The First Workshop on Efficient Spatial Reasoning

Spatial reasoning—the ability to understand, represent, and manipulate spatial relationships among objects, agents, and environments—has been profoundly advanced by large foundation models, enabling breakthroughs in 3D reconstruction, scene understanding, and vision–language reasoning. However, current models often rely on massive parameter scales or test-time extensions, introducing significant inefficiencies during training and inference. They also struggle with multi-step reasoning and the nuanced comprehension of complex spatial relations, where unreliable reasoning paths undermine both efficiency and accuracy. To address these challenges, we propose a workshop that unites researchers and practitioners from academia and industry to advance efficient spatial reasoning—approaches that improve generalization and robustness while remaining computationally practical. Topics include symbolic–neural integration, geometric deep learning, scalable reasoning architectures, and evaluation frameworks. Through invited talks and discussions, the workshop will examine efficiency–accuracy trade-offs, cross-modal reasoning, and real-world robustness, fostering collaboration across AI, cognitive science, and applied domains.