# LLMs as Layout Designers: Enhanced Spatial Reasoning for Content-Aware Layout Generation

arXiv: https://arxiv.org/abs/2509.16891 · HTML: https://mlsanity.com/paper/2509.16891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 18 of its 23 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 18 of 18)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout](https://mlsanity.com/paper/2303.15937/) (2023)
- [Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs](https://mlsanity.com/paper/2205.00303/) (2022)
- [LayoutDM: Transformer-based Diffusion Model for Layout Generation](https://mlsanity.com/paper/2305.02567/) (2023)
- [LayoutPrompter: Awaken the Design Ability of Large Language Models](https://mlsanity.com/paper/2311.06495/) (2023)
- [Unifying Layout Generation with a Decoupled Diffusion Model](https://mlsanity.com/paper/2303.05049/) (2023)
- [LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models](https://mlsanity.com/paper/2303.11589/) (2023)
- [Relation-Aware Diffusion Model for Controllable Poster Layout Generation](https://mlsanity.com/paper/2306.09086/) (2023)
- [Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation](https://mlsanity.com/paper/2311.13602/) (2023)
- [Geometry Aligned Variational Transformer for Image-conditioned Layout Generation](https://mlsanity.com/paper/2209.00852/) (2022)
- [LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models](https://mlsanity.com/paper/2309.09506/) (2023)
- [PosterO: Structuring Layout Trees to Enable Language Models in Generalized Content-Aware Layout Generation](https://mlsanity.com/paper/2505.07843/) (2025)
- [CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design](https://mlsanity.com/paper/2506.21934/) (2025)
- [LayoutRectifier: An Optimization-based Post-processing for Graphic Design Layout Generation](https://mlsanity.com/paper/2508.11177/) (2025)
- [ReLayout: Integrating Relation Reasoning for Content-aware Layout Generation with Multi-modal Large Language Models](https://mlsanity.com/paper/2507.05568/) (2025)

## Built on by (most cited first, 1 of 1)

- [Think Before You Move: Latent Motion Reasoning for Text-to-Motion Generation](https://mlsanity.com/paper/2512.24100/) (2025)

## Abstract (arXiv)

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.