title: Have we unified image generation and understanding yet? An empirical
    study of GPT-4o's image generation ability
summary: OpenAI's multimodal GPT-4o has demonstrated remarkable capabilities in image
  generation and editing, yet its ability to achieve world knowledge-informed
  semantic synthesis--seamlessly integrating domain knowledge, contextual
  reasoning, and instruction adherence--remains unproven. In this study, we
  systematically evaluate these capabilities across three critical dimensions:
  (1) Global Instruction Adherence, (2) Fine-Grained Editing Precision, and (3)
  Post-Generation Reasoning. While existing benchmarks highlight GPT-4o's strong
  capabilities in image generation and editing, our evaluation reveals GPT-4o's
  persistent limitations: the model frequently defaults to literal
  interpretations of instructions, inconsistently applies knowledge constraints,
  and struggles with conditional reasoning tasks. These findings challenge
  prevailing assumptions about GPT-4o's unified understanding and generation
  capabilities, exposing significant gaps in its dynamic knowledge integration.
  Our study calls for the development of more robust benchmarks and training
  strategies that go beyond surface-level alignment, emphasizing context-aware
  and reasoning-grounded multimodal generation.
paper_authors._id: 67fdc0c50c63732d9e0b139a
paper_authors.name: Ning Li
paper_authors._id: 67fdc0c50c63732d9e0b139b
paper_authors.name: Jingran Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-15T08:25:03.508Z
paper_authors._id: 67fdc24ad2c9d1369d390f01
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/51JiReMgHiFHZiJ9OVdaf.png
paper_authors.fullname: Jingran Zhang
paper_authors.type: user
paper_authors.user: zhangjingran
paper_authors._id: 67fdc0c50c63732d9e0b139c
paper_authors.name: Justin Cui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-15T08:24:57.129Z
paper_authors._id: 65862671e878be571bf9fc52
paper_authors.avatarUrl: /avatars/b2a1b939f3112b476e7641e0c5fd2dc7.svg
paper_authors.fullname: cuijiaxing
paper_authors.type: user
paper_authors.user: cuijiaxing
paper_title: Have we unified image generation and understanding yet? An empirical
    study of GPT-4o's image generation ability
paper_summary: OpenAI's multimodal GPT-4o has demonstrated remarkable capabilities in image
  generation and editing, yet its ability to achieve world knowledge-informed
  semantic synthesis--seamlessly integrating domain knowledge, contextual
  reasoning, and instruction adherence--remains unproven. In this study, we
  systematically evaluate these capabilities across three critical dimensions:
  (1) Global Instruction Adherence, (2) Fine-Grained Editing Precision, and (3)
  Post-Generation Reasoning. While existing benchmarks highlight GPT-4o's strong
  capabilities in image generation and editing, our evaluation reveals GPT-4o's
  persistent limitations: the model frequently defaults to literal
  interpretations of instructions, inconsistently applies knowledge constraints,
  and struggles with conditional reasoning tasks. These findings challenge
  prevailing assumptions about GPT-4o's unified understanding and generation
  capabilities, exposing significant gaps in its dynamic knowledge integration.
  Our study calls for the development of more robust benchmarks and training
  strategies that go beyond surface-level alignment, emphasizing context-aware
  and reasoning-grounded multimodal generation.
paper_ai_summary: Evaluation of GPT-4o shows limitations in its ability to integrate world knowledge, apply contextual reasoning, and adhere to complex instructions in image generation and editing.
paper_ai_keywords: multimodal GPT-4o semantic synthesis global instruction adherence fine-grained editing precision post-generation reasoning knowledge constraints conditional reasoning context-aware generation reasoning-grounded generation