arXiv:2610.34572v1 Announce Type: new
Abstract: The largest open German text datasets are built from broad web crawls, which provide scale but limited coverage of specific domains. We introduce Refin...
By Maximilian Idahl, Oleh Astappiev, Benedikt Droste, Robert Scholz, Bj\"orn Pl\"uster, Jan Philipp Harries, Robert J\"aschke, Wolfgang Nejdl
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
arXiv:2607. 22662v1 Announce Type: new Abstract: Open-web corpora curated via highly selective filters, such as FineWeb-Edu and DCLM, constitute the core of LLM pretraining data and have significantly advanced LLM performance.
By Peiguang Li, Yongwei Zhou, Juncheng Diao, Yuchun Fan, Jian Yang, Jianxiao Yang, Zhongda Su, Shuguang Jiao, Xiao Wei, Zhiye Zou, Gan Dong, Zhizhao Zeng, Rongxiang Weng, Jingang Wang, Xunliang Cai
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.
By Ziqi Chen, Yingli Zhou, Fangyuan Zhang, Quanqing Xu, Chuanhui Yang, Yixiang Fang
RIT‑RAG is a retrieval‑augmented generation framework that combines content retrieval with structural navigation of documents. It builds a tree for each document from its table of contents or sitemap, then uses retrieved chunk positions to induce manageable sub‑trees that an LLM agent can navigate, read selectively, and reformulate queries. Experiments on financial, scientific, and customer‑support benchmarks show RIT‑RAG outperforms vanilla, graph‑based, and agentic baselines, achieving up to 11.4‑point accuracy gains on the new EntQABench of 2.84 million technical‑documentation webpages.
By Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan, Yatin Nandwani, Sachindra Joshi, Dinesh Raghu
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
By Boyang Zhang, Adrian Lyjak, Eli Stewart, Zhaoqi Li, Simon Suo