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Toward Executable Multi-framework Front-end Code Generation with Self-Correction — arXiv:2611.01383

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01383
License (as archived): CC0-1.0
Archived: 2026-11-03T11:06:17.674Z
SHA-256 of the archived text: b36873fab4484517abe93d5bcd7e1955b5162a94d72b6e8bcc8dde878502e716
Stable URL of this page: https://metraipolis.com/documento/dc_2db2fc52880c

How to cite

Toward Executable Multi-framework Front-end Code Generation with Self-Correction — arXiv:2611.01383 — arXiv (en), https://arxiv.org/abs/2611.01383 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2db2fc52880c (Metraipolis archive dc_2db2fc52880c, sha256 b36873fab4484517abe93d5bcd7e1955b5162a94d72b6e8bcc8dde878502e716)

BibTeX

@misc{dc2db2fc52880c,
  title = {Toward Executable Multi-framework Front-end Code Generation with Self-Correction — arXiv:2611.01383},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01383},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2db2fc52880c; sha256 b36873fab4484517abe93d5bcd7e1955b5162a94d72b6e8bcc8dde878502e716},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01383. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generating codes from UI screenshots has recently benefited from MLLMs, yet existing methods centered on HTML targets and transfer poorly to multi-frameworks such as React, Vue, and Angular. However, unlike HTML, which does not entail compilation issues, generating executable code in multi-framework settings is more challenging due to framework-specific differences in syntax and failure modes, as well as the need to maintain global consistency across files. To address this problem, we study executable multi-framework front-end code generation and propose MESCoder, a tree-structured self-corrective generation framework. Our method first extracts a structural scaffold from the screenshot and then constructs sub-trees and assembly relations step by step, transforming large project generation into a sequence of localized decisions. On top of this scaffold, we introduce a unified Project Agent that continuously revises its own partial project under runtime sandbox feedback and dynamically chooses between local repair and cluster level repair. For training, we adopt a two-stage strategy that first initializes the policy offline to learn basic multi-framework generation and repair priors, and then refines it with online reinforcement learning to optimize long horizon executability and final page quality. The resulting framework models multi-framework executability as a conditioned project state transition process and improves the stability and quality of complex front end generation through self correction. Experiments show that our method improves executability, while narrowing the visual-fidelity gap to HTML-based generation. Code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2611.01383 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:59:46.008Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).