https://ym-xu.github.io/living-survey/fields/rag/#tag=generalEnd-to-end RAG pipelines and domain systems — the catch-all.Thu, 12 Nov 2026 15:12:01 +0000https://arxiv.org/abs/2611.14355https://arxiv.org/abs/2611.14355Tue, 10 Nov 2026 00:00:00 +0000S M Rayeed, Charles V. Stewart, Yao Ma Retrieval-augmented systems often retrieve passages that are accurate and relevant but state the answer at a coarser level of granularity than the question requires. For example, a question may ask which specific disease a drug treats (familial hyperlipidemia), while the retrieved passage mentions the broader disease f…https://arxiv.org/abs/2611.14263https://arxiv.org/abs/2611.14263Tue, 10 Nov 2026 00:00:00 +0000Wenqi Zhou, Leonardo Baldo, Kai Goebel, Manuel Arias Chao Fault diagnosis in complex systems requires linking observed changes in physical quantities to the failure mechanisms that produce them. Experienced engineers perform this task by combining sensor information with knowledge of system behavior and fault propagation. Large Language Models (LLMs) can support this process,…https://arxiv.org/abs/2611.13675https://arxiv.org/abs/2611.13675Tue, 10 Nov 2026 00:00:00 +0000Senhao Liu, Yuheng Zhang, Chunyu Wei, Yueguo Chen, et al. · NeurIPS 2026 Retrieval-augmented generation (RAG) is bottlenecked by the token budget of large language models: feeding long documents into the context window is expensive, while aggressive textual compression discards fine-grained information that is irreversibly lost in a one-dimensional token sequence. We argue that this bottlen…https://arxiv.org/abs/2611.11960https://arxiv.org/abs/2611.11960Mon, 09 Nov 2026 00:00:00 +0000Guoqing Zhang, Rafik Hadfi, Takayuki Ito Unplanned 30-day intensive care unit (ICU) readmission is a critical benchmark for evaluating healthcare quality. Most predictive models assume readmission risk depends solely on a patient's individual pathophysiological state, ignoring hospital operational context and resource capacity. In practice, discharge decision…https://arxiv.org/abs/2611.11771https://arxiv.org/abs/2611.11771Mon, 09 Nov 2026 00:00:00 +0000Yan Wang, Yuxin Zhang, Shenyu Zhang, Yongrui Chen, et al. Retrieval-Augmented Generation (RAG) systems have demonstrated remarkable effectiveness in mitigating hallucinations by incorporating external knowledge. However, the retrieval process inevitably introduces noise, posing significant challenges to RAG robustness. Fundamentally, noise robustness is a composite competency…https://arxiv.org/abs/2611.11349https://arxiv.org/abs/2611.11349Sun, 08 Nov 2026 00:00:00 +0000Qinjiang Niu, Lu Yan The rapid growth of scientific literature and the high heterogeneity of biological terminology and mechanistic descriptions make it difficult for researchers to integrate scattered research evidence, identify connections among biological events, and construct mechanistic networks. Here, we developed a high-throughput h…https://arxiv.org/abs/2611.11113https://arxiv.org/abs/2611.11113Sun, 08 Nov 2026 00:00:00 +0000Linyao Zheng, Xuhang Shi, Zhifang Mao, Chongqi Qu, et al. Complex Web question answering requires retrieval-augmented generation (RAG) systems to identify target entities from multi-clue descriptions and connect