Papers for

ui/ux design teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Multi-agent approach improves code generation across front-end frameworks

MulFCoder: Framework-conditioned Multi-agent for MLLM-based Multi-framework Front-end Code Generation

Abstract: Generating runnable front-end code from UI screenshots is a long-standing goal in automated software engineering. Existing MLLM-based methods predominantly focused on HTML/CSS, leaving multi-framework generation for React/Vue/Angular underexplored. Naively modifying prompts leads to substantial performance gaps across multi-framework and highly framework-specific error modes. To address this, we propose MulFCoder, a framework-conditioned multi-agent method that explicitly encodes framework constraints to bring multi-framework differences into a decidable rule space. MulFCoder orchestrates four agents: Grounder constructs an ElementTable, ContentTable, and macro-layout regions from detected UI elements; Planner builds a DOM-like hierarchical layout tree, produces a task schedule, and derives a framework-specific file Contract; Writer generates structured file writes or patches within a restricted edit window; Judger enforces lightweight, framework-conditioned constraints to accept or reject updates and trigger bounded repairs, preventing drift and deadlocks without expensive builds. Experiments demonstrate that MulFCoder substantially improves compilation success rate and reduces framework-specific errors, with particularly pronounced gains on Angular.

Sun 25 OctSoftware EngineeringArtificial Intelligence
The gist
Generating front-end code from screenshots is hard, especially for different frameworks like React, Vue, and Angular. The authors found that simply changing instructions confuses current AI models, causing many errors. They created MulFCoder, which uses multiple cooperating agents that understand rules unique to each framework, improving code accuracy and reducing errors. This system works especially well for generating Angular code.
Open → 2610.34418v1

Designer tool lets users shape AI-generated user interfaces

GUIDE: Designer-in-the-loop Authoring of Conformant Generative User Interfaces

Abstract: Generative User Interfaces (GenUIs) enable applications to generate interfaces on demand from user needs and context. Like conventional UIs, they must still reflect designers' intent and conform to requirements such as brand identity. Unlike conventional UIs, designers cannot directly specify or see every interface a GenUI may produce, making design intent harder to enforce. We introduce GUIDE (GenUI Development Environment), a system that lets designers continuously inspect and refine GenUI behavior as they create and edit interfaces. GUIDE uses designers' modifications and interactions to adapt GenUIs through prompt optimization and a novel adaptive conformance scoring model. We validate GUIDE's scoring model and system. The scoring model matched or outperformed proprietary LLM baselines on held-out comparisons of real and synthetic application screens. In a study with 12 UI/UX practitioners, participants found GUIDE effective and usable and significantly preferred aligned outputs over a strong baseline using exemplars and a model-generated design.md.

Fri 18 SeptHuman-Computer Interaction
The gist
Creating user interfaces with AI is tricky because designers can’t always see or control what the AI will produce. The authors introduce a system called GUIDE that helps designers inspect and tweak AI-generated interfaces as they build them. GUIDE learns from the designer’s feedback and uses a new way to score how well the AI’s designs match the designer’s goals. Tests showed GUIDE works well compared to other methods, and designers liked it because it gave them more control over the AI’s output.
Open → 2609.21285v1