LaTeXTrans: Structured LaTeX Translation with Multi-Agent Coordination

The paper introduces LaTeXTrans, a collaborative multi-agent system that overcomes the challenges of translating structured LaTeX documents by decomposing, translating, validating, and reconstructing content to ensure both semantic accuracy and the preservation of complex formatting and syntax.

The Gist

Imagine you have a very complex, intricate blueprint for a building. This blueprint isn't just written in plain English; it's written in a special code called LaTeX. This code tells a computer exactly how to draw the walls, where to put the windows, how to format the math equations, and where to place the pictures.

Now, imagine you need to translate this blueprint from English into Chinese or Japanese so a different team can build it.

If you just hand the blueprint to a standard translator (like Google Translate), disaster strikes. The translator sees the code symbols (like \section{} or \begin{equation}) and tries to translate them too, or worse, deletes them. The result? The blueprint is broken. The computer can't read it, the math formulas turn into gibberish, and the building plan falls apart.

LaTeXTrans is the solution to this problem. Think of it not as a single translator, but as a highly organized construction crew working together to translate the blueprint without breaking the structure.

Here is how their "crew" works, broken down into simple steps:

1. The Architect (The Parser)

Before anyone starts translating, the Architect walks through the blueprint.

• What they do: They identify the parts that must not be touched (like the math formulas, the diagrams, and the specific code commands). They cover these parts with "protective tarps" (placeholders).
• The Analogy: Imagine a surgeon covering the vital organs with sterile drapes before operating. The Architect covers the math and code so the translators know: "Do not touch this! Only translate the text around it."

2. The Translation Team (The Multi-Agent System)

Once the blueprint is prepped, a team of four specialists gets to work on the text:

• The Translator: The main writer who translates the sentences.
• The Editor (Validator): This person double-checks the work. They ask, "Did you accidentally translate a math symbol? Did you delete a command?" If they find a mistake, they send it back for a redo. It's like a strict proofreader who ensures the code stays intact.
• The Summarizer: This person keeps a running summary of what was just translated. This helps the Translator remember the context so they don't contradict themselves later in the document.
• The Dictionary Keeper (Terminology Extractor): In academic papers, words like "neural network" or "algorithm" must always be translated the same way. This agent keeps a list of these "special words" to ensure consistency throughout the whole document.

3. The Builder (The Generator)

Once the text is translated and checked, the Builder comes in.

• What they do: They take the "protective tarps" off the math and code, put the translated text back into the original blueprint structure, and hand you a brand new, perfectly formatted document in the target language.
• The Result: You get a document that looks exactly like the original, but the words are in a new language, and the computer can still compile it into a perfect PDF.

Why is this a big deal?

Previously, trying to translate these documents was like trying to translate a recipe while someone kept changing the oven temperature and removing the ingredients. You'd end up with a burnt mess.

LaTeXTrans is like having a team that knows exactly which ingredients are the "code" and which are the "words." They protect the code, translate the words, and put it all back together perfectly.

The Results:
The paper shows that this system is incredibly successful.

• 97% Success Rate: In tests translating English to Chinese, the system produced documents that could be compiled into a PDF without errors 97% of the time.
• Better than the competition: It beats standard translation tools and even other AI models at keeping the document's structure intact.

In short, LaTeXTrans is a smart, collaborative robot team that ensures scientific papers can be shared across language barriers without losing their mathematical precision or structural beauty.

Technical Summary

Here is a detailed technical summary of the paper "LaTeXTrans: Structured LaTeX Translation with Multi-Agent Coordination".

1. Problem Statement

Translating structured LaTeX documents presents unique challenges that standard Machine Translation (MT) systems fail to address effectively.

• Structural Complexity: LaTeX documents interleave natural language with domain-specific syntax (mathematical equations, tables, figures, cross-references, and custom macros).
• Limitations of Existing Approaches:
  • PDF Translation: Compiling LaTeX to PDF first and then translating the image/PDF often leads to formatting errors and loss of source code structure.
  • Source-Level Translation: Directly translating raw LaTeX source files using standard LLMs often results in broken syntax, missing commands (e.g., \textbf), or incorrect handling of non-translatable elements (like math formulas), leading to compilation failures.
  • Current Systems: Existing tools are often limited to short, simple snippets and struggle with multi-file projects, user-defined commands, and complex macro environments found in real-world academic papers.

