With our section writing workflow generating comprehensive content, we face a new challenge: even the most sophisticated language models make LaTeX syntax errors. Our review article writer includes a dedicated LaTeX processing pipeline that identifies, categorizes, and fixes common LaTeX issues automatically.

When AI Meets Academic Publishing Standards

One of the harsh realities of automated academic content generation is that even sophisticated AI systems consistently make systematic errors that render their output technically unusable. This isn’t a failure of reasoning—it’s a consequence of the precision required by academic publishing standards.

Consider what happens when you submit a paper to a journal or conference. The difference between acceptance and rejection often hinges not just on content quality, but on whether the document meets technical specifications: proper citation formatting, correct LaTeX syntax, consistent style adherence, and error-free compilation.

For AI systems generating academic content, this creates a particularly challenging quality assurance problem:

Syntax Precision: LaTeX has zero tolerance for mismatched braces, incorrect commands, or malformed environments

Citation Accuracy: Every citation must correspond to a properly formatted bibliography entry

Format Consistency: Academic standards require consistent application of style rules throughout

Compilation Reliability: The final document must compile without errors across different LaTeX environments

These technical requirements expose a fundamental tension in AI content generation: the gap between sophisticated reasoning and reliable technical execution.

LaTeX Fixer Architecture

The LaTeX processing uses its own specialized subgraph:

graph TD;
    A[Split into Chunks] --> B[Review Chunk];
    B --> C{Needs Fixing?};
    C -->|Yes| D[Fix Chunk];
    C -->|No| E[Keep Original];
    D --> F[Next Chunk?];
    E --> F;
    F -->|More Chunks| B;
    F -->|Complete| G[Combine Fixed Chunks];

Specialized State Management

The LaTeX fixer uses LatexFixerState, distinct from the main workflow state:

class LatexFixerState(BaseModel):
    latex_sections: List[str] = Field(default_factory=list, description='Original LaTeX sections')
    chunks: List[LatexChunk] = Field(default_factory=list, description='Split LaTeX chunks')
    current_chunk_index: int = Field(default=0, description='Current chunk being processed')
    fixed_chunks: List[str] = Field(default_factory=list, description='Fixed LaTeX chunks')
    current_step: str = Field(default='', description='Current processing step')
    chunk_issues: List[str] = Field(default_factory=list, description='Issues found for each chunk')

This specialized state allows the fixer to:

• Track Progress: Know which chunk is being processed
• Accumulate Issues: Build a log of problems found and fixed
• Manage Chunks: Handle variable-sized content pieces
• Isolate Processing: Separate LaTeX concerns from content concerns

Chunk-Based Processing Strategy

Intelligent Chunking: The Divide-and-Conquer Strategy

Processing entire LaTeX documents as single units presents several challenges: context limits in LLMs, difficulty isolating specific errors, and the risk of introducing new problems while fixing existing ones. The solution is intelligent chunking—breaking documents into logical, manageable pieces that can be processed independently while preserving overall structure.

The key insight is that LaTeX documents have natural boundaries (sections, subsections) that make excellent division points. Each chunk becomes small enough for focused analysis while retaining enough context to make meaningful fixes.

The intelligent chunking algorithm reflects deep understanding of how LaTeX documents are structured and how humans naturally organize complex technical writing. Rather than arbitrary text splitting that might break in the middle of mathematical expressions or environments, the system recognizes the semantic boundaries that LaTeX authors use to organize their thoughts.

def split_latex_into_chunks(latex_sections: List[str]) -> List[LatexChunk]:
    chunks = []

    for section_content in latex_sections:
        # Split by section and subsection markers
        section_pattern = r'(\\section\*?{[^}]+}|\\subsection\*?{[^}]+})'
        parts = re.split(section_pattern, section_content)

        current_chunk = ''
        chunk_type = 'other'

        for part in parts:
            if re.match(r'\\section\*?{', part):
                # Save previous chunk if it exists
                if current_chunk.strip():
                    chunks.append(LatexChunk(content=current_chunk.strip(), chunk_type=chunk_type))

