How Far Can Coding Agents Go in Visual Generation?

Modern image generation largely relies on specialized generative models that leave the process of image construction implicit in learned parameters. We explore an alternative: general-purpose coding agents that construct images through executable programs.

现代图像生成主要依赖于专门的生成模型，这些模型将图像构建过程隐含在学习到的参数中。我们探索了一种替代方案：通过可执行程序构建图像的通用编码智能体。

Our study of zero-shot drawing on AgentDrawBench, a 200-prompt benchmark, reveals a distinctive capability profile. Agents excel at preserving structure and satisfying precise visual constraints, achieving competitive results in domains where compositional accuracy matters more than photorealistic appearance, while remaining limited in perceptual richness.

我们对包含 200 个提示词的基准测试 AgentDrawBench 进行了零样本绘图研究，揭示了其独特的能力特征。智能体在保持结构和满足精确视觉约束方面表现出色，在构图准确性比照片级真实感更重要的领域取得了具有竞争力的结果，但在感知丰富度方面仍存在局限。

Their visual expressiveness varies with the drawing interface, and a qualitative study on reconstruction shows how procedural skills extracted from human demonstrations support different artistic media. We then examine code as an external visual language of thought: a structured, executable representation that agents can construct and inspect.

它们的视觉表现力随绘图界面的不同而变化，一项关于重构的定性研究展示了从人类演示中提取的程序化技能如何支持不同的艺术媒介。随后，我们将代码视为一种外部的视觉思维语言：一种智能体可以构建和检查的结构化、可执行的表示形式。

We test it in visual question answering task and find reconstructed visual code retains substantial information for question answering and, in some domains, supports higher accuracy than the original image. These abilities raise a further question: do agents draw on a shared internal representation of scene content beyond a particular drawing program?

我们在视觉问答任务中对其进行了测试，发现重构的视觉代码保留了大量用于问答的信息，并且在某些领域比原始图像支持更高的准确性。这些能力提出了一个进一步的问题：智能体是否在特定绘图程序之外，利用了场景内容的共享内部表示？

We explore an internal visual language-of-thought hypothesis, in which a shared, compositionally structured representation supports visual operations across drawing languages. Cross-harness drawings and images assembled from independent single-pixel queries exhibit related layouts and tones.

我们探索了一种内部视觉思维语言假设，即一种共享的、结构化的表示形式支持跨绘图语言的视觉操作。跨工具的绘图以及由独立单像素查询组装而成的图像，展现出了相关的布局和色调。

Agents also maintain self-generated room arrangements across viewpoints after visible drawings are removed. These results demonstrate that the coding agent would maintain a consistent and coherent world inside its imagine even before the harness gets involved and the generation starts.

即使在移除可见绘图后，智能体在不同视角下仍能保持其自生成的房间布局。这些结果表明，编码智能体在工具介入和生成开始之前，就能在其想象中维持一个一致且连贯的世界。