Retrieval-augmented generation (RAG) enhances large language models (LLMs) by conditioning generation on retrieved external documents, but the effect of retrieved context is often non-trivial. In realistic retrieval settings, the retrieved document set often contains a mixture of documents that vary in relevance and usefulness. While prior work has largely examined these phenomena through output behavior, little is known about how retrieved context shapes the internal representations that mediate information integration in RAG. In this work, we study RAG through the lens of latent representations. We systematically analyze how different types of retrieved documents affect the hidden states of LLMs, and how these internal representation shifts relate to downstream generation behavior. Across four question-answering datasets and three LLMs, we analyze internal representations under controlled single- and multi-document settings. Our results reveal how context relevancy and layer-wise processing influence internal representations, providing explanations of LLMs' output behaviors and insights for RAG system design.
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Department of Mathematical Sciences, Tsinghua University（清华大学数学科学系）
Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG
Johannes Kepler University Linz（约翰内斯·开普勒大学林茨）；Linz Institute of Technology（林茨技术学院）；Institute of Computational Perception（计算感知研究所）；Artificial Intelligence Lab（人工智能实验室）
PrLM: Learning Explicit Reasoning for Personalized RAG via Contrastive Reward Optimization
Gaoling School of Artificial Intelligence\ University of China Beijing China；School of Information Technology；Gaoling School of Artificial Intelligence\ University of China
Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；National University of Defense Technology（国防科技大学）；Chongqing University of Technology（重庆理工大学）
CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstruction
University of Southern Queensland（昆士兰大学）；University of Technology Sydney（新南威尔士大学）；The Hong Kong Polytechnic University（香港理工大学）；National University of Singapore（新加坡国立大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；The Education University of Hong Kong（香港教育大学）
Finding What Matters: Anchoring Context Knowledge with Evolving Indices for Iterative Retrieval
School of Computer Science and Engineering, Northeastern University, China（东北大学计算机科学与工程学院）；Department of Computer Science and Technology, Institute for AI, Tsinghua University, China（清华大学人工智能研究院计算机科学与技术系）；Beijing National Research Center for Information Science and Technology, China（北京信息科学与技术国家研究中心）；School of Computer Science, Beijing University of Posts and Telecommunications, China（北京邮电大学计算机学院）
Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算先进创新中心）；School of Artificial Intelligence, Beihang University（北航人工智能学院）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Kuaishou Technology Co., Ltd.（快手科技有限公司）；School of Information Technology and Management, University of International Business and Economics（对外经济贸易大学信息学院）
DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generation
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎- Rocquencourt 国家信息与自动化研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒尔研究实验室）；Zhejiang University（浙江大学）；Peking University（北京大学）；University of New South Wales（新南威尔士大学）；Macquarie University（麦考瑞大学）；CSIRO’s Data61（CSIRO 数据61）
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）
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context
School of Computer Science, University of Science and Technology of China（中国科学技术大学计算机科学学院）；School of Biomedical Engineering, USTC（USTC生物医学工程学院）；Suzhou Institute for Advanced Reasearch, USTC（USTC苏州市先进研究所）
The Impact of Ideological Discourses in RAG: A Case Study with COVID-19 Treatments
Wichita State University（威斯康星州立大学）；Pontifícia Universidade Católica de São Paulo（圣保罗天主教大学）；Institut national de la recherche scientifique（国家科学研究中心）；Université du Québec en Outaouais（魁北克大学Outaouais）
