arXiv:2607. 15082v1 Announce Type: cross Abstract: Understanding newspaper images remains a challenging task due to their complex, nested hierarchical structures and dense, heterogeneous layouts.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 22679v1 Announce Type: new Abstract: In visually-rich documents, information is encoded not only in individual page objects such as tables, headers, and text blocks, but also in the structural relations among them, making document structure analysis fundamental to information retrieval and document understanding.
arXiv:2608. 12898v1 Announce Type: cross Abstract: Document parsing aims to transform unstructured documents into structured and machine-readable representations.
arXiv:2606. 04231v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation.
arXiv:2607. 08539v1 Announce Type: cross Abstract: Leveraging large language models (LLMs) to analyze complex documents -- such as academic papers, technical manuals, and financial reports -- has emerged as a mainstream and critical task in both research and industry.
arXiv:2608. 15064v1 Announce Type: new Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence.
arXiv:2607. 04625v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset.