Render Structure Uncertainty for HTML Repair in MLLM-based UI-to-Code Generation

Existing MLLM-based UI-to-Code generation methods can generate complete HTML/CSS codes through end-to-end or multi-stage pipelines, yet the generated codes often contain structural errors such as incorrect repeated layouts, wrong component boundaries, or missing interaction slots. Subsequent generations may continue along the erroneous structure, causing the error to propagate and amplify. This paper studies post-generation repair: how can we locate and repair unreliable local DOM structures? We observe that this repair process is ambiguous in two ways: a mismatch between the target screenshot and the output may correspond to multiple plausible DOM subtrees, and a single DOM subtree may admit multiple repairs with different rendered behaviors. We propose Render Structure Uncertainty (RSU)for partial DOM repair, a browser grounded uncertainty layer that can be attached to any base HTML generator. RSU parses the target screenshot into a visual structure graph and converts the browser execution trace of the generated code into a DOM-anchored render structure graph. By matching these graphs, RSU yields structural mismatches and a small set of candidate DOM holes, preserving error attribution uncertainty. For each hole, RSU generates multiple local candidates, renders them in the full page context, and clusters them using render-structure signatures that encode layout relations, repeated patterns, content slots, semantic roles, and invalid states, thereby modeling repair uncertainty. The retained representative candidates form a compact Partial DOM candidate set, over which we perform global reranking to produce the final HTML code. In this way, RSU reframes HTML repair into uncertainty-guided search in render-structure space, avoiding premature commitment to either a single error location or a single local repair. Experiments show that RSU improves the generation quality of base models, producing higher-quality HTML with better layout consistency and fewer local structural errors.