A Dual-Track Framework for Template-Constrained LaTeX Conversion

• URL: http://arxiv.org/abs/2606.23107v1
• Date: Mon, 22 Jun 2026 09:51:11 GMT
• Title: A Dual-Track Framework for Template-Constrained LaTeX Conversion
• Abstract summary: Existing approaches largely depend on either deterministic rule-based converters or pure end-to-end Large Language Model (LLM) generation.<n>We introduce a robust Dual-Track Framework that systematically decouples template formatting from document processing.<n>Our method achieves better structural fidelity, diverse layout, and achieves a higher compilation success rate compared to the previous baselines.
• Score: 1.906311743485569
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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