提出双轨框架，让LaTeX转换更准更稳。
A Dual-Track Framework for Template-Constrained LaTeX Conversion
- 分离模板约束与内容处理，离线生成可复用的格式清单
- 混合执行：大模型只负责复杂语义，规则引擎处理确定性任务
- 在7个模板上编译成功率达96%，显著优于基线
随着对高级文档转换需求的增长，将结构化Markdown草稿映射为符合模板要求的LaTeX格式仍具挑战。现有方法主要依赖确定性规则转换器或纯端到端大语言模型（LLM）生成。前者无法正确处理资产插入和模板特定约束，后者易引发语义漂移，导致难以调试的幻觉。为此，我们提出一种稳健的双轨框架，系统性地分离模板格式化与文档处理：离线轨道将模板约束提取为可复用的清单，线上轨道实现混合执行流水线。该流水线仅在推理密集型组件（如语义元数据、参考文献、复杂图文/表格布局）中使用LLM，其余部分交由规则引擎处理。在7个LaTeX模板和56篇已发表研究论文上的实证评估表明，该方法在保持更好结构保真度、满足多样化布局约束方面表现更优，并实现了更高的编译成功率（96%），优于先前基线。
原文摘要 · Abstract (English)
With the increasing demands for advanced document conversion, mapping structured Markdown drafts into template-compliant formats like LaTeX remains a challenge. Existing approaches largely depend on either deterministic rule-based converters or pure end-to-end Large Language Model (LLM) generation. The former fails to correctly handle asset insertions and template-specific constraints, while the latter tends to induce semantic drift, leading to hallucinations that are difficult to debug. To address these limitations, we introduce a robust Dual-Track Framework that systematically decouples template formatting from document processing: an offline track extracts template constraints into a reusable manifest, while an online track implements a hybrid execution pipeline. This pipeline confines LLM usage exclusively to reasoning-intensive components (e.g., semantic metadata, bibliographic references, and complex visual/tabular layouts) while delegating rule-based engines for deterministic processing. Empirical evaluation across 7 LaTeX templates and 56 published research papers demonstrates that our method preserves better structural fidelity, satisfies diverse layout constraints, and achieves a higher compilation success rate compared to the previous baselines.
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