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.

翻译：我们提出了一种新颖的自动化室内设计框架，该框架将大语言模型（LLMs）与基于网格的整数规划相结合，以协同优化房间布局与家具摆放。给定文本提示，由LLM驱动的智能体工作流会提取与房间配置和家具布置相关的结构化设计约束。这些约束被编码到一个受“模度”启发的统一基于网格的表示中。我们的建模考虑了关键的设计要求，包括走廊连通性、房间可达性、空间排他性以及用户指定的偏好。为了提高计算效率，我们采用了从粗到精的优化策略：首先在低分辨率网格上求解简化问题，并以此指导全分辨率下的最终求解。在多种场景下的实验结果表明，我们的联合优化方法在解决方案质量上显著优于现有的两阶段设计流程，并通过从粗到精的策略实现了显著的计算效率提升。