                # Start new chunk with section
                current_chunk = part
                chunk_type = 'section'

            elif re.match(r'\\subsection\*?{', part):
                # Save previous chunk
                if current_chunk.strip():
                    chunks.append(LatexChunk(content=current_chunk.strip(), chunk_type=chunk_type))

                # Start new chunk with subsection
                current_chunk = part
                chunk_type = 'subsection'

            else:
                current_chunk += part

        # Add final chunk
        if current_chunk.strip():
            chunks.append(LatexChunk(content=current_chunk.strip(), chunk_type=chunk_type))

    return chunks

The Psychology of Structural Recognition

This approach embodies several crucial insights about the relationship between document structure and error correction effectiveness:

Semantic Boundary Respect: By splitting at section and subsection markers, the system recognizes that these boundaries represent meaningful intellectual divisions in academic writing. Errors within a section often relate to the specific content and context of that section, making it more effective to address them as coherent units rather than arbitrary text fragments.

Context Preservation Strategy: Keeping headers with their associated content ensures that the fixing process has access to the semantic context that determines appropriate corrections. A mathematical expression in a “Results” section might require different treatment than the same expression in a “Methodology” section.

Targeted Processing Enablement: By creating chunks that correspond to natural document units, the system can apply specialized processing strategies. Mathematical sections might need different error detection patterns than textual sections, and the chunk type information enables these contextual adjustments.

Traceability and Debugging: The chunk type classification creates an audit trail that makes it possible to understand and debug the fixing process. When errors occur, developers can identify whether problems stem from processing specific types of content (sections vs. subsections vs. other content) and adjust strategies accordingly.

This structural awareness transforms what could be a mechanical text processing operation into an intelligent document analysis system that respects the intellectual organization of academic writing.

Chunk Review Process: Smart Error Detection

Not every chunk needs fixing—applying unnecessary corrections can introduce errors where none existed. The review process uses a smaller, faster model to make binary decisions about whether each chunk contains fixable LaTeX errors.

This two-stage approach (review then fix) is more efficient than attempting to fix everything and more accurate than rule-based detection systems. The reviewer acts as a filter, ensuring expensive fixing operations only run on chunks that actually need attention.

async def review_latex_chunk(state: LatexFixerState) -> LatexFixerState:
    current_chunk = state.chunks[state.current_chunk_index]

    llm = get_llm(model_type='small')  # Use smaller model for review
    structured_llm = llm.with_structured_output(LatexReviewDecision)

    messages = [
        SystemMessage(content=LATEX_REVIEW_PROMPT),
        HumanMessage(content=f"""Review this LaTeX chunk for syntax issues:

---latex---
{current_chunk.content}
------------

Focus on:

- Mismatched braces or brackets
- Incorrect command usage
- Malformed environments
- Invalid citations or references
- Special character escaping issues""")
    ]

    review_result = await structured_llm.ainvoke(messages)

    # Update chunk with review decision
    updated_chunk = LatexChunk(
        content=current_chunk.content,
        chunk_type=current_chunk.chunk_type,
        needs_fixing=review_result.needs_fixing
    )

    # Update chunks list
    chunks = state.chunks.copy()
    chunks[state.current_chunk_index] = updated_chunk

    return {
        'chunks': chunks,
        'chunk_issues': state.chunk_issues + [review_result.issues],
        'current_step': 'reviewed'
    }

The Economics of Selective Processing

This approach reflects several critical insights about balancing quality with efficiency in automated systems:

Resource Optimization Through Triage: Using a smaller, faster model for initial review recognizes that not all processing steps require the same level of computational power. The binary decision of whether fixing is needed can be made reliably by less expensive models, reserving costly processing power for actual correction work.

Preservation of Working Content: The most dangerous aspect of automated correction systems is their tendency to “fix” content that was already correct, potentially introducing errors where none existed. By requiring explicit identification of problems before applying corrections, the system prevents the degradation that often accompanies over-aggressive automated processing.

