A Multiagent Large Language Model-Based System for Early-Stage Building Layout Planning

Abstract

This paper proposes a multiagent large language model (LLM)–based system for early-stage building layout planning, which enables flexible design requirement inputs and robust spatial reasoning. Existing generative methods often depend on structured inputs, and perform poorly when faced with incomplete requirements or regulatory constraints. Moreover, although LLMs excel at understanding language, they typically lack the spatial reasoning capabilities required for layout generation. To address these limitations, the proposed system includes five core modules: a requirement interpreter that standardizes design requirements and employs multimodal retrieval-augmented generation (RAG) to fetch similar layouts; a planner module composed of specialized LLM agents such as an architect, accessibility expert, and user experience advocate; a layout executor that transforms planning strategies into bubble diagrams; and an evaluation module that combines rule-based checks and LLM-based feedback. All modules are coordinated by a director module using multichain-of-thought reasoning to iteratively refine the design process. The system supports natural language prompts, layout boundaries, and optional reference layouts, making it suitable for both incomplete and detailed design scenarios. After a bubble diagram is finalized, a diffusion-based generative model produces a complete architectural layout. Experimental results using the Tell2Design data set demonstrated significant improvements in both geometric quality and semantic alignment over a baseline LLM-only system. Case studies of office and multiapartment buildings further validated the system’s adaptability and effectiveness in complex design contexts.