Year: 2026
Type: article
Abstract: Scene design—spanning interior arrangement, virtual environment creation, and simulated asset generation for robotics—requires semantic understanding, physical plausibility, and aesthetic judgement. While traditional approaches rely on expert rules or optimization, Large Language Models (LLMs) promise more flexible, language-grounded reasoning and multimodal grounding. This survey provides a focused definition and task framework for layout agents and proposes a hierarchical categorization based on LLM layout agents' output representation. At the highest level, we distinguish Direct Methods, which predict asset poses (one-stage vs. multi-stage), from Indirect Methods, which emit intermediate representations (constraint-based or programmatic) that are converted to layouts via post-processing. Furthermore, we categorize the evaluation of layout agents into four dimensions: physical plausibility, prompt fidelity, semantic coherence, and aesthetics and realism, and summarize the commonly used evaluation methods. Finally, we review the current challenges faced by layout agents, including the lack of spatial reasoning ability, instability, poor generalization, and low efficiency. By organizing recent progress and identifying gaps, this survey aims to guide future research toward more capable, generalizable LLM layout agents.
Source: Journal of Design Intelligence
DOI: 10.64509/jdi.11.51
Authors: Cheng Wan, Yongsen Mao, Yuan Liu
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7131291663
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