Humans possess the remarkable ability to flexibly acquire and apply abstract concepts when interpreting the concrete world around us. Consider the concept "maze": our mental model can interpret mazes constructed with conventional materials (e.g., drawn lines) or unconventional ones (e.g., icing), and reason about mazes across a wide range of configurations and environments (e.g., in a cardboard box or on a knitted square). Our goal is to build systems that can make such flexible and broad generalizations as humans do. This necessitates a reconsideration of a fundamental question: what makes a maze look like a maze? A maze is not defined by concrete visual features such as the specific material of walls or particular perpendicular intersections, but by lifted rules over symbols—a plausible model for a maze includes its layout, the walls, and the designated entry and exit.

Current VLMs often struggle to reason about visual abstractions at a human level, frequently defaulting to literal interpretations of images, such as a collection of object categories. Here, we propose Deep Schema Grounding (DSG), a framework for models to interpret visual abstractions. At the core of DSG are schemas—a dependency graph description of abstract concepts. Schemas characterize common patterns that humans use to interpret the visual world, generalize efficiently from limited data, and reason across multiple levels of abstraction for flexible adaptation. A schema for "helping" allows us to understand relations between characters in a finger puppet scene, while a schema for "tic-tac-toe" allows us to play the game even when the grid is composed of hula hoops instead of drawn lines. A schema for "maze" makes a maze look like a maze.