We present a novel framework for automated interior design that combines large language models (LLMs) with grid-based integer programming to jointly optimize room layout and furniture placement. Given a textual prompt, the LLM-driven agent workflow extracts structured design constraints related to room configurations and furniture arrangements. These constraints are encoded into a unified grid-based representation inspired by ``Modulor". Our formulation accounts for key design requirements, including corridor connectivity, room accessibility, spatial exclusivity, and user-specified preferences. To improve computational efficiency, we adopt a coarse-to-fine optimization strategy that begins with a low-resolution grid to solve a simplified problem and guides the solution at the full resolution. Experimental results across diverse scenarios demonstrate that our joint optimization approach significantly outperforms existing two-stage design pipelines in solution quality, and achieves notable computational efficiency through the coarse-to-fine strategy.
@article{arxiv.2511.12474,
title = {Co-Layout: LLM-driven Co-optimization for Interior Layout},
author = {Chucheng Xiang and Ruchao Bao and Biyin Feng and Wenzheng Wu and Zhongyuan Liu and Yirui Guan and Ligang Liu},
journal= {arXiv preprint arXiv:2511.12474},
year = {2026}
}