2. Methodology: The LaTeXTrans Framework

LaTeXTrans is a collaborative multi-agent system designed to translate raw LaTeX source files while preserving structural fidelity and semantic integrity. It operates through three main modules and six specialized agents.

A. Parsing Module

The goal is to decompose complex LaTeX documents into translation-friendly units.

• Parser Agent:
  • Placeholder Substitution: Replaces non-translatable components (math environments, figures, tables, captions) with unique placeholders (e.g., <placeholder_ENV_1>). This isolates the natural language text from structural commands.
  • Segmentation: Splits the document into manageable units (sections, subsections, and captions).
  • Filter Agent: Uses an LLM to dynamically determine if a user-defined environment requires translation (True/False), handling cases where rule-based lists are insufficient.

B. Translation Module

This module employs a team of four agents to ensure high-quality, context-aware translation.

• Translator Agent: Translates the parsed units.
• Validator Agent: Performs a Translator-Validator Iteration. After the Translator generates output, the Validator checks for LaTeX command preservation (e.g., ensuring \left isn't translated as "left"). If errors are found, an error report is generated, and the Translator revises the output. This loop continues until structural integrity is maintained.
• Summarizer Agent: Generates and updates a summary of preceding content to maintain long-range context coherence across the document.
• Terminology Extractor Agent: Extracts domain-specific terms from the source and translation, maintaining a dynamic dictionary to ensure terminology consistency throughout the document.

C. Generation Module

• Generator Agent: Reconstructs the final document by reinserting the translated text and the original placeholders back into the LaTeX structure.
• Compilation: Automatically selects the appropriate engine (e.g., pdflatex or xelatex) based on document characteristics and compiles the result into a PDF.

3. Key Contributions

1. Multi-Agent Architecture: Introduced a novel framework where specialized agents (Parser, Translator, Validator, Summarizer, Terminology Extractor, Generator) collaborate to handle the specific constraints of LaTeX translation.
2. Placeholder & Filtering Strategy: Developed a robust mechanism to isolate non-translatable structural elements, preventing LLMs from hallucinating or corrupting syntax.
3. Iterative Self-Correction: Implemented a Validator-driven loop that specifically targets LaTeX command preservation, significantly reducing compilation errors.
4. Comprehensive Benchmark: Created a new multi-domain, multilingual benchmark using 100 real-world arXiv papers (Computer Science, Physics, Mathematics) to evaluate LaTeX-level translation, a gap previously unaddressed by public datasets.
5. Open-Source System: Released the source code, an online demonstration platform, and a demo video to facilitate further research and adoption.

4. Experimental Results

The system was evaluated on English-to-Chinese (En-Zh), English-to-Japanese (En-Ja), and English-to-Korean (En-Ko) tasks.

• Structural Preservation (Compilation Success):
  • Compilation Success Rate (CSR): LaTeXTrans achieved a 97% one-pass compilation success rate for En-Zh, compared to significantly lower rates for baselines. In Physics and Mathematics domains, CSR reached 100%.
  • Formatting Error Count (FEC): The average formatting error rate was below 0.5 per document, demonstrating superior structural integrity compared to direct LLM translation and tools like gpt-academic.
• Translation Quality:
  • Metrics: Evaluated using COMETkiwi and LLM-score. LaTeXTrans outperformed traditional MT systems (NiuTrans, Google Translate) and matched or exceeded standalone LLMs (Qwen-3, DeepSeek-V3, GPT-4o) while maintaining structural correctness.
  • Human Evaluation: In a blind study, LaTeXTrans produced a significantly higher proportion of "Perfect Preservation" (Level A) outputs compared to gpt-academic.
• Ablation Study:
  • Removing the Parser caused a significant drop in translation quality (COMETkiwi).
  • Removing the Validator increased formatting errors.
  • The Summarizer and Terminology Extractor improved cross-paragraph coherence and terminology consistency.

5. Significance

• Academic Accessibility: By enabling high-quality translation of complex LaTeX papers, LaTeXTrans lowers the barrier for non-native English speakers to access and publish scientific research, addressing the "English dominance" in academia.
• Technical Robustness: It solves the "compilability" problem, ensuring that translated documents are not just readable but also functional and ready for publication without manual reformatting.
• Scalability: The system is designed to handle real-world, multi-file LaTeX projects with custom macros, moving beyond the limitations of snippet-based translation tools.
• Future Direction: It establishes a new paradigm for structured document translation, suggesting that multi-agent coordination is essential for tasks requiring strict adherence to syntax and format constraints.