Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables. Current approaches for extracting visual content from documents are largely built around generic document layout analysis, where figures and tables are treated as uniformly relevant document objects rather than semantically meaningful analytical artifacts.
arXiv:2606. 06242v1 Announce Type: cross Abstract: Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables.
arXiv:2608. 15064v1 Announce Type: new Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence.
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
arXiv:2607. 29124v1 Announce Type: cross Abstract: Scientific figures often encode the visual evidence behind scientific findings, yet figure plagiarism remains underexplored as a benchmarked multimodal evaluation problem.
Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets. In domains such as finance, healthcare, education, transportation, and enterprise operations, downstream workflows rely on normalized schemas, entity identities, keys, cross-table relationships, and integrity constraints for analytics, compliance, auditing, and SQL-backed decision making.
arXiv:2607. 24745v1 Announce Type: cross Abstract: Key Information Extraction (KIE) is vital for many document applications, but creating training datasets is traditionally a time-consuming manual process.
DocAttriBench (DAB) is a large‑scale benchmark for fine‑grained, element‑level source attribution in Document Visual Question Answering (VQA). It introduces MAPPET, a Mask‑based Perplexity‑Derived Attribution method that uses document layout and language modeling to identify the most informative layout element for each answer. The benchmark contains 237k documents and 296k question‑answer pairs with element‑level grounding, and it evaluates multimodal LLMs on answer accuracy, attribution accuracy, and overall answer quality, revealing that even strong models often fail to localize supporting elements.
arXiv:2608. 08459v1 Announce Type: cross Abstract: Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets.
The paper introduces the Structural Semantic Unit (SSU) and the Coverage, Overlap, Trespass, and Excess (COTe) score as a new framework for evaluating Document Layout Analysis (DLA) models. Unlike traditional metrics such as IoU, F1, or mAP, which are tailored to 2D projections of 3D space, COTe focuses on the semantic structure of printed media and is decomposable to reveal specific failure modes like breaching semantic boundaries or redundant parsing. Experiments on five common DLA models across three datasets show that COTe is more informative and robust—especially under granularity mismatches—than F1, and the authors provide an SSU-labelled dataset and a Python library to facilitate adoption.
arXiv:2609.14352v1 Announce Type: new Abstract: AI-generated image detection has attracted increasing attention, but existing evaluations mainly focus on natural images, leaving AI-generated document...
DocAttriBench (DAB) is a large-scale benchmark that provides fine-grained, element-level source attribution for Document Visual Question Answering (VQA). It introduces MAPPET, a Mask-based Perplexity-Derived Attribution method that uses document layout and language modeling to identify the most informative layout element for each answer. The benchmark contains 237k documents and 296k question-answer pairs with grounding annotations, and it evaluates multimodal LLMs on answer accuracy, attribution accuracy, and overall answer quality.
Transforming table-form documents into machine-processable records requires recovering not only their visible content but also the multilevel structure that organizes it. However, existing benchmarks evaluate either holistic document outputs or conventional table grids, and their aggregate scores provide little insight into where structural failures occur.