LaTeX Placeholder Document Structure

• Placeholder document structure in LaTeX is a systematic representation of document hierarchy using commands like \section and \subsection to define both visual and logical organization.
• It encodes spatial and semantic information, facilitating enhanced tasks such as reading order prediction, information retrieval, and document question answering.
• Leveraging a detect-order-construct framework, deep learning models extract and reconstruct document trees to improve performance in analyzing and summarizing academic texts.

A placeholder document structure in LaTeX refers to the explicit or inferred logical and visual layout of documents generated with the LaTeX typesetting system, where semantic groupings such as sections, subsections, figures, tables, and formulas are encoded using LaTeX’s hierarchical commands. The representation and analysis of these document structures play a pivotal role in document layout analysis, information retrieval, and document question answering, as they provide a blueprint for the logical organization and visual arrangement of content in academic and professional documents.

1. Hierarchical Nature of LaTeX Documents

LaTeX natively encodes document structure using a hierarchical schema. Commands such as \section, \subsection, \paragraph, and environments like \begin{table}...\end{table} and \begin{figure}...\end{figure} define the nesting and organization of content. When a LaTeX document is compiled, these directives manifest as distinct visual features in the resulting PDF—such as heading fonts, numbering, and spatial arrangements—making it possible to infer the logical hierarchy solely from the rendered output in cases where the source is unavailable.

This hierarchy typically forms a tree structure: the root represents the document itself; child nodes represent sections; grandchildren correspond to subsections; and so forth. Each node encapsulates content blocks—paragraphs, figures, tables—organized in reading order as dictated by the logical structure of the source code.

2. Tree Construction and the Detect-Order-Construct Framework

Detect-Order-Construct, a tree construction–based approach, systematically addresses hierarchical document structure analysis through three intertwined stages: detection of page objects, prediction of their reading order, and construction of the intended hierarchical structure (Wang et al., 22 Jan 2024).

1. Page Object Detection (Detect Stage): Detects visually distinct elements such as paragraphs, section titles, figures, tables, and formulas on the rendered document. This is accomplished by deploying top-down graphical object detectors (e.g., Mask R-CNN, Mask2Former) to localize non-text elements, and clustering methods to group OCR- or PDF-extracted text lines into meaningful blocks. Deep learning models are used to extract multiscale visual features for each candidate object:

$$V_{t_i} = LN(\mathrm{ReLU}(FC(\mathrm{ROIAlign}(C_{fuse}, b_{t_i}))))$$

where $V_{t_i}$ is the visual embedding of text-line $t_i$, $b_{t_i}$ is its bounding box, and $C_{fuse}$ is the fused feature map. This feature extraction aids in classifying elements (headers vs. paragraphs vs. equations) based on their visual profiles.

1. Reading Order Prediction (Order Stage): Determines the sequential reading flow among detected objects. While LaTeX documents commonly employ a “top-to-bottom, left-to-right” ordering, layout complexities such as multi-column arrangements may necessitate explicit relation modeling. The relative spatial compatibility between text lines is encoded as:

$$f_{ij} = FC_q(F_{t_i}) \cdot FC_k(F_{t_j}) + MLP(r(b_{t_i}, b_{t_j}))$$

with a softmax normalization to generate transition probabilities $s_{ij}$ for candidate next elements. The approach is robust even when explicit reading order markers are absent, as in placeholder LaTeX documents.

1. Hierarchical Structure Construction (Construct Stage): Predicts and reconstructs the document’s logical tree, leveraging detected section headers and their spatial/semantic relationships. Multi-modal representations, positional encodings (e.g., RoPE), and a specialized TOC Relation Prediction Head are utilized to infer parent–child and sibling connections. For a section heading $sec_i$, the structural relation is scored as:

$$s^{p}_{ij} = \frac{\exp(f_{ij})}{\sum_{j} \exp(f_{ij})}$$

Section headings are recursively inserted into a tree according to these scores, reconstructing the intended hierarchy even from visual-only input.

3. Structure-Preserving Representations: The LaTeX Paradigm

Recent advances in multimodal LLMs (MLLMs) for document understanding emphasize the crucial role of input structure (Liu et al., 19 Jun 2025). Encoding extracted text and objects into a LaTeX-like representation, rather than presenting raw or flat OCR output, preserves both spatial relationships and logical groupings. Key features include:

• Explicit Delimitation: Wrapping content in LaTeX environments (e.g., \section{...} for headers, \begin{table}...\end{table} for data tables) ensures that hierarchy and grouping are visible to downstream models.
• Spatial Correspondence: The structure reflects page layouts, with environment boundaries and ordering mirroring the visual arrangement of elements.

The abstract representation can be summarized as:

$$Structured\_Text = \{\ LaTeX(environment_1),\ LaTeX(environment_2),\dots \}$$

where each environment encodes a distinct document block, preserving the tree’s branches and leaves.

4. Impact on Attention and Model Performance

Structured LaTeX representations influence neural attention mechanisms and overall document comprehension in multimodal models. Experimental attention analyses reveal two key effects (Liu et al., 19 Jun 2025):

• Reduction of Attention Dispersion: When input consists of raw OCR text, attention in MLLMs often diffuses across redundant or non-semantic tokens, including border areas irrelevant to content understanding. In contrast, LaTeX-structured input naturally guides attention to semantically salient regions: titles, main text blocks, figures, and tables.
• Induction of Structured Attention: Models focus more acutely on logically grouped and hierarchically significant sections, improving their ability to associate textual descriptions with corresponding images or data. This focused attention directly correlates with higher performance in document question answering, as empirically demonstrated.

5. Evaluation Methodologies and Benchmarks

Systematic evaluation of placeholder LaTeX document analysis is conducted using benchmarks designed for hierarchical document structure analysis; Comp-HRDoc is a notable example (Wang et al., 22 Jan 2024). This benchmark simultaneously assesses:

• Page Object Detection: Evaluated using segmentation-based mean average precision (mAP).
• Reading Order Prediction: Assessed using reading edit distance scores (REDS).
• Table of Contents Extraction and Structural Tree Matching: Metrics such as Micro-STEDS and Macro-STEDS measure accuracy in reconstructing section containment and nesting.

Predicted tree structures are quantitatively compared to ground-truth extracted from LaTeX sources or manual annotation, ensuring that both physical and logical layouts are faithfully recovered.

6. Practical Example: Constructing Hierarchy From Placeholder LaTeX

Consider the following schematic LaTeX template:

\documentclass{article} \begin{document} \title{A Sample LaTeX Document} \author{Author Name} \maketitle \section{Introduction} This is the introduction text... \subsection{Motivation} Detailed discussion... \section{Methodology} Description of methods... \subsection{Detection Module} Explanation of page object detection... \subsection{Construction Module} Details on the tree construction approach... \end{document}

Upon compilation, the resulting PDF contains visually distinct regions for the title, section headings, and subsections. The Detect stage identifies these as objects with unique bounding boxes; the Order stage sequences them (e.g., “Introduction” before “Methodology”, and “Motivation” under “Introduction”); the Construct stage synthesizes a hierarchical tree such as:

ROOT ├── Title/Author ├── Introduction │ └── Motivation └── Methodology ├── Detection Module └── Construction Module

Comp-HRDoc metrics are then applied to compare this tree to a reference structure.

7. Significance and Implications in Multimodal Document Understanding

Analysis and preservation of placeholder document structure in LaTeX is foundational for robust document understanding, particularly in advanced MLLM systems. By leveraging LaTeX’s inherent organization and encoding detected elements in this paradigm, models not only maintain logical and spatial relations but also exhibit improved comprehension and task performance. Structured LaTeX input mitigates attention dispersion and structure loss, leading to enhancements in document question answering and downstream analytical tasks (Liu et al., 19 Jun 2025). A plausible implication is that standardized structure encoding could generalize to other authoring environments that support hierarchical schemas, provided their layouts can be expressed with similar explicitness.

In summary, understanding and reproducing LaTeX document structure—whether from source or rendered form—enables the faithful extraction, analysis, and manipulation of hierarchical organization essential for information retrieval, summarization, and deep document understanding tasks.