Paper deep dive
Intelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents
Ren Jian Lim, Rushi Dai
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 92%
Last extracted: 7/12/2026, 1:59:46 AM
Summary
The paper introduces an LLM-based multimodal multi-agent framework for interior spatial design that converts natural language and imagery into 3D designs. It employs specialized agents (Reference, Spatial, Interactive, Grader) and Retrieval-Augmented Generation to enable real-time user interaction and iterative refinement, reducing reliance on extensive training data. Evaluations show high user satisfaction (77%) and superior performance over traditional design software in intent alignment, aesthetics, and functionality.
Entities (10)
Relation Signals (9)
Rushi Dai → authored → Intelligent Co-Design Framework
confidence 95% · Authors:Ren Jian Lim, Rushi Dai
Ren Jian Lim → authored → Intelligent Co-Design Framework
confidence 95% · Authors:Ren Jian Lim, Rushi Dai
Intelligent Co-Design Framework → uses → Large Language Model
confidence 95% · This research presents an LLM-based, multimodal, multi-agent framework that dynamically converts natural language descriptions and imagery into 3D designs.
Intelligent Co-Design Framework → employs → Interactive Agent
confidence 90% · Specialized agents (Reference, Spatial, Interactive, Grader), operating via prompt guidelines, collaboratively address core challenges
Intelligent Co-Design Framework → employs → Grader Agent
confidence 90% · Specialized agents (Reference, Spatial, Interactive, Grader), operating via prompt guidelines, collaboratively address core challenges
Intelligent Co-Design Framework → employs → Reference Agent
confidence 90% · Specialized agents (Reference, Spatial, Interactive, Grader), operating via prompt guidelines, collaboratively address core challenges
Intelligent Co-Design Framework → employs → Spatial Agent
confidence 90% · Specialized agents (Reference, Spatial, Interactive, Grader), operating via prompt guidelines, collaboratively address core challenges
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Abstract
Abstract: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: this https URL
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- Source: https://arxiv.org/abs/2603.15341v1
- Canonical: https://arxiv.org/abs/2603.15341v1
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Skip to main content arXiv is now an independent nonprofit! Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2603.15341v1 (cs) [Submitted on 16 Mar 2026] Title:Intelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents Authors:Ren Jian Lim, Rushi Dai View a PDF of the paper titled Intelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents, by Ren Jian Lim and 1 other authors View PDF Abstract: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: this https URL Comments: 25 pages, 20 figures; accepted for publication in the Proceedings of ACADIA 2025 Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA) Cite as: arXiv:2603.15341 [cs.AI] (or arXiv:2603.15341v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.15341 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ren Jian Lim [view email] [v1] Mon, 16 Mar 2026 14:28:51 UTC (1,587 KB) Full-text links: Access Paper: View a PDF of the paper titled Intelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents, by Ren Jian Lim and 1 other authorsView PDF view license Current browse context: cs.AI < prev | next > new | recent | 2026-03 Change to browse by: cs cs.HC cs.MA References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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