Tsinghua study: AI reasons better on spatial tasks with images

Tsinghua researchers found AI models reason more effectively on spatial tasks when inputs are image-formatted rather than text. This suggests multimodal architectures leverage visual-spatial priors unavailable in token sequences. Practitioners building spatial reasoning pipelines should consider image-based input encoding over text serialization.