WorldGenBench: A World-Knowledge-Integrated Benchmark for
  Reasoning-Driven Text-to-Image Generation
Authors: name: Daoan Zhang, name: Che Jiang, name: Ruoshi Xu, name: Biaoxiang Chen, name: Zijian Jin, name: Yutian Lu, name: Jianguo Zhang, name: Liang Yong, name: Jiebo Luo, name: Shengda Luo

Recent advances in text-to-image (T2I) generation have achieved impressive
results, yet existing models still struggle with prompts that require rich
world knowledge and implicit reasoning: both of which are critical for
producing semantically accurate, coherent, and contextually appropriate images
in real-world scenarios. To address this gap, we introduce
WorldGenBench, a benchmark designed to systematically evaluate T2I
models' world knowledge grounding and implicit inferential capabilities,
covering both the humanities and nature domains. We propose the
Knowledge Checklist Score, a structured metric that measures how well
generated images satisfy key semantic expectations. Experiments across 21
state-of-the-art models reveal that while diffusion models lead among
open-source methods, proprietary auto-regressive models like GPT-4o exhibit
significantly stronger reasoning and knowledge integration. Our findings
highlight the need for deeper understanding and inference capabilities in
next-generation T2I systems. Project Page:
https://dwanzhang-ai.github.io/WorldGenBench/{https://dwanzhang-ai.github.io/WorldGenBench/}