ICML 2026poster0 citations

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

Jie Wu, Haoran Ma, Shisong Tang, Yulin Xu, Xiaoyu Kang, Jiechao Gao

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 \textbf{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 FastGate constraints to accept or reject updates and trigger bounded repairs, preventing drift and deadlocks without expensive builds. Experiments demonstrate that MulFCoder substantially improves multi-framework compilation success and reduces framework-specific errors, with particularly pronounced gains on constraint-heavy frameworks.

AgentsTheory
BibTeX
@inproceedings{
wu2026mulfcoder,
title={Mul{FC}oder: Framework-conditioned Multi-agent for {MLLM}-based Multi-framework Front-end Code Generation},
author={Jie Wu and Haoran Ma and Shisong Tang and Yulin Xu and Xiaoyu Kang and Jiechao Gao},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=xUW3aH4ie3}
}