Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation

Tianyi Xiong; Zhengyuan Yang; Xiaofei Wang; Chung-Ching Lin; Ruichun Ma; Kevin Lin; Zhendong Wang; Linjie Li; Chenxi Liu; Ruibo Chen; Ramani Duraiswami; Heng Huang; Lijuan Wang

arXiv · 2608.24138

Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation

Tianyi Xiong·Zhengyuan Yang·Xiaofei Wang·Chung-Ching Lin·Ruichun Ma·Kevin Lin·Zhendong Wang·Linjie Li·Chenxi Liu·Ruibo Chen·Ramani Duraiswami·Heng Huang·Lijuan Wang

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Abstract

Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify a fundamental obstacle, termed visual repair coupling: a local code edit may propagate through layout, style, and component dependencies, correcting one visual mismatch while degrading regions that were previously faithful. To address this issue, we present RubSE, a Rubric-guided Self-Evolution framework that uses rubrics to represent visual feedback as a structured visual-repair context. At each refinement round, RubSE generates typed candidate rubrics, selects one prioritized repair target, and stores previously selected rubrics as history, thereby steering each revision toward a well-scoped visual repair while discouraging repeated or over-broad changes. Evaluations across six VLMs and three UI-to-code benchmarks demonstrate that RubSE substantially outperforms naïve self-evolution in final-round and best-round settings, achieving more stable refinement trajectories and a higher trajectory-level performance ceiling. Further analysis shows that RubSE mitigates trajectory collapse by improving recovery from severe visual regressions, and that stronger rubric generators can transfer effective visual-repair guidance to weaker code improvers.

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Tianyi Xiong, Zhengyuan Yang, Xiaofei Wang, Chung-Ching Lin, Ruichun Ma, Kevin Lin, Zhendong Wang, Linjie Li, Chenxi Liu, Ruibo Chen, Ramani Duraiswami, Heng Huang, Lijuan Wang. 2026-08-25. Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation. https://arxiv.org/abs/2608.24138

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