Equipping Retrieval-Augmented Large Language Models with Document Structure Awareness
School of Computer Science, Beijing Institute of Technology（计算机学院，北京理工大学）；Ant Group（蚂蚁集团）；Southeast Academy of Information Technology, Beijing Institute of Technology（信息科技东南学院，北京理工大学）
While large language models (LLMs) demonstrate impressive capabilities, their reliance on parametric knowledge often leads to factual inaccuracies. Retrieval-Augmented Generation (RAG) mitigates this by leveraging external documents, yet existing approaches treat retrieved passages as isolated chunks, ignoring valuable structure that is crucial for document organization. Motivated by this gap, we propose Retrieve-DocumentRoute-Read (RDR2), a novel framework that explicitly incorporates structural information throughout the RAG process. RDR2 employs an LLM-based router to dynamically navigate document structure trees, jointly evaluating content relevance and hierarchical relationships to assemble optimal evidence. Our key innovation lies in formulating document routing as a trainable task, with automatic action curation and structure-aware passage selection inspired by human reading strategies. Through comprehensive evaluation on five challenging datasets, RDR2 achieves state-of-the-art performance, demonstrating that explicit structural awareness significantly enhances RAG systems' ability to acquire and utilize knowledge, particularly in complex scenarios requiring multi-document synthesis.
University of Cambridge（剑桥大学）；ELLIS Institute Tübingen（ELLIS研究所图宾根）；MPI-IS（马克斯·普朗克智能系统研究所）；Tübingen AI Center（图宾根人工智能中心）；Mila（Mila魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）
KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates
School of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；Department of Electrical Engineering, Tsinghua University（清华大学电子工程系）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室）
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Baidu Inc.（百度公司）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京市大数据管理与分析方法重点实验室）
Heterogeneous Element-Aware Cross-Version Differencing of Scientific Documents via Layout-Aware Alignment and Structure-Aware Reasoning
Beijing Renhe Information Technology Co., Ltd.（北京人和信息技术有限公司）；Key Laboratory of Digital Publishing and Total Process Management of Scientific and Technical Journals（科技期刊数字出版与全程管理重点实验室）
$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
Technical University of Darmstadt（达姆施塔特工业大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；University of Pennsylvania（宾夕法尼亚大学）；Tencent AI Lab（腾讯AI实验室）
DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences (CAS)（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences, CAS（中国科学院大学）；Wenlan School of Business, Zhongnan University of Economics（中南财经政法大学文澜商学院）
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；National University of Singapore（新加坡国立大学）；Baidu Inc.（百度公司）；School of Information, Renmin University of China（中国人民大学信息学院）
