Fan Wu
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2026
Looks Right, Works Right: A Project-Level Benchmark for Multi-Screen Mobile App Generation
Fan Wu | Cuiyun Gao | Yiming Huang | Yang Xiao | Yujia Chen | Qing Liao
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Fan Wu | Cuiyun Gao | Yiming Huang | Yang Xiao | Yujia Chen | Qing Liao
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Recent multimodal large language models can convert visual designs directly into executable code, but real mobile products require multiple screenshots to become a buildable codebase with shared components and working navigation. This project-level setting exposes three limits of existing design-to-code benchmarks: they focus on single-page generation rather than complete codebases, cannot evaluate cross-page navigation, and do not measure project-wide maintainability. We introduce MobileForge, the first benchmark for project-level multi-screen mobile app generation, comprising 29 real mobile apps, 309 human-reviewed screens, structured page-relationship annotations, and 701 navigation test specifications. MobileForge supports five-axis evaluation of build, navigation, visual fidelity, code maintainability, and efficiency. We also propose state-isolated navigation testing to avoid cascading failures in navigation evaluation and an anchor-referenced list-wise visual evaluation protocol to improve visual-judge reliability. Across 174 end-to-end runs on six frontier multimodal LLMs, current models can build mobile-app projects that compile and reach the correct pages, but interactive navigation remains unreliable and visual fidelity and maintainability still lag. The benchmark and supporting materials are available at https://anonymous.4open.science/r/MobileForge-E2E5/.