https://ym-xu.github.io/living-survey/fields/rag/#tag=agenticRetrieval inside a reasoning loop: multi-hop, query planning, search agents.Thu, 12 Nov 2026 15:12:01 +0000https://arxiv.org/abs/2611.10657https://arxiv.org/abs/2611.10657Sat, 07 Nov 2026 00:00:00 +0000Haoran Luo, Ziyue Zhu, Shangyang Wu, Linhao Luo, et al. · NeurIPS 2026 Driven by increasingly complex real-world applications, Retrieval-Augmented Generation (RAG) has evolved from textual pipelines to multimodal settings, comprising two complementary dimensions: knowledge organization and retrieval enhancement. However, existing methods still face challenges of high construction cost, co…https://arxiv.org/abs/2611.10477https://arxiv.org/abs/2611.10477Sat, 07 Nov 2026 00:00:00 +0000Yi Ge, Heng Huang · NeurIPS 2026 Reinforcement-learning-trained agents for retrieval-augmented generation retrieve content (passages, knowledge-graph fragments, or hyperedges) and supervise training with outcome rewards on the final answer. Across these paradigms the reasoning route through retrieved content never leaves the language model's hidden st…https://arxiv.org/abs/2611.09423https://arxiv.org/abs/2611.09423Fri, 06 Nov 2026 00:00:00 +0000Sofia Campos, Carlos Martins, Pedro Pires, João Gama, et al. In the healthcare sector, claim denials impose significant financial and administrative burdens, emphasizing the need for automated and trustworthy decision-support systems. This paper proposes a modular Agentic Artificial Intelligence (AI) framework for healthcare claim denial analysis, integrating specialized agents …https://arxiv.org/abs/2611.09414https://arxiv.org/abs/2611.09414Fri, 06 Nov 2026 00:00:00 +0000Rosario Napoli, Antonio Celesti, Massimo Villari, Maria Fazio Knowledge Graphs (KGs) and Large Language Models (LLMs) represent two approaches for knowledge representation and reasoning over heterogeneous data. Recent research has explored their integration through Graph Retrieval-Augmented Generation (GraphRAG) systems, which use KGs to provide structured context to LLMs. Howeve…https://arxiv.org/abs/2611.09407https://arxiv.org/abs/2611.09407Fri, 06 Nov 2026 00:00:00 +0000Ye Zihao, Lin Xiaoheng Large language models (LLMs) can draft plausible travel itineraries, but the plans they produce are frequently infeasible: attractions are grouped into days without regard for geography, opening hours are ignored, and a single generation pass cannot reliably organize a multi-day trip. We present ROAM, a retrieval-augme…https://arxiv.org/abs/2611.08935https://arxiv.org/abs/2611.08935Fri, 06 Nov 2026 00:00:00 +0000Jing Hao, Weinan Zhou, Zhihong Lu, Yaling Shen, et al. Reliable dermatology AI requires access to verifiable evidence distributed across heterogeneous textual and visual sources. However, conventional retrieval-augmented generation (RAG) typically relies on one-shot retrieval from flat corpora, limiting its ability to adapt retrieval granularity, explore multi-hop relation…https://arxiv.org/abs/2611.07393https://arxiv.org/abs/2611.07393Thu, 05 Nov 2026 00:00:00 +0000Vihan Lakshman, Peter Baile Chen, Zhuohan Gu, Michael Cafarella · CIKM 2026 Workshop Recent work in machine learning has demonstrated that transformer-based language model agents can communicate state not only through text, but also directly in latent space via key-value (KV) caches. In this paper, we argue that such cache-to-cache translation can provide utility beyond the original motivating applicat…https://arxiv.org/abs/2611.06481https://arxiv.org/abs/2611.06481Wed, 04 Nov 2026 00:00:00 +0000Jyotin Goel, Abhinav Arun, Tejas Prakash Agarwal, Bhaskarjit Sarmah, et al. Graph-based retrieval-augmented generation (GraphRAG) answers questions by retrieving over a knowledge graph built from a corpus. Yet retrieval strategies are designed around the structure their own construction pipeline produces, and whether they survive a change of construction is rarely tested. We study this cross-c…https://arxiv.org/abs/2611.04625https://arxiv.org/abs/2611.04625Tue, 03 Nov 2026 00:00:00 +0000Manzong Huang, Chenyang Bu, Yi He, Jiapu Wang, et al. Multi-hop retrieval-augmented generation (RAG) supports Web information seeking and fact verification by recovering complementary evidence across related pages within a limited retrieval budget. Scoring evidence independently against the original question can miss supporting units whose relevance emerges through interm…https://arxiv.org/abs/2611.02810https://arxiv.org/abs/2611.02810Mon, 02 Nov 2026 00:00:00 +0000Yingtao Ren, Xiao Luo, Yiwei Fu, Jinzhao Zhou, et al. · EMNLP 2026 This paper studies retrieval-augmented generation (RAG), which leverages external knowledge to improve the performance of language models. Despite the remarkable progress, current RAG approaches are still far from satisfactory due to inadequate retrieval and potential hallucinations. Towards this end, we propose a nove…https://arxiv.org/abs/2611.02286https://arxiv.org/abs/2611.02286Sun, 01 Nov 2026 00:00:00 +0000Ahmet Bahaddin Ersoz, Justin McHenry, Yevgeniya Kovalchuk, Edward Barrett, et al. The rise of agentic artificial intelligence (AI) in the built environment is shifting attention from isolated AI tools toward autonomous systems that reason, plan, and act across project and organizational workflows. The effectiveness of such systems depends not only on large language model capabilities, but also on th…https://arxiv.org/abs/2611.01910https://arxiv.org/abs/2611.01910Sun, 01 Nov 2026 00:00:00 +0000Sonam Gupta, Vignesh P, Vishwajeet Kumar, Adnan Qidwai, et al. Retrieval-Augmented Generation (RAG) for multi-hop reasoning requires retrieval to adapt as intermediate evidence reveals new information needs. Existing approaches support such retrieval through explicit graph structures or iterative query reformulation, introducing graph-construction or query-time overhead. We presen…https://arxiv.org/abs/2610.42068https://arxiv.org/abs/2610.42068Fri, 30 Oct 2026 00:00:00 +0000Xin Ming Li, Peng Hu Low-Earth orbit (LEO) satellite networks present a challenging routing environment due to rapidly changing topology, traffic, and link conditions. Existing deep reinforcement learning (DRL) routing methods typically rely on current observations and information encoded in network weights, limiting their ability to explo…https://arxiv.org/abs/2610.40409https://arxiv.org/abs/2610.40409Thu, 29 Oct 2026 00:00:00 +0000Chenjie Qiu, Zixuan Zeng, Shaobin Shi, Bingfeng Chen, et al. Complex questions in retrieval-augmented generation require evidence spread over several documents, and an answer is reliable only when the retriever finds all of it. Chain embedding retrievers collect such evidence step by step, re-encoding the question together with the passages retrieved so far, and we find that the…https://arxiv.org/abs/2610.39671https://arxiv.org/abs/2610.39671Thu, 29 Oct 2026 00:00:00 +0000Lingkai Kong, Yuzhe Gu, Haiteng Zhao, Zijian Wu, et al. Large language models (LLMs) draw on parametric knowledge and retrieval-augmented generation (RAG) to solve knowledge-intensive tasks. Yet problem solving in unfamiliar domains calls for tightly coupled knowledge acquisition and reasoning, beyond factual retrieval and aggregation or learning solely from a preassembled …https://arxiv.org/abs/2610.39231https://arxiv.org/abs/2610.39231Thu, 29 Oct 2026 00:00:00 +0000Siqing Huo, Andrew Parry, Debasis Ganguly, Mark Smucker, et al. Recent systems applying retrieval-augmented generation (RAG) and "deep research" iteratively generate queries, retrieve documents, and examine their contents, using