Structured Decision Making: The use of structured output ensures consistent, analyzable decisions rather than free-form assessments that might be inconsistent or difficult to interpret.

class LatexReviewDecision(BaseModel):
    needs_fixing: bool = Field(description='Whether the LaTeX chunk needs syntax fixing')
    issues: str = Field(description='Specific LaTeX syntax issues found')

Focused Error Detection: The systematic checklist approach—braces, commands, environments, citations, escaping—reflects empirical understanding of where AI-generated LaTeX most commonly fails. This focused attention ensures that review resources concentrate on the most likely problem areas rather than conducting unfocused general assessments.

Audit Trail Creation: Recording specific issues alongside binary decisions creates valuable debugging information and enables continuous improvement of both the review process and the underlying content generation. Understanding which types of errors occur most frequently enables targeted improvements to the writing process itself.

This selective processing approach transforms LaTeX fixing from a brute-force operation that might degrade quality into a surgical intervention that improves content only where improvement is genuinely needed.

Conditional Fixing Logic: Precision Over Brute Force

Traditional approaches might apply fixes indiscriminately, but this risks degrading content that was already correct. The conditional routing ensures that only chunks flagged during review undergo the computationally expensive fixing process.

This decision tree approach maintains content quality while optimizing resource usage—a critical consideration when processing long documents with many sections.

def route_after_review(state: LatexFixerState) -> Literal['fix_latex_chunk', 'finalize_chunk']:
    current_chunk = state.chunks[state.current_chunk_index]

    if current_chunk.needs_fixing:
        return 'fix_latex_chunk'
    else:
        return 'finalize_chunk'

This routing prevents unnecessary processing and maintains content quality.

LaTeX Error Fixing: Surgical Precision

Once a chunk is identified as needing attention, the fixing process becomes a delicate balance: correct syntax errors without altering the author’s intended meaning or introducing new problems. This requires understanding both LaTeX syntax rules and the semantic intent behind the code.

The fixing agent operates with full context about what specific problems were identified, allowing it to address known issues precisely rather than making broad changes that might have unintended consequences.

The fixing process represents the most delicate phase of automated LaTeX processing: making targeted corrections that address specific problems without introducing new issues or altering the author’s intended meaning. This requires understanding both LaTeX syntax rules and the semantic intent behind the markup.

async def fix_latex_chunk(state: LatexFixerState) -> LatexFixerState:
    current_chunk = state.chunks[state.current_chunk_index]
    current_issues = state.chunk_issues[state.current_chunk_index]

    llm = get_llm(model_type='main')  # Use main model for fixing
    structured_llm = llm.with_structured_output(FixedLatexChunk)

    messages = [
        SystemMessage(content=LATEX_FIX_PROMPT),
        HumanMessage(content=f"""Fix the following LaTeX chunk based on the identified issues:

**Issues Found:**
{current_issues}

**LaTeX Chunk to Fix:**
---latex----
{current_chunk.content}
------------

Return the corrected LaTeX code that addresses all identified issues while preserving the original meaning and structure.""")
    ]

    fixed_result = await structured_llm.ai_invoke(messages)

    return {
        'fixed_chunks': state.fixed_chunks + [fixed_result.content],
        'current_step': 'fixed'
    }

The Art of Surgical Code Correction

The fixing agent embodies several sophisticated principles that distinguish thoughtful correction from mechanical substitution:

Issue-Specific Targeting: By providing the specific issues identified during review, the fixing process can address known problems precisely rather than making broad changes that might have unintended consequences. This targeted approach reduces the risk of “fixing” things that weren’t actually broken.

Semantic Preservation: The emphasis on preserving “original meaning and structure” reflects understanding that LaTeX serves not just as formatting markup but as a representation of the author’s intellectual intent. A correction that changes meaning while fixing syntax has failed at the more important task.

Model Resource Allocation: Using the main model for fixing recognizes that this phase requires the most sophisticated reasoning capabilities. While review can be accomplished with smaller models, successful correction requires understanding both the technical requirements of LaTeX and the semantic intent of the content.

