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document-understanding

topic4 events
papersTODAY 04:00 UTC

MED-VRAG: Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering

A new arXiv paper introduces MED-VRAG, a retrieval-augmented generation approach for medical question answering that works with full document pages rather than only extracted text chunks. Existing medical RAG pipelines typically discard tables, figures, and page layout, so the proposed method retains that visual information and applies retrieval iteratively. The authors argue this multimodal, multi-step design better serves medical QA tasks.

rag/medical/multimodal/research
papersTODAY 04:00 UTC

Audit Finds Frontier Agents Lose Accuracy When Evidence Is Buried in Documents

A controlled data-room audit tested how frontier models answer questions when supporting evidence sits in hard-to-find locations instead of being directly surfaced. Burying the evidence lowered accuracy while increasing forced declarations, the number of tool calls, and the cost per correct answer. The authors conclude that strong results on shallow document and chart reading tasks can conceal these weaknesses.

agents/evaluation/benchmarks/safety
papersSEP 12 04:00 UTC

TeleOCR Paper Targets Document Parsing for Both Digital and Camera-Captured Files

A new arXiv paper introduces TeleOCR, a method for converting unstructured documents into structured, machine-readable output. The work focuses on the gap between digitally born PDFs and photos of physical documents, a distinction that existing vision-language model approaches often handle unevenly. It falls within ongoing research into applying VLMs to document understanding tasks.

document-parsing/vision-language-models/ocr/research