aiqbench analysis
This preprint proposes a fine-tuning method for multimodal LLMs that turn WebUI screenshots into code for React, Vue, and Angular. Its core idea is to order training groups by measured cross-framework compatibility and to use phase-wise adapters to limit interference, targeting compilation failures from mixed framework syntax. The abstract reports improvements over baselines but gives no figures, so the practical gains remain unverified until the full results are available.
Phase-wise MLLM Tuning for Multi-framework WebUI Code Generation
arXiv:2611.01692v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) achieve strong performance in translating WebUI screenshots into HTML/CSS. However, when they faced multiple frameworks (React/Vue/Angular), they often suffer from negative transfer that leads to compilation failures. These failures commonly stem from mixing framework specific syntax and violating framework constraints. This paper studies supervised fine-tuning for multi-framework WebUI code generation and proposes a phase-wise MLLM tuning method driven by compatibility and heterogeneity signals across frameworks. We first apply a normalization mapping that preserves each framework's native syntax while suppressing noise from variable naming and literal content. We then estimate cross framework heterogeneity and compatibility offline, and automatically derive training groups and their ordering by maximizing a phased objective. Finally, we perform phase-wise adapter tuning to reduce cross framework interference. Experiments on multiple benchmarks show that our proposed method improves both generation quality and compilation success rate compared with baselines.
Abstract:Multimodal large language models (MLLMs) achieve strong performance in translating WebUI screenshots into HTML/CSS. However, when they faced multiple frameworks (React/Vue/Angular), they often suffer from negative transfer that leads to compilation failures. These failures commonly stem from mixing framework specific syntax and violating framework constraints. This paper studies supervised fine-tuning for multi-framework WebUI code generation and proposes a phase-wise MLLM tuning method driven by compatibility and heterogeneity signals across frameworks. We first apply a normalization mapping that preserves each framework's native syntax while suppressing noise from variable naming and literal content. We then estimate cross framework heterogeneity and compatibility offline, and automatically derive training groups and their ordering by maximizing a phased objective. Finally, we perform phase-wise adapter tuning to reduce cross framework interference. Experiments on multiple benchmarks show that our proposed method improves both generation quality and compilation success rate compared with baselines.
Accepted at NeurIPS 2026. 27 pages
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2611.01692 [cs.SE]
(or arXiv:2611.01692v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2611.01692 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 247 words