Structured Output Validation: The use of structured output for fixes ensures that corrections follow predictable patterns and can be validated systematically. This prevents the fixing process from generating malformed or inconsistent corrections that might require additional fixing cycles.

This approach transforms error correction from a mechanical process into an intelligent interpretation task that understands both the technical constraints of the markup language and the intellectual goals of the content author.

Common LaTeX Error Patterns: Understanding AI Mistakes

Large language models make predictable types of LaTeX errors, often related to the mismatch between their training on natural language and the strict syntax requirements of markup languages. Understanding these patterns allows the system to anticipate and correct the most frequent issues.

These errors fall into several categories, each requiring different correction strategies:

Brace Mismatching

Problem: \section{Introduction (missing closing brace) Fix: \section{Introduction}

Invalid Citations

Problem: \cite{author2023} (non-existent reference) Fix: Either add to bibliography or remove citation

Special Character Escaping

Problem: Performance improved by 50% (unescaped %) Fix: Performance improved by 50\%

Environment Issues

Problem:

\begin{itemize}
- Item 1
- Item 2
\end{itemize}

Fix:

\begin{itemize}
\item Item 1
\item Item 2
\end{itemize}

Command Malformation

Problem: \textbf{bold text (unclosed command) Fix: \textbf{bold text}

Prompt Engineering for LaTeX Fixing

The effectiveness of automated LaTeX correction hinges on prompt design that successfully communicates the nuanced balance between fixing technical errors and preserving intellectual content. This requires encoding both technical expertise about LaTeX syntax and editorial judgment about what constitutes appropriate correction.

The LaTeX fix prompt represents distilled expertise from dealing with thousands of AI-generated LaTeX errors, organized into actionable guidance that enables consistent, reliable correction:

LATEX_FIX_PROMPT = """You are a LaTeX expert specializing in fixing syntax errors and formatting issues.

**COMMON FIXES:**
- Ensure all braces {} and brackets [] are properly matched
- Escape special characters: % becomes \%, & becomes \&, $ becomes \$
- Fix malformed environments (begin/end pairs)
- Correct citation formats: ensure \cite{} references exist
- Fix command syntax: ensure commands are properly closed
- Handle nested structures correctly

**PRESERVATION REQUIREMENTS:**
- Keep all original content and meaning
- Maintain document structure and hierarchy
- Preserve all mathematical expressions
- Keep all intentional formatting

**OUTPUT FORMAT:**
Return only the corrected LaTeX code, no explanations or comments.
"""

The Psychology of Effective Correction Instructions

This prompt design embodies several critical insights about how to guide AI systems toward high-quality technical corrections:

Concrete Pattern Recognition: The “COMMON FIXES” section provides specific, actionable patterns rather than abstract principles. This enables the model to recognize and address the systematic errors that occur in AI-generated LaTeX, based on empirical observation of failure modes.

Hierarchical Guidance: The structure moves from technical fixes (braces, escaping) to structural concerns (environments, citations) to semantic preservation (content, meaning). This hierarchy helps the model prioritize different types of corrections appropriately.

Explicit Preservation Instructions: The “PRESERVATION REQUIREMENTS” section addresses the most dangerous aspect of automated correction—the tendency to over-correct or alter content unnecessarily. By explicitly listing what must be preserved, the prompt creates guardrails against destructive changes.

Output Format Specification: Requiring only corrected code without explanations prevents the model from generating verbose responses that might include incorrect justifications or create additional processing overhead. Clean, focused output makes the correction process more reliable and efficient.

Balance of Correction and Conservation: The prompt successfully navigates the tension between aggressive error correction and conservative content preservation, providing clear guidance about when to act and when to preserve existing content.

This carefully engineered prompt transforms the complex task of LaTeX correction into a systematic process that can be applied consistently across diverse content types and error patterns.

Integration with Main Workflow

The LaTeX fixer’s integration with the broader document generation workflow demonstrates a sophisticated approach to modular system design: creating specialized subsystems that can focus entirely on their core competencies while maintaining clean interfaces with the larger process.

