用多智能体LLM实现用户与设计师的实时空间设计协作。
Intelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents
- 多智能体系统通过自然语言和图像生成3D设计方案
- 用户交互使设计迭代更高效，77%满意度超传统软件
- 无需训练新模型，靠检索增强降低数据依赖
在室内设计中，客户缺乏专业知识，设计师难以解释复杂空间关系，常导致沟通失误、延期和损失。现有生成布局工具虽能自动化3D可视化，但规则系统约束僵硬，数据模型依赖大量训练数据。本研究提出基于大语言模型（LLM）的多模态多智能体框架，通过参考、空间、交互、评分四个专用智能体，协同将自然语言描述与图像转化为3D设计。该系统支持实时用户互动，实现空间方案的迭代优化；采用检索增强生成（RAG）技术，无需任务特定训练即可减少数据依赖。评估显示，独立LLM评价器对参与式设计在意图契合度、美学一致性、功能性与流线合理性上评分更高；问卷调查显示77%用户满意，明显偏好此系统。结果表明，该框架提升了以用户为中心的沟通效率，推动更具包容性、高效且稳健的设计流程。
原文摘要 · Abstract (English)
In architectural interior design, miscommunication frequently arises as clients lack design knowledge, while designers struggle to explain complex spatial relationships, leading to delayed timelines and financial losses. Recent advancements in generative layout tools narrow the gap by automating 3D visualizations. However, prevailing methodologies exhibit limitations: rule-based systems implement hard-coded spatial constraints that restrict participatory engagement, while data-driven models rely on extensive training datasets. Recent large language models (LLMs) bridge this gap by enabling intuitive reasoning about spatial relationships through natural language. This research presents an LLM-based, multimodal, multi-agent framework that dynamically converts natural language descriptions and imagery into 3D designs. Specialized agents (Reference, Spatial, Interactive, Grader), operating via prompt guidelines, collaboratively address core challenges: the agent system enables real-time user interaction for iterative spatial refinement, while Retrieval-Augmented Generation (RAG) reduces data dependency without requiring task-specific model training. This framework accurately interprets spatial intent and generates optimized 3D indoor design, improving productivity, and encouraging nondesigner participation. Evaluations across diverse floor plans and user questionnaires demonstrate effectiveness. An independent LLM evaluator consistently rated participatory layouts higher in user intent alignment, aesthetic coherence, functionality, and circulation. Questionnaire results indicated 77% satisfaction and a clear preference over traditional design software. These findings suggest the framework enhances user-centric communication and fosters more inclusive, effective, and resilient design processes. Project page: https://rsigktyper.github.io/AICodesign/
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