A Dual-Track Framework for Template-Constrained LaTeX Conversion
Proposes a dual-track framework combining rule engines and LLMs, boosting LaTeX template conversion fidelity and success rate.
Key Findings
Methodology
The framework employs an offline track to extract template constraints into a reusable manifest, reducing runtime inference. The online track implements a hybrid pipeline: deterministic syntax mapping via rule engines, complex reasoning via LLMs (e.g., Qwen-Plus). It divides tasks into three regions—deterministic, probabilistic, neuro-symbolic—guided by the manifest. Local validation ensures structure integrity, while the pipeline merges validated segments for final compilation. This neurosymbolic approach balances efficiency, robustness, and fidelity, leveraging neural reasoning only where necessary.
Key Results
- Across 7 LaTeX templates and 56 papers, success rate reached 94.6%, outperforming rule-based Pandoc (82.1%) and end-to-end LLM (62.5%). Structural retention (SRR) improved by 10%, with stable complex asset and citation handling. The hybrid pipeline reduced inference time by 5× and token consumption by 60%. Asset layout and mathematical environments showed minimal drift, validating robustness. Ablation studies confirmed template extraction and validation as key contributors.
- The system maintained high citation and semantic fidelity, with ROUGE-L and BERTScore F1 scores comparable to baselines. Asset preservation was enhanced, especially in complex layouts. The approach demonstrated consistent performance across venues, highlighting its scalability and reliability in real-world academic workflows.
- Overall, the framework effectively mitigates semantic drift and structural errors typical of pure LLM methods, enabling high-quality, automated LaTeX document generation suitable for production environments.
Significance
This work addresses a core challenge in automated academic publishing: reliably converting loosely structured Markdown into template-compliant LaTeX. By systematically integrating rule-based and neural reasoning, it overcomes the limitations of pure rule engines and end-to-end LLMs. The approach offers a scalable, auditable, and efficient solution, significantly advancing the state-of-the-art in structured document automation. Its success in complex asset handling and template adherence paves the way for broader adoption in enterprise content management, digital publishing, and scientific communication, marking a substantial step toward intelligent, reliable document engineering.
Technical Contribution
The paper introduces a novel dual-track architecture that decouples template constraint extraction from document processing. It innovatively combines rule engines for deterministic syntax with neural models for complex reasoning, guided by a precomputed manifest. The multi-region task division and local validation mechanisms ensure high fidelity and robustness. This hybrid pipeline reduces inference costs and improves stability, setting new benchmarks for template-constrained document automation. The integration of neurosymbolic systems with offline template analysis represents a significant technical advance, enabling scalable, auditable, and efficient LaTeX conversion.
Novelty
This is the first systematic integration of offline template constraint extraction with online hybrid inference for LaTeX conversion. The multi-region task segmentation, combined with local validation, addresses the long-standing challenge of balancing structural fidelity and semantic correctness. Unlike prior work that relied solely on rule-based or neural methods, this framework leverages their complementary strengths, achieving unprecedented success rates and robustness in template-constrained environments. Its neurosymbolic architecture offers a new paradigm for structured document automation.
Limitations
- Asset layout handling is limited to simple placeholders, lacking support for multi-panel figures or non-standard layouts, which may cause asset duplication or layout verification issues.
- Extremely high asset density (>10 assets) or complex tables with non-standard markup can lead to parsing failures or asset loss, especially in multilingual or non-standard templates.
- Current validation and asset processing are primarily tested on English computer science papers, limiting generalization to other disciplines, languages, or citation styles. Further adaptation and robustness improvements are needed.
Future Work
Future efforts will focus on extending multi-modal asset support, including multi-panel figures and non-standard layouts, as well as enhancing validation mechanisms for high-density assets. Cross-lingual adaptation and support for diverse academic disciplines will be prioritized. Additionally, expanding the framework to other structured formats like HTML and XML will facilitate broader enterprise applications. Improving scalability, automation, and user interface integration will further promote adoption in real-world workflows.
AI Executive Summary
Converting structured Markdown drafts into LaTeX templates is a critical challenge in academic publishing and scientific documentation. Traditional rule-based systems like Pandoc struggle with complex asset layouts and strict template constraints, leading to errors and low success rates. Conversely, end-to-end neural models, while flexible, often generate structurally inconsistent or semantically drifting outputs, making debugging difficult. Addressing this, the authors introduce a dual-track framework that systematically separates template constraint extraction from document processing.
The offline track analyzes LaTeX templates to extract formatting constraints, generating a reusable manifest that guides the online pipeline. This online pipeline employs a hybrid approach: deterministic syntax mapping handled by rule engines for predictable regions, and neural reasoning via models like Qwen-Plus for complex assets, references, and semantic regions. The pipeline incorporates local validation layers to prevent structural hallucinations, ensuring high fidelity and robustness.
Extensive experiments across 7 templates and 56 research papers demonstrate the framework’s effectiveness. The success rate reaches 94.6%, surpassing rule-based and pure neural methods. Structural retention rates improve by 10%, and inference speed increases fivefold, with token consumption reduced by 60%. Asset and mathematical environment preservation are notably enhanced, with minimal drift observed.
This approach significantly advances automated document engineering, offering a scalable, auditable, and efficient solution for academic and enterprise applications. While limitations remain in handling highly complex assets and cross-lingual scenarios, future work aims to extend multi-modal support, improve validation, and broaden applicability to other structured formats, promising a new era of reliable, intelligent document automation.
Deep Dive
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.