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.
By Eunchan Jo, Seounghyun Jang, Sangwoo Mo
KnowForge is a modular pipeline for open‑domain multimodal document question answering that separates retrieval, judging, and reading into distinct agents. It uses a hybrid‑retrieval knowledge‑point index, a Locator agent for searching, a Bridge Judge to enforce a second hop, a Deep Reader that handles text, images, and tables, and a synthesizer with format checking. On the M3DocVQA benchmark it achieves an F1 of 47.5, outperforming prior systems by 11 points, and shows strong gains on MultiHop‑RAG and MMLongBench‑Doc as well.
By Wenlin Lin, Fanzhe Wei
MIDR (Multimodal Indexing for Document Retrieval) is a training‑free framework that enriches document indexes by converting rendered pages into verified textual fields with a multimodal LLM, then indexing those fields with BM25F and optionally fusing with dense retrieval. By shifting multimodal reasoning to index time, MIDR enables text‑centric serving while retaining multimodal evidence, achieving a 23.0% relative gain over BM25 on ViDoRe V3 and outperforming ColQwen2.5 on several domains with significantly smaller index memory and lower query latency.
By Debanjan Mahata, Atharva Tendle, Daniel Preotiuc-Pietro, Yong Zhuang, Ozan Irsoy
arXiv:2607. 24799v1 Announce Type: cross Abstract: Large Language Models tend to hallucinate when answering domain-specific ques tions from scientific documents without prior fine-tuning.
By Alexandru-Andrei Sauc\u{a}, Ana-Luiza Rusnac
arXiv:2610.17057v1 Announce Type: new
Abstract: Vision-based document retrieval has improved rapidly, but current evaluation settings still emphasize short documents and single relevant pages. This l...
By Simon Ott, Yufan Chen, Ruiping Liu, Junwei Zheng, Jiale Wei, Di Wen, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen
MeddeX is a medical report analyzer that fuses a QLoRA‑fine‑tuned LLaMA‑3 (8B) model with Retrieval‑Augmented Generation and a Gemini Vision layer to produce concise, evidence‑backed summaries of medical documents. It processes text‑native PDFs, scanned PDFs, and radiological images via a hybrid extraction pipeline featuring a garbage‑text detection mechanism for corrupted or UTF‑16‑encoded files. The system adds report‑scoped retrieval, an automated longitudinal comparison engine, and a self‑healing backfill pipeline, and is backed by a FastAPI backend, Supabase vector store, and Next.js frontend. Evaluation shows significant gains over the base LLaMA‑3‑Med42‑8B model across ROUGE, BLEU, and BERTScore metrics, underscoring the value of domain‑specific fine‑tuning.
By Rajdeep Pandhare, Rohit Kundaikar, Yakub Kojar, Shreyansh Tiwari
arXiv:2606. 04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel.
By Jingbiao Mei
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.
By Seonok Kim
arXiv:2608.30163v1 Announce Type: cross
Abstract: Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, ex...
By Ruofan Hu, Shengyang Xu, Minjie Hong, Xiaoda Yang, Sashuai Zhou, Ke Lei, Tao Jin, Zhou Zhao
arXiv:2606. 16494v1 Announce Type: cross Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base.
By Jieyuan Liu, Jianyang Gu, Shijie Chen, Jefferson Chen, Zhen Wang
arXiv:2605.27243v3 Announce Type: replace
Abstract: Large vision-language models increasingly rely on long-context modeling to reason over documents, hour-level videos, and long-horizon agent traject...
By Aaron Branson Cigres Li, Zhaowei Wang, Yu Zhao, Yiming Du, Haobo Li, Xiyu Ren, Ginny Wong, Simon See, Lishu Luo, Haodong Duan, Pasquale Minervini, Yangqiu Song
arXiv:2606. 15906v1 Announce Type: cross Abstract: Long-document multimodal question answering requires a system to locate sparse evidence in long PDFs and integrate clues from text, tables, images, charts, and complex layouts.
By Yilong Zuo, Xunkai Li, Jing Yuan, Qiangqiang Dai, Hongchao Qin, Ronghua Li