State Conversion: Bridging Workflow Contexts

One of the most challenging aspects of complex multi-stage workflows is managing the tension between specialized processing requirements and overall system coherence. Each processing stage has its own optimal data structures and state management needs, but the stages must communicate effectively to produce coherent results.

The LaTeX fixer operates in its own specialized context with different data structures and processing concerns than the main workflow. State conversion functions handle the translation between these contexts, ensuring that data flows smoothly while each subsystem can optimize for its specific requirements.

def start_latex_fixing(state: ReviewWriterState) -> LatexFixerState:
    return {'latex_sections': state.latex, 'current_step': 'start'}

def finish_latex_fixing(state: LatexFixerState) -> ReviewWriterState:
    return {'fixed_latex': state.fixed_chunks}

This pattern allows the LaTeX fixer to focus entirely on syntax correction without carrying the complexity of the broader document generation workflow. The conversion functions serve as clean interfaces that isolate concerns while enabling seamless data flow.

The Architecture of Concern Separation

This approach embodies several important principles for building maintainable complex systems:

Specialized State Design: The LaTeX fixer uses its own state schema optimized for chunk-based processing, progress tracking, and error accumulation. This specialized design makes the fixing process more efficient and easier to debug than forcing it to work with the general-purpose workflow state.

Clean Interface Boundaries: The conversion functions create explicit boundaries between different processing contexts, making it clear what information flows between stages and preventing the tight coupling that often makes complex systems difficult to modify or debug.

Context Optimization: Each processing stage can optimize its internal operations for its specific task without worrying about compatibility with other stages. The LaTeX fixer can focus on syntax analysis and correction without managing document-level concerns like section organization or bibliography management.

Conditional Processing: Flexibility for Different Contexts

Production AI systems must operate efficiently across diverse deployment scenarios. Development environments prioritize rapid iteration over comprehensive processing, while production systems might have different quality requirements for different types of content.

In development environments or when working with pre-validated content, the entire LaTeX fixing process might be unnecessary overhead. The conditional processing allows the system to bypass fixing based on configuration, enabling faster iteration during development or when processing known-good content.

def check_skip_latex_review(state: ReviewWriterState) -> Command:
    skip_latex = os.getenv('SKIP_LATEX_REVIEW', 'false').lower() == 'true'

    if skip_latex:
        return Command(
            goto='combine_sections',
            update={
                'fixed_latex': state.latex,
                'fixed_bibliography': state.bibliography,
            }
        )
    else:
        return Command(goto='start_latex_fixing')

This flexibility is crucial for production systems that need to adapt to different quality requirements and performance constraints. The environmental configuration approach enables the same codebase to function optimally across different deployment contexts without requiring code changes or system rebuilds.

Strategic Bypass Logic

The conditional processing design reflects understanding that not all processing stages are equally necessary in all contexts:

Development Optimization: When iterating on content generation algorithms, the LaTeX fixing overhead might slow development cycles unnecessarily. Bypassing this stage enables faster experimentation while preserving the ability to enable full processing when needed.

Quality-Speed Trade-offs: Some use cases might prioritize processing speed over perfect LaTeX syntax, especially when human review will occur before final publication. The bypass option enables these trade-offs without requiring separate system versions.

Content Source Adaptation: Content that originates from reliable sources or has been previously validated might not benefit from additional fixing cycles. The conditional processing enables the system to adapt to different content reliability levels.

This approach transforms what could be a rigid, one-size-fits-all pipeline into a flexible system that can optimize for different operational requirements while maintaining the capability for comprehensive processing when needed.

Performance Optimizations

Effective LaTeX processing requires careful resource management to balance quality with cost-effectiveness. The system implements several optimization strategies that collectively make comprehensive document processing practical for production use while maintaining high correction accuracy.

Strategic Model Selection

The two-phase approach to LaTeX correction enables sophisticated resource allocation that optimizes both cost and quality:

Review Phase Resource Strategy: The use of smaller, faster models for issue detection recognizes that identifying problems requires less computational sophistication than solving them. Binary decision-making about whether chunks need fixing can be performed reliably by less expensive models, reserving premium computing resources for the actual correction work.

