognitive functions, highlighting their scarcity. We propose methods to better disentangle spatial-related functions to improve spatial understanding, which is consistent with the activation of latent spatial heads. Intervention experiments further demonstrate their critical role in spatial reasoning: removing functional heads leads to performance degradation, while emphasizing them enhances accuracy. This study provides new interpretability driven insights into how VLMs attend to space and paves the way for enhancing complex spatial reasoning in multimodal models.