3 papers
cs.AI2026
From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design
Sha Li, Stefano Petrangeli, Yu Shen +1
We introduce LaySPA, a reinforcement learning framework that equips large language models (LLMs) with explicit and interpretable spatial reasoning for content-aware graphic layout…
cs.AI2026
LLMs as Layout Designers: Enhanced Spatial Reasoning for Content-Aware Layout Generation
Sha Li, Stefano Petrangeli, Yu Shen +2
While Large Language Models (LLMs) have demonstrated impressive reasoning and planning abilities in textual domains and can effectively follow instructions for complex tasks, their…
cs.AI2026
PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement
Huaxiaoyue Wang, Sunav Choudhary, Franck Dernoncourt +2
Graphic design often involves exploring different stylistic directions, which can be time-consuming for non-experts. We address this problem of stylistically improving designs base…