title: WorldGenBench: A World-Knowledge-Integrated Benchmark for
    Reasoning-Driven Text-to-Image Generation
summary: 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/}
paper_authors._id: 681965b8a52fff9bf3ff6441
paper_authors.name: Daoan Zhang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-06T01:28:25.306Z
paper_authors._id: 64ee52837ce493905c82bed5
paper_authors.avatarUrl: /avatars/1f9f0db4eee887f9ed03dffc8c36ea23.svg
paper_authors.fullname: DaoanZhang
paper_authors.type: user
paper_authors.user: DwanZhang
paper_authors._id: 681965b8a52fff9bf3ff6442
paper_authors.name: Che Jiang
paper_authors._id: 681965b8a52fff9bf3ff6443
paper_authors.name: Ruoshi Xu
paper_authors._id: 681965b8a52fff9bf3ff6444
paper_authors.name: Biaoxiang Chen
paper_authors._id: 681965b8a52fff9bf3ff6445
paper_authors.name: Zijian Jin
paper_authors._id: 681965b8a52fff9bf3ff6446
paper_authors.name: Yutian Lu
paper_authors._id: 681965b8a52fff9bf3ff6447
paper_authors.name: Jianguo Zhang
paper_authors._id: 681965b8a52fff9bf3ff6448
paper_authors.name: Liang Yong
paper_authors._id: 681965b8a52fff9bf3ff6449
paper_authors.name: Jiebo Luo
paper_authors._id: 681965b8a52fff9bf3ff644a
paper_authors.name: Shengda Luo
paper_title: WorldGenBench: A World-Knowledge-Integrated Benchmark for
    Reasoning-Driven Text-to-Image Generation
paper_summary: 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/}
paper_ai_summary: WorldGenBench evaluates text-to-image models using the Knowledge Checklist Score to identify gaps in world knowledge grounding and implicit reasoning.
paper_ai_keywords: diffusion models auto-regressive models GPT-4o Knowledge Checklist Score