Reasoning Path and Latent State Analysis for Multi-view Visual Spatial Reasoning: A Cognitive Science Perspective

Spatial reasoning is a core aspect of human intelligence forperception and inference in 3D environments. However, current vision-language models (VLMs) struggle to maintain geometric coherence andcross-view consistency for spatial reasoning in multi-view settings. Inthis paper, we aim to understand the successes and failure of VLMs inmulti-view spatial reasoning, via structured analysis of how VLMs en-code perceptual evidence, integrate relational information, construct in-termediate spatial representations and perform perspective transforma-tion. Guided by cognitive science theories of human spatial reasoning,our analysis examines VLM’s consistency and representational persis-tence across reasoning phases that align with human’s spatial cognition,in explicit and implicit manners. Using a self-constructed spatial bench-mark, namely ReMindView-Bench, which consists of >50,000 VQA sam-ples with complementary views and controlled viewpoint configurations,spatial patterns and query types, our explicit analysis using an LLM-as-graph-parser workflow and self-consistency prompting shows that VLMsremain reliable for in-frame perceptual encoding but degrade sharplywhen integrating information across views. Our implicit analysis of linearprobing and entropy dynamics further reveals progressive loss of task-relevant spatial information and increasing uncertainty over reasoningphases. These findings provide important insights of how multi-view spa-tial mental models are formed, destabilized, and degraded across reason-ing phases in VLMs. The ReMindView-Bench benchmark is available athttps://huggingface.co/datasets/Xue0823/ReMindView-Bench, andthe source codes of benchmark construction and VLM reasoning analy-sis are available at https://github.com/pittisl/ReMindView-Bench.