我们是否已经统一了图像生成与理解？GPT-4o图像生成能力的实证研究
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability
AI总结：
本文通过全局指令遵循、细粒度编辑精度和生成后推理三个维度实证评估GPT-4o，揭示其在动态知识整合与条件推理方面的显著局限，呼吁构建更稳健的基准与训练策略。
摘要：
OpenAI的多模态GPT-4o在图像生成与编辑方面展现出卓越能力，但其实现世界知识驱动的语义合成——即无缝整合领域知识、上下文推理与指令遵循——的能力仍未得到证实。本研究从三个关键维度系统评估这些能力：（1）全局指令遵循，（2）细粒度编辑精度，（3）生成后推理。尽管现有基准凸显了GPT-4o在图像生成与编辑方面的强大能力，我们的评估揭示了GPT-4o持续存在的局限性：该模型经常默认对指令进行字面解释，不一致地应用知识约束，并在条件推理任务中表现挣扎。这些发现挑战了关于GPT-4o统一理解与生成能力的普遍假设，暴露了其在动态知识整合方面的显著差距。本研究呼吁开发超越表层对齐的更稳健的基准与训练策略，强调上下文感知和以推理为基础的多模态生成。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
