LLMs as Layout Designers: Enhanced Spatial Reasoning for Content-Aware Layout Generation
This addresses the challenge of spatial understanding in LLMs for graphic design applications, offering a domain-specific incremental improvement over existing methods.
The paper tackled the problem of limited spatial reasoning in LLMs for content-aware graphic layout design by introducing LaySPA, a reinforcement learning framework that enhances LLM agents with explicit spatial reasoning, resulting in substantial improvements in generating structurally valid and visually appealing layouts, outperforming larger general-purpose LLMs and matching state-of-the-art specialized models.
While Large Language Models (LLMs) have demonstrated impressive reasoning and planning abilities in textual domains and can effectively follow instructions for complex tasks, their ability to understand and manipulate spatial relationships remains limited. Such capabilities are crucial for content-aware graphic layout design, where the goal is to arrange heterogeneous elements onto a canvas so that final design remains visually balanced and structurally feasible. This problem requires precise coordination of placement, alignment, and structural organization of multiple elements within a constrained visual space. To address this limitation, we introduce LaySPA, a reinforcement learning-based framework that augments LLM-based agents with explicit spatial reasoning capabilities for layout design. LaySPA employs hybrid reward signals that jointly capture geometric constraints, structural fidelity, and visual quality, enabling agents to navigate the canvas, model inter-element relationships, and optimize spatial arrangements. Through group-relative policy optimization, the agent generates content-aware layouts that reflect salient regions, respect spatial constraints, and produces an interpretable reasoning trace explaining placement decisions and a structured layout specification. Experimental results show that LaySPA substantially improves the generation of structurally valid and visually appealing layouts, outperforming larger general-purpose LLMs and achieving performance comparable to state-of-the-art specialized layout models.