Thinking with Blueprints: Assisting Vision-Language Models in Spatial Reasoning via Structured Object Representation

Understand

Spatial reasoning -- the ability to perceive and reason about relationships in space -- advances vision-language models (VLMs) from visual perception toward spatial semantic understanding.

• Existing approaches either revisit local image patches, improving fine-grained perception but weakening global spatial awareness, or mark isolated coordinates, which capture object locations but overlook their overall organization.
• In this work, we integrate the cognitive concept of an object-centric blueprint into VLMs to enhance spatial reasoning.
• Given an image and a question, the model first constructs a JSON-style blueprint that records the positions, sizes, and attributes of relevant objects, and then reasons over this structured representation to produce the final answer.