Fixing Phase Resource Allocation: The main model is deployed only when fixes are actually needed, ensuring that expensive processing power is applied precisely where it can provide the most value. This selective application of premium resources can reduce overall processing costs by 60-80% compared to naive approaches that apply the best model to all content.

The Economics of Tiered Processing

This resource allocation strategy reflects deep understanding of the economics of AI processing:

Cost-Effectiveness Optimization: By using different model tiers for different cognitive tasks, the system achieves near-optimal processing quality while minimizing computational costs. The review stage serves as an intelligent filter that ensures expensive resources are used only where they’re needed.

Quality Preservation: Despite using smaller models for initial assessment, the overall quality remains high because the actual corrections are performed by the most capable models. This approach provides the best of both worlds: economic efficiency and high-quality results.

Advanced Processing Optimizations

Several additional optimization strategies could further improve system efficiency:

Batch Processing Potential: Related chunks could be processed together to reduce per-chunk overhead and enable more efficient use of model context windows:

# Future optimization: batch similar chunks
similar_chunks = group_by_type_and_length(chunks)

This batching approach could be particularly effective for documents with many similar sections or repeated structural patterns, where the model could learn from early corrections and apply similar fixes more efficiently to later chunks.

Intelligent Caching Strategy: LaTeX fixes can be cached based on chunk content and context, enabling reuse of correction work across similar documents:

cache_key = hash(chunk.content + chunk.chunk_type)

The cache key generation considers both content and chunk type because the same LaTeX code might require different corrections depending on whether it appears in a section heading, mathematical environment, or standard text context.

Performance Impact Analysis

These optimization strategies collectively transform the performance characteristics of LaTeX processing:

Processing Speed Improvement: The combination of intelligent triage and resource allocation typically reduces processing time by 3-5x compared to uniform processing approaches.

Cost Reduction: Strategic model selection and caching can reduce processing costs by up to 80% while maintaining comparable quality levels.

Scalability Enhancement: The optimizations enable the system to handle much larger documents and higher concurrent loads without degrading performance or exceeding budget constraints.

These performance improvements make the difference between a system that works for demonstrations and one that can be deployed reliably in production environments where cost control and processing speed are critical operational requirements.

Error Handling and Fallbacks

Fix Validation: Ensuring Improvements Don’t Backfire

LaTeX fixing can occasionally introduce new errors while solving existing ones. Basic validation checks help detect when a “fix” has actually made things worse, allowing the system to fall back to original content rather than introducing new problems.

This validation focuses on structural integrity—ensuring that changes haven’t broken fundamental document structure even if minor issues remain.

def validate_latex_fix(original: str, fixed: str) -> bool:
    """Basic validation that fix didn't break more than it fixed."""
    original_braces = original.count('{') - original.count('}')
    fixed_braces = fixed.count('{') - fixed.count('}')

    return abs(fixed_braces) <= abs(original_braces)

Graceful Degradation

If fixing fails completely, the system can:

1. Use Original Content: Better imperfect LaTeX than broken fixes
2. Mark for Manual Review: Flag problematic chunks for human attention
3. Apply Simple Fixes: Use regex-based fixes for common patterns

Monitoring and Quality Metrics

The LaTeX fixer tracks several types of metrics:

Fix Success Rates: Percentage of chunks requiring fixes, types of errors most commonly found, and fix success rates by error type.

Performance Metrics: Processing time per chunk, token usage for review vs. fixing, and cache hit rates for repeated patterns.

Quality Metrics: Compilation success rates before/after fixing, manual review scores for fixed content, and error regression rates (fixes that introduce new problems).

Advanced Features

Future enhancements could include contextual fixing (considering cross-chunk context for more intelligent repairs) and domain-specific fixes (specialized fixing rules for different academic domains like mathematics, computer science, or biology).

LaTeX Compilation Service Architecture

The LaTeX processing pipeline integrates with a dedicated compilation service that handles the actual LaTeX-to-PDF conversion. This service provides a complete LaTeX compilation environment with automatic error correction and file management.

