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
The paper introduces a production-ready content‑extraction system tailored for generative AI workloads, addressing the heterogeneity of enterprise data formats such as PDFs, spreadsheets, and scanned documents. It features selective OCR routing, a scarcity‑first curation engine with a reference‑based extraction scorer, a deterministic structure‑aware chunker, and a read‑only retrieval evaluator that generates grounded questions and reports metrics like Hit@k and MRR. On a 180‑document corpus, the system achieves high accuracy (97.4/100 character score, 0.13% error rate) and strong retrieval performance (Hit@1 68.6%, Hit@10 92.8%, MRR 0.77).
By Abbas Raza Ali, Muhammad Ajmal Siddiqui, Moona Zahid
The paper introduces a lightweight CPU-based extension to GROBID that uses layout-guided masking to identify figure, table, and paratext regions in scientific PDFs. By routing tokens to specialized GROBID models or discarding them, the method improves structural accuracy on PMC corpora and enhances figure caption recovery. It also achieves competitive table detection and body‑text precision compared to vision‑based GPU parsers while operating entirely on CPU and costing significantly less.
By Luca Foppiano, Sana Khamassi, Vipul Gupta
arXiv:2608. 15064v1 Announce Type: new Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence.
By Yuefeng Zou, Yichen Lu, Jingxiao Yang, Bingtao Fu, Gaoyang Zhang, Xiongfei Bai, Tian Chen, Xiang Qi
The paper evaluates how different chunking strategies—fixed-size, recursive paragraph-first, and content-defined—affect the cost of updating embeddings when Wikipedia documents are revised. Using 120 pairs of Chinese and English revisions, the authors find that fixed-size chunking requires the most recomputation (57‑68 % of the workload), while recursive and content-defined chunking need less (38‑56 % and 43‑54 % respectively). Despite these differences, evidence recall under a 2048‑token retrieval budget varies by less than two percentage points across methods and languages.
By Yuning Bian, Lele Xu, Yifan Yuan, Xinyu Yin, Chengyan Zhan
arXiv:2603. 26815v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems for financial document QA typically follow a chunk-based paradigm: documents are split into fragments, embedded, and retrieved by similarity.
By Zhiyuan Cheng, Longying Lai, Yue Liu
Unlimited OCR 2 is a document parsing model that processes only the text it transcribes, avoiding re‑scanning earlier pages. It replaces all decoder attention with Sparse Reference Sliding Window Attention (SR‑SWA), keeping computational costs constant regardless of document length. The model achieves significant per‑step speedups over Full Attention and R‑SWA, and sets new state‑of‑the‑art results on the MPDocBench and MarathonDocBench benchmarks for hundred‑page documents.
By Shu Wei, Jingjing Wu, Qunyi Xie, Lingshu Zhang, Xiangcheng Du, Yannian Fu, Mengjie Wang, Zuyi Wang, Zhichao Liu, Yangliu Xu, Youyang Yin, Huanhuan Liu, YY, Yi Yang
arXiv:2604.27724v2 Announce Type: replace
Abstract: Medical retrieval-augmented generation (RAG) systems typically operate on text chunks extracted from biomedical literature, discarding the rich vis...
By Xupeng Chen, Binbin Shi, Chenqian Le, Jiaqi Zhang, Kewen Wang, Ran Gong, Jinhan Zhang, Chihang Wang
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
ShredBench is a new benchmark for evaluating multimodal large language models (MLLMs) on the task of reconstructing documents from shredded fragments. It uses an automated pipeline that generates fragmented documents from Markdown, covering English, Chinese, code, and table scenarios with varying fragmentation levels. Experiments show that while MLLMs perform well on intact documents, their performance drops sharply when documents are shredded, revealing a gap in fine‑grained cross‑modal reasoning.
By Zichun Guo, Yuling Shi, Wenhao Zeng, Chao Hu, Haotian Lin, Terry Yue Zhuo, Jiawei Chen, Xiaodong Gu, Wenping Ma
arXiv:2606.11350v2 Announce Type: replace
Abstract: Retrieval-augmented generation (RAG) degrades when applied to large, heterogeneous document collections. In RAG systems, dense similarity becomes l...
By Nabaraj Subedi, Ahmed Abdelaty, Shivanand Venkanna Sheshappanavar
AmbiHTMLBench is a new benchmark that evaluates how well models can convert real-world documents into executable HTML while preserving content, organization, and visual appearance. It contains 1,115 pages with structured annotations and uses both objective metrics (table fidelity, text error, layout consistency) and a reference-anchored subjective protocol (LLM judges aligned with human preferences). Results show that different systems excel in objective versus subjective measures, and fine‑tuning a small expert model improves performance, highlighting the benchmark’s role as a diagnostic tool for advanced document parsing.
By Boyu Niu, Yingjie Xiong, Shurui Li, Linhan Pang, Yuntong Zhang, Han Wang, Hao Wu, Kai Zuo, Xu Tang, Xuanhe Zhou, Fan Wu