arXiv:2606.15932v1 Announce Type: new Abstract: While LLMs have substantially advanced text-to-code synthesis, many real programming tasks specify intent through visual artifacts such as screenshots, charts, documents, 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, geometry, data semantics, editability, interaction behavior, and domain-specific constraints that apply after execution. This survey examines Multimodal Code Intelligence, covering syst
Large Language Models have demonstrated strong text-to-code capabilities, naturally pushing the frontier towards integrating visual and other multimodal data for more realistic and complex programming tasks.
This development indicates a significant leap in AI's ability to interpret and act on human intent, moving beyond purely linguistic instructions to understanding richer, real-world context for code generation.
The scope of AI-assisted programming expands from symbolic textual input to encompassing visual and interactive specifications, potentially automating UI/UX development and dynamic system creation more effectively.
- · AI developers
- · Software development agencies
- · UI/UX designers using AI tools
- · Enterprises adopting AI for dev workflows
- · Entry-level coders relying on simple text-to-code
- · Traditional low-code/no-code platforms
- · Companies slow to integrate multimodal AI development tools
Multimodal AI systems will significantly accelerate software development cycles and broaden AI's applicability in creating complex applications.
This could lead to a ' Cambrian explosion' of new applications and interfaces that leverage visual and interactive programming paradigms.
The abstraction layer between human intent and executable code will thin considerably, potentially blurring the lines between design and engineering.
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Read at arXiv cs.CL