Knowledge Visualization: A Benchmark and Method for Knowledge-Intensive Text-to-Image Generation

ArXi:2604.22302v1 Announce Type: new Recent text-to-image (T2I) models have nstrated impressive capabilities in photorealistic synthesis and instruction following. However, their reliability in knowledge-intensive settings remains largely unexplored. Unlike natural image generation, knowledge visualization requires not only semantic alignment but also strict adherence to domain knowledge, structural constraints, and symbolic conventions, exposing a critical gap between visual plausibility and scientific correctness. To systematically study this problem, we