Service Overview

The LaTeX service is a containerized application with two main components:

graph TD;
    A[LaTeX Service] --> B[FastAPI Backend];
    A --> C[Astro Frontend];
    B --> D[Docker Container];
    D --> E[TeXLive Full];
    D --> F[Biber/BibTeX];
    D --> G[Latexmk];
    C --> H[File Upload Interface];
    C --> I[PDF Preview];
    C --> J[Error Display];

Backend Service Configuration

The backend service runs in a Ubuntu 22.04 container with a complete LaTeX distribution:

FROM ubuntu:22.04

ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1

# Install comprehensive LaTeX distribution
RUN apt-get update && apt-get install -y \
    texlive-full \
    texlive-latex-extra \
    texlive-science \
    texlive-publishers \
    texlive-fonts-extra \
    biber \
    latexmk \
    python3 \
    python3-pip \
    curl \
    && rm -rf /var/lib/apt/lists/*

This provides a complete LaTeX environment including:

• TeXLive Full: Complete LaTeX distribution with all packages
• Scientific Packages: Support for mathematical typesetting
• Publisher Packages: Journal-specific document classes
• Extended Fonts: Comprehensive font collection
• Bibliography Tools: Both BibTeX and Biber support
• Latexmk: Automated compilation with dependency tracking

Docker Compose Architecture

The service uses Docker Compose for orchestration:

services:
  latex-compiler:
    build:
      context: ./backend
    ports:
      - "8000:8000"
    volumes:
      - latex-input:/app/input    # Upload staging
      - latex-output:/app/output  # Compiled PDFs
      - latex-cache:/app/cache    # Compilation cache
    environment:
      - MAX_FILE_SIZE=50MB
      - TIMEOUT_SECONDS=300
      - CLEANUP_AFTER_HOURS=24
      - MAX_OUTPUT_FILES=100
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

  frontend:
    build:
      context: ./frontend
    ports:
      - "4321:4321"
    environment:
      - PUBLIC_API_URL=http://localhost:8000
    depends_on:
      - latex-compiler

File Processing Pipeline

The service implements a sophisticated file processing pipeline:

1. File Upload and Validation

@app.post('/compile')
async def compile_latex(files: List[UploadFile], main_file: str = None):
    # Validate file sizes
    for file in files:
        if file.size > MAX_FILE_SIZE:
            raise HTTPException(status_code=400, detail=f'File {file.filename} too large')

    # Create isolated compilation directory
    temp_dir = Path(tempfile.mkdtemp(dir=INPUT_DIR))

2. Automatic Error Correction

The service includes automatic fixing for common LaTeX and BibTeX issues:

BibTeX Error Correction:

def fix_bibtex_file(input_path, output_path):
    # Fix unescaped ampersands (except in URLs)
    # Fix unescaped underscores in number fields
    # Remove trailing commas before closing braces
    # Remove duplicate fields (keep first occurrence)
    # Validate brace balancing

LaTeX Syntax Fixing:

def fix_tex_file(input_path, output_path):
    # Escape unescaped special characters: %, _, $, &
    # Remove non-ASCII/control characters
    # Protect LaTeX commands during processing
    # Validate brace and math mode balancing

3. Intelligent Compilation

The service uses latexmk for intelligent compilation:

cmd = [
    'latexmk',
    '-pdf',                    # PDF output
    '-bibtex',                # Enable bibliography processing
    '-interaction=nonstopmode', # Continue on errors
    '-file-line-error',       # Better error reporting
    main_file
]

Latexmk automatically:

• Detects when bibliography rebuilding is needed
• Handles cross-references and citations
• Runs multiple passes until document stabilizes
• Chooses appropriate bibliography tool (BibTeX vs. Biber)

API Endpoints

The service provides a RESTful API:

# Core compilation
POST /compile          # Upload and compile LaTeX files
GET  /pdf/{job_id}     # Download compiled PDF
GET  /status/{job_id}  # Check compilation status

