A floor plan is a spatial object — rooms have positions, dimensions, adjacencies, door placements. A language model processes sequences of tokens. These seem incompatible. But a room is also a discrete entity with attributes: type (bedroom), area (12 m²), position (northwest corner), connections (adjacent to hallway, shares wall with bathroom). Write these as tokens and the floor plan becomes a sentence.
HouseMind (arXiv:2603.11640) introduces discrete room-instance tokens as a unified vocabulary that bridges layouts and symbolic reasoning. Each room instance becomes a token in the model's vocabulary. The floor plan becomes a sequence the model can generate through next-token prediction, understand through instruction following, and edit through targeted token modification.
The editing capability is what separates this from image-based approaches. Tell a diffusion model to “move the kitchen next to the dining room” and it regenerates the entire image, possibly breaking geometric constraints. Tell a token-based model to do the same and it reorders tokens while preserving the spatial grammar — wall alignments, door positions, room proportions. The edit is local because the representation is local.
The structural insight: the right tokenization scheme is a compression of the domain. A floor plan image has millions of pixels, mostly redundant. The token representation has dozens of room instances, each carrying exactly the information an architect cares about. The compression isn't lossy — it's a projection from pixel space to design space. And design space is where the decisions live.