Context-Dependent Affordance Computation in Vision-Language Models

Abstract

We propose that the standard vision pipeline inverts the actual structure of biological spatial cognition. Spatial awareness is grounded in functional semantics: geometric structure becomes accessible to reasoning only through prior contextual and affordance encoding. We formalize this as a semantic-first spatial architecture and derive testable predictions distinguishing it from geometry-first alternatives, evaluated against the behaviour of contemporary vision-language models.