the resulting information to guide subsequent search steps. When this process is carried out autonomously rather than by a human searcher, the system must …https://arxiv.org/abs/2610.37826https://arxiv.org/abs/2610.37826Tue, 27 Oct 2026 00:00:00 +0000Dingyi Kang, Bingzhe Li Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant passages from a large corpus. However, RAG struggles with multi-hop questions, which require chaining facts spread across several documents through intermediate entities that the question never mentions. Graph…https://arxiv.org/abs/2610.37080https://arxiv.org/abs/2610.37080Tue, 27 Oct 2026 00:00:00 +0000Peichun Hua, He Sun, Zirui Zhuang, Qi Qi, et al. Web retrieval-augmented generation (RAG) splits a page into fixed-size text chunks or, in recent visual pipelines, screenshot tiles, and the retrieved item serves at once as the unit of indexing, ranking, and reading. We trace answer evidence from source HTML to reader input over the full English Wikipedia and find tha…https://arxiv.org/abs/2610.36991https://arxiv.org/abs/2610.36991Tue, 27 Oct 2026 00:00:00 +0000Zafer Sahinoglu, Abdulazeez Mohammed, Buse Yaren Kazangirler, Cahit Berkay Kazangirler Retrieval-augmented generation (RAG) systems ground large language models (LLMs) by retrieving external evidence and conditioning generation on the retrieved context. Although dense retrieval is effective for local semantic matching, independently ranked retrieval can return redundant passages, miss cross-chunk depende…https://arxiv.org/abs/2610.36444https://arxiv.org/abs/2610.36444Tue, 27 Oct 2026 00:00:00 +0000Zekai Chen, Hao Zhu, Yu Liu, Xunkai Li, et al. Web-based applications increasingly rely on understanding long multimodal documents, where answering questions often requires combining information across pages, tables, and figures. Agentic retrieval-augmented generation addresses these tasks through iterative retrieval and reasoning. However, Agentic execution is not…https://arxiv.org/abs/2610.36433https://arxiv.org/abs/2610.36433Tue, 27 Oct 2026 00:00:00 +0000Xianxiang Zhao, Advik Mehta, Falak Sethi, Brian Gue, et al. Construction safety management still struggles to produce daily safety bulletins (DSBs), structured daily communications that identify site-specific hazards, convey safety protocols, and set preventive measures for workers. Manual DSB creation takes 2-3 hours daily and yields inconsistent quality, with 25% of crews nev…https://arxiv.org/abs/2610.36335https://arxiv.org/abs/2610.36335Mon, 26 Oct 2026 00:00:00 +0000Hui Yang, Jiaoyan Chen, Yongsheng Gao Large language models (LLMs) perform well across many real-world tasks but remain prone to hallucination, limiting their reliability in domains such as biomedicine and healthcare, where answers must be grounded in established knowledge. Retrieval-augmented generation (RAG) mitigates this problem by incorporating extern…https://arxiv.org/abs/2610.35412https://arxiv.org/abs/2610.35412Mon, 26 Oct 2026 00:00:00 +0000Jiate Liu, Liuyi Chen, Xiaofei Yuan, Yifu Tang, et al. Graph-based retrieval-augmented generation (GraphRAG) answers multi-hop questions by organizing a corpus into a graph and retrieving along its structure. Existing retrieval methods face two limitations. Diffusion- and similarity-based methods expand retrieval along fixed edge weights or by similarity to the question, w…https://arxiv.org/abs/2610.35391https://arxiv.org/abs/2610.35391Mon, 26 Oct 2026 00:00:00 +0000Erdal Aydin, Yaman Arkun High-fidelity digital twins in continuous biomanufacturing are often bottlenecked by incomplete kinetic models, while standard data-driven algorithms (e.g., SINDy, PINNs) lack interpretability or impose rigid physical assumptions. This paper introduces a hybrid closed-loop architecture for autonomous