Have we unified image generation and understanding yet? An empirical
  study of GPT-4o's image generation ability
Authors: name: Ning Li, name: Jingran Zhang, name: Justin Cui

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.