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    <abstract>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/.</abstract>
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%0 Conference Proceedings
%T Looks Right, Works Right: A Project-Level Benchmark for Multi-Screen Mobile App Generation
%A Wu, Fan
%A Gao, Cuiyun
%A Huang, Yiming
%A Xiao, Yang
%A Chen, Yujia
%A Liao, Qing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wu-etal-2026-looks
%X 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/.
%U https://aclanthology.org/2026.emnlp-main.2650/
%P 60294-60319

Markdown (Informal)

[Looks Right, Works Right: A Project-Level Benchmark for Multi-Screen Mobile App Generation](https://aclanthology.org/2026.emnlp-main.2650/) (Wu et al., EMNLP 2026)

• Looks Right, Works Right: A Project-Level Benchmark for Multi-Screen Mobile App Generation (Wu et al., EMNLP 2026)

ACL

• Fan Wu, Cuiyun Gao, Yiming Huang, Yang Xiao, Yujia Chen, and Qing Liao. 2026. Looks Right, Works Right: A Project-Level Benchmark for Multi-Screen Mobile App Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 60294–60319, Budapest, Hungary. Association for Computational Linguistics.