kinetic state-spac…https://arxiv.org/abs/2610.35240https://arxiv.org/abs/2610.35240Mon, 26 Oct 2026 00:00:00 +0000Radha Krishna Deshpande, Ajoy Mondal, Akash Manna, C V Jawahar India's historical public archives present severe computer vision and document image analysis challenges: decades of parliamentary records survive only as degraded, multi-column scanned pages featuring mixed scripts, complex layouts, and dense visual noise where OCR failure is the primary obstacle to information retrie…https://arxiv.org/abs/2610.35142https://arxiv.org/abs/2610.35142Mon, 26 Oct 2026 00:00:00 +0000Qianchi Zhang, Hainan Zhang, Liang Pang, Hongwei Zheng, et al. Large language models (LLMs) often produce plausible but incorrect answers, motivating reinforcement learning methods that reward correct answers, penalize incorrect answers, and allow abstention. However, fixed outcome-based rewards do not distinguish incorrect responses with different confidence levels or account for…https://arxiv.org/abs/2610.35056https://arxiv.org/abs/2610.35056Mon, 26 Oct 2026 00:00:00 +0000Runsong Jia, Qian Chang, Mengjia Wu, Ciprian Doru Giurcăneanu, et al. · EMNLP 2026 Findings Retrieval-augmented generation (RAG) grounds language models in retrieved evidence, but its outputs often contain claims unsupported by that evidence. Recent multi-agent RAG systems decompose the pipeline into cooperative decisions, yet they typically optimize all agents under a shared task reward such as EM or F1, whi…https://arxiv.org/abs/2610.34872https://arxiv.org/abs/2610.34872Mon, 26 Oct 2026 00:00:00 +0000Muhammad Aimal Rehman, Chi-Kuang Yeh Retrieval-augmented generation (RAG) can improve factual grounding without guaranteeing that every generated claim is supported. Previous work has studied claim-level conformal factuality in single-stage RAG and, more recently, multi-hop RAG. We examine its behavior in agentic RAG, where a language-model policy adaptiv…https://arxiv.org/abs/2610.34585https://arxiv.org/abs/2610.34585Sun, 25 Oct 2026 00:00:00 +0000Bahaeddin Türkoğlu Large language models (LLMs) are neural networks trained on huge text collections to generate text by predicting what comes next. Engineers using ChatGPT and other generative artificial intelligence (AI) tools need a clear picture of what training changes and prompts add. The article starts with the Transformer, which …https://arxiv.org/abs/2610.33279https://arxiv.org/abs/2610.33279Fri, 23 Oct 2026 00:00:00 +0000Daisy Rae Lopez, Angela Boccia, Maurizio Sessa Introduction: Increasing volumes of individual case safety reports (ICSRs) challenge expert-led causality assessment in pharmacovigilance. We developed MARCA (Multi-Agent Retrieval-Augmented Causality Assessment), a four-agent framework for Naranjo-based assessment, and evaluated agreement with human experts and explor…https://arxiv.org/abs/2610.32851https://arxiv.org/abs/2610.32851Fri, 23 Oct 2026 00:00:00 +0000Garima Agrawal, Prasun Das, Akshay Sarvade, Priyanka L, et al. Enterprises are deploying agentic AI across the software development lifecycle, and they need the behavioral logic of the systems they act on, not stakeholder documentation. A seven-agent reverse-engineering pipeline recovered this logic, processing 241 repositories across 18 services into thousands of artifacts descri…https://arxiv.org/abs/2610.32435https://arxiv.org/abs/2610.32435Fri, 23 Oct 2026 00:00:00 +0000Juan Li, Fangshu Chang, Xinyi Jiang, Ling Huang, et al. Building Task-Oriented Dialogue (TOD) systems for high-value scenarios such as financial marketing requires large volumes of domain-specific dialogue data, which privacy regulations, annotation costs, and the need for expert labor make hard to obtain. User simulation offers a remedy, but existing methods target general…https://arxiv.org/abs/2610.30755https://arxiv.org/abs/2610.30755Thu, 22 Oct 2026 00:00:00 +0000Zhengye Bao, Shengtao Zhang, Haokai Zhang, Shiqi Lou, et al. LLM memory is widely used in long-term conversation understanding. Agentic memory systems often use mutation, structural organization, learned retrieval strategies, or other complex operations. Is this complexity always necessary? We study Raw-RAG, a naive RAG baseline using dense retrieval and reranking over raw histo…https://arxiv.org/abs/2610.31703https://arxiv.org/abs/2610.31703Wed, 21 Oct 2026 00:00:00 +0000Elham J. Barezi, Primus C Kabuo, Niveditha Nerella Most questions that reach an HPC help desk have already been answered in the center's user guides or in a ticket someone else filed last semester; users still file new tickets because the guides are hard to navigate and the ticket history is not theirs to search. We present an assistant that answers from both sources: …https://arxiv.org/abs/2610.29933https://arxiv.org/abs/2610.29933Wed, 21 Oct 2026 00:00:00 +0000Yufeng Li, Zaijing Li, Fuchen Zhang, Zongzong Wu, et al. · EMNLP 2026 Findings Retrieval-augmented generation (RAG) frequently integrates knowledge graphs (KGs) for complex multi-hop question answering. However, their performance is increasingly bottlenecked by the knowledge structuring and reasoning processes. (i) Severe semantic loss: Distilling continuous texts into discrete symbols strips awa…https://arxiv.org/abs/2610.30167https://arxiv.org/abs/2610.30167Fri, 16 Oct 2026 00:00:00 +0000Maria Ana Cardei, Aniket Singh, Amir Moghadam, Srividhya Baikadi, et al. Optimizing Information Retrieval (IR) pipelines for Retrieval-Augmented Generation (RAG) requires jointly selecting structural components and numerical hyperparameters under limited evaluation budgets. We introduce the Information Retrieval Agentic Harness (IRAH), which combines Language Agent Tree Search for IR (LATS-…https://arxiv.org/abs/2610.21900https://arxiv.org/abs/2610.21900Thu, 15 Oct 2026 00:00:00 +0000Pengfeng Song, Yunkai Zhuang, Liye Shi An agent's memory must remain useful as information changes across conversations. A new fact may replace an earlier value without making that value irrelevant to a later question. We present Temporal State Tracking Memory (TEM), a training-free framework for tracking these changes. TEM stores facts as versioned records…https://arxiv.org/abs/2610.21184https://arxiv.org/abs/2610.21184Thu, 15 Oct 2026 00:00:00 +0000Jiaqi Wei, Wenxuan Huang, Chengdong Xu, Xiang Zhang, et al. Multi-hop retrieval-augmented generation (RAG) requires an agent to retrieve evidence for a sequence of dependent sub-questions. When a wrong answer lacks a required passage, the passage may be inaccessible to the retriever, or the agent may have failed to issue a query that would retrieve it. Standard retrieval evalua…https://arxiv.org/abs/2610.19528https://arxiv.org/abs/2610.19528Wed, 14 Oct 2026 00:00:00 +0000Shijun Li, Yang Liu, Najmeh Sadoughi, Dongmei Wang, et al. Retrieving external knowledge can improve large language model (LLM) translation by providing supplementary information such as idiom interpretations and terminology. However, existing retrieval-augmented machine translation systems typically apply predefined retrieval strategies uniformly across all inputs. We argue t…https://arxiv.org/abs/2610.19398https://arxiv.org/abs/2610.19398Wed, 14 Oct 2026 00:00:00 +0000Linyao Zheng, Xuhang Shi, Zhifang Mao, Xiuquan Hou, et al. Iterative retrieval-augmented generation (RAG) accumulates knowledge from retrieved evidence to answer complex questions, but existing methods struggle to reconstruct entity correspondences and inferential dependencies across retrieved passages as new information emerges. We propose RCER, an iterative RAG method that r…