arXiv AI
VL-DocIR: A Benchmark for Vision-Based Long Document Information Retrieval
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arXiv AI
Jul 29
VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents
arXiv:2607. 24748v1 Announce Type: cross Abstract: Visually-rich documents such as reports, slides, and manuals often distribute the evidence needed to answer a question across multiple pages, mixing text with layout cues, tables, charts, and figures.
arXiv Computation and Language
2d ago
Document-as-Image Representations Fall Short for Scientific Retrieval
arXiv:2604.18508v2 Announce Type: replace-cross Abstract: Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the un...
arXiv Computation and Language
2d ago
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search
arXiv:2610.20367v1 Announce Type: new Abstract: Retrieving information from visually rich documents requires matching queries against pages that contain complex evidence in tables, figures, and layou...
arXiv AI
Aug 18
LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents
arXiv:2608. 15064v1 Announce Type: new Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence.
arXiv AI
Aug 18
What the Reranker Sees: Multi-Aspect Page Annotation for Long-Document Multimodal Question Answering
arXiv:2608. 14841v1 Announce Type: new Abstract: Long-document visual question answering (VQA) over documents of tens to hundreds of pages mixing text, tables, charts, and figures typically follows retrieve-then-read pipelines.
arXiv AI
1d ago
Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG
The paper introduces Loud and Clear (LoC), a method that enhances multimodal document retrieval-augmented generation by fully utilizing visual token capacity and refining retrieved pages to highlight evidence. LoC combines higher-resolution pages with OCR of query-relevant regions, achieving significant accuracy gains over eight prior methods on multiple benchmarks. The study demonstrates that the way retrieved pages are presented to the generator is a critical bottleneck beyond retrieval itself.