Aug 2026· Proceedings of the 2026 ACM Symposium on Document Engineering· pp. 1-1· 0 citations
TL;DR
By bridging the gap between visually complex documents and machine-readable knowledge, Docling provides a foundation for reliable document understanding in next-generation AI systems.
Abstract
Documents remain one of the primary carriers of knowledge in scientific, enterprise, and governmental settings, yet their complex visual structure—comprising layouts, tables, figures, and multi-column text—poses significant challenges for modern AI systems. Large language models do not natively understand such document structures, while traditional PDF parsers often lose semantic and structural information, leading to noisy or incomplete representations. In this talk, we present Docling, an open-source document processing framework designed to transform heterogeneous documents into high-quality, structured representations suitable for AI applications. Docling combines layout analysis, OCR, table structure recognition, and document assembly into a unified pipeline that produces rich, LLM-friendly outputs such as structured JSON and Markdown while preserving visual grounding and reading order. The framework supports multiple input formats and integrates with modern AI ecosystems, enabling applications including retrieval-augmented generation, schema-based information extraction, and agent-driven workflows. We discuss the architecture of Docling, its emerging ecosystem of models and tools, and its role in enabling scalable, privacy-preserving document intelligence through local execution and open standards. By bridging the gap between visually complex documents and machine-readable knowledge, Docling provides a foundation for reliable document understanding in next-generation AI systems.
This work presents DocMaster, a hierarchical structure-aware document analysis system that parses documents into hierarchical document trees preserving original layouts and constructs a structure-aware semantic index that enables accurate document filtering and in-depth analysis.
Ziqi Chen, Yingli Zhou, Fangyuan Zhang et al.· 0 citations
Large language models (LLMs) have transformed AI, yet they remain fundamentally limited by hallucination, unverifiable reasoning, and shallow evidence grounding. We argue that structure mining-rooted in decades of KDD research on taxonomy induction, ontology design, entity typing, and knowledge graph construction-is the key to overcoming these limitations. This tutorial presents a unified vision in which structuring serves as the enabling foundation for three pillars of next-generation LLM systems: (1) Structured Retrieval, where organizing corpora into ontology-guided multidimensional representations enables SQL-like queries that achieve substantially more precise and complete retrieval than similarity-based approaches; (2) Structured Reasoning, where grounding each inference step in typed, graph-structured evidence transforms opaque generation into auditable, verifiable reasoning chains; and (3) Structured Agent Memory, where multi-dimensional memory architectures bridge external corpus knowledge and experiential agent knowledge through a mutually enriching dual-memory design. Across all three pillars, we highlight how the cooperative interplay between classical KDD techniques and modern LLMs-where KDD defines structural schemas and quality constraints while LLMs execute flexible extraction and reasoning-creates systems that are more reliable, interpretable, and faithful. The tutorial covers both foundational methods and the latest advances (2024--2026), and concludes with open problems and future research directions at the intersection of data mining and LLMs.
Pengcheng Jiang, Jiashuo Sun, Wonbin Kweon et al.· Proceedings of the 32nd ACM...· 0 citations
Long-document question-answering experiments show that human-verified TOC hierarchies and contextual relationships improve reasoning, with their combination providing complementary benefits.
Yuefeng Zou, Yichen Lu, Jingxiao Yang et al.· 0 citations
Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critical for unlocking global knowledge. However, existing benchmarks predominantly focus on high-resource languages like English and Chinese, creating an evaluation blind spot concerning model performance on other languages. While recent Vision-Language Models (VLMs) claim support for hundreds of languages, the lack of ground truth makes it impossible to empirically verify these capabilities. To bridge this gap, we introduce MORE, a large-scale benchmark designed for multilingual document parsing evaluation. MORE distinguishes itself through three key dimensions: (1) Unprecedented Scale: It covers 149 languages, making it the most linguistically diverse benchmark to date; (2) Structural Complexity: Unlike previous works, it extends evaluation beyond plain text to include structural elements such as code blocks, tables, and catalogs; and (3) Data Authenticity: All samples are curated from real-world documents via a model-assisted, human-refined annotation pipeline. We evaluate state-of-the-art models using MORE, establishing new performance baselines for long-tail languages and validating the benchmark's effectiveness in diagnosing model capabilities in realistic, diverse scenarios. The MORE dataset will be available at https://github.com/zimoqingfeng/MORE.
Long Xu, Binghong Wu, Ting-Ting Yu et al.· 3 citations
Structured documents such as invoices, forms, reports, and scientific articles derive meaning from the interplay between spatial layout, textual content, and logical structure. Generative models operating at the pixel or token level often struggle to capture these dependencies effectively. We explore FRAGMENT, a generative framework that represents a document as a typed relational graph and factorizes its distribution as p(structure, content) = p(structure) * p(content | structure). The framework consists of two stages. The first stage, the Architect, is a causally masked Transformer conditioned on document category that autoregressively generates the graph topology and typed spatial relations. The second stage, the Builder, is a GATv2-based graph attention network that enriches the graph with normalized bounding boxes, text, and visual style attributes. Both stages define explicit likelihood models, yielding a tractable document-level likelihood that serves as an anomaly score for forgery detection. For controlled editing, a prompt-conditioned extension injects instruction embeddings into the Builder through cross-attention, enabling semantic and entity-aware modifications. We describe training on DocLayNet and fine-tuning on FUNSD and SROIE. Experiments on DocLayNet, FUNSD, and SROIE evaluate FRAGMENT alongside representative autoregressive, layout-only, and graph-based baselines, providing an empirical analysis of the characteristics and trade-offs of the proposed factorized graph generation framework.
Ayoub El Bouchtili, Guilhaume Leroy-Meline· 0 citations
Despite the remarkable generative capabilities of Large Language Models (LLMs), existing long-form document generation methods frequently suffer from structural collapse and logical drift as the document length increases. We identify the root cause as a persistent structural retrieval gap, where traditional Retrieval-Augmented Generation (RAG) paradigms focus almost exclusively on fine-grained factual snippets while neglecting the macro-level organizational logic required for coherent narratives. To bridge this gap, we propose a pioneering hierarchical structure-retrieved generation framework (HS-RAG) that reconceptualizes the generation task as a systematic retrieval-alignment-fusion process from template to text, marking a fundamental paradigm shift from spontaneous generation to grounded structural anchoring. The proposed framework introduces three core methodological innovations: 1) A novel dual-stream structural indexing mechanism that extracts retrievable prototypes from historical templates, effectively closing the structural retrieval gap; 2) An adaptive query-guided alignment algorithm that maps unstructured user intents into explicit hierarchical logical constraints; 3) A reinforcement learning (RL)-driven hierarchical relevance feedback optimization strategy designed to dynamically maximize the alignment between generated content, retrieved structures, and external knowledge. Extensive experiments across four diverse datasets demonstrate that our approach significantly outperforms state-of-the-art baselines, including GPT-5.2 and advanced RAG variants. Specifically, our framework achieves an exceptional Structure Match Rate (SMR) of 0.951 and a semantic fidelity score (BERTScore) of 0.921 on AcademicPapers dataset, outperforming the strongest SOTA baseline by 5.43% and 4.19%, respectively. These compelling quantitative indicators validate that explicit structural priors are essential for mitigating long-range incoherence in controllable document generation.
Yongpan Wang, Yu Tan, Mingli Song et al.· IEEE Access· 0 citations