AI写代码，现在要看图了
你让AI写个网页，它写出来了，但布局不对、颜色不对——因为它只看文字，没看你给的截图。这篇综述把这类问题统称为「多模态代码智能」：AI不仅要理解文字需求，还要看懂截图、图表、视频、交互状态，才能生成对的代码。研究者把任务分成四类：做界面、做科学图表、做矢量图、以及更前沿的智能体任务。核心难点是「怎么验证对错」——光语法对不够，布局、数据语义、交互行为都得对。他们提出四个验证方向：多信号联合验证、多状态验证、跨任务迁移测试、以及可验证的智能体轨迹。这不是你明天就能用的技术，但它指出了AI编程的下一个真实战场：从「写对语法」到「看懂画面」。
📄 原文摘要(英文)
While Large Language Models (LLMs) have substantially advanced text-to-code synthesis, many real programming tasks specify intent through visual artifacts such as screenshots, charts, vector drawings, videos, and interactive states. These tasks require models to connect visual perception to executable programs, because correctness depends not only on syntax but also on layout, data semantics, interaction behavior, and domain-specific constraints that apply after execution. This survey examines Multimodal Code Intelligence, covering systems that generate, edit, refine, or reason with code under visually grounded inputs and outputs. We first formulate the field by the role that code plays in each task, distinguishing code as a rendered artifact, an editable symbolic structure, a scientific representation, an intermediate reasoning trace, or an executable policy or tool interface. We then organize benchmarks and methods into four domains: Graphical User Interface, Scientific Visualization, Structured Graphics, and Frontier Tasks and Frameworks. This taxonomy connects mature artifact-generation problems to emerging agentic and unified settings and allows us to compare how different tasks treat evidence of correctness. Looking ahead, we argue that future research may benefit from four verification-centered directions. Multi-signal validation can combine complementary evidence of correctness, multi-state verification can test behavior across execution trajectories, cross-task transfer testing can probe reusable visual-code skills, and verifiable agent traces can reveal whether agent actions are grounded in visual evidence. Together, these directions may move this field from single-output imitation toward evidence-grounded executable systems. An ongoing project and resources are available on https://github.com/xjywhu/Awesome-Multimodal-LLM-for-Code{GitHub}.