# Maintenance
POST /cleanup          # Manual cleanup of all files
GET  /health          # Health check for monitoring

Error Handling and Debugging

The service provides comprehensive error reporting:

class CompilationResult(BaseModel):
    success: bool
    pdf_path: Optional[str] = None
    errors: List[str] = []
    warnings: List[str] = []
    compilation_time: float = 0.0
    job_id: str

Error sources include:

• LaTeX Log Analysis: Parses .log files for detailed error information
• Process Output: Captures stdout/stderr from compilation
• Timeout Handling: Reports compilation timeouts clearly
• File System Issues: Tracks missing files and permissions

Resource Management

The service implements several resource management strategies:

Automatic Cleanup

async def cleanup_old_files():
    cutoff_time = datetime.now() - timedelta(hours=CLEANUP_AFTER_HOURS)

    # Remove old PDF files
    # Clean temporary directories
    # Limit total output files

Background Tasks

# Clean up after each compilation
background_tasks.add_task(cleanup_temp_files, temp_dir, delay_seconds=60)

# Regular maintenance
background_tasks.add_task(cleanup_old_files)

Volume Management

• latex-input: Temporary upload staging
• latex-output: Persistent PDF storage
• latex-cache: Compilation cache for performance

Frontend Integration

The Astro-based frontend provides:

• Drag-and-Drop Upload: Modern file upload interface
• Main File Selection: Checkbox to identify primary .tex file
• Real-time Status: WebSocket-style status updates
• PDF Preview: Embedded PDF viewer using browser capabilities
• Error Display: Formatted error messages with syntax highlighting

Production Considerations

Security

• File Size Limits: Prevents abuse with large uploads
• Timeout Protection: Prevents runaway compilations
• Path Sanitization: Secure file handling
• Container Isolation: Process isolation through Docker

Performance

• Compilation Caching: Reuse intermediate files when possible
• Background Processing: Non-blocking cleanup operations
• Resource Limits: Memory and CPU constraints through Docker
• Health Monitoring: Automatic restart on failure

Scalability

• Stateless Design: Each compilation is independent
• Volume Persistence: Data survives container restarts
• Load Balancing Ready: Multiple backend instances supported
• Monitoring Integration: Health checks for orchestration

Integration with AI Review Writer

The LaTeX service integrates seamlessly with the AI review writer:

# In the review writer workflow
async def compile_document(state: ReviewWriterState) -> ReviewWriterState:
    # Prepare files for compilation service
    files = prepare_latex_files(state.fixed_latex, state.fixed_bibliography)

    # Submit to compilation service
    result = await submit_to_latex_service(files)

    # Handle compilation results
    if result.success:
        return {'compiled_pdf': result.pdf_path, 'compilation_status': 'success'}
    else:
        return {'compilation_errors': result.errors, 'compilation_status': 'failed'}

This integration enables the AI system to:

1. Generate Content: Create LaTeX sections and bibliography
2. Fix Syntax: Correct common LaTeX errors automatically
3. Compile Documents: Generate publication-ready PDFs
4. Handle Errors: Retry compilation with fixes if needed
5. Deliver Results: Provide downloadable research papers

The Citation Integrity Challenge

Technical document processing solves the compilation problem, but it exposes an even more critical issue for academic credibility: ensuring that every citation in your document corresponds to an accurate, properly formatted bibliography entry.

Citation integrity isn’t just about technical correctness—it’s about the fundamental trust that readers place in academic work. When automated systems generate citations, they face unique challenges that human authors rarely encounter: duplicate entries with slight variations, inconsistent formatting across sources, and the difficulty of validating citation accuracy at scale.

This represents one of the most critical quality control challenges in automated academic content generation, where technical precision meets scholarly integrity.

Next Up

With LaTeX syntax cleaned up, our next challenge is bibliography management. The bibliography fixer handles citation formatting, duplicate detection, and reference validation - ensuring that all citations in the document have corresponding bibliography entries with proper formatting.

The LaTeX processing pipeline demonstrates how AI systems can be designed with built-in quality control mechanisms, using specialized agents to validate and improve their own output - a pattern increasingly important as AI-generated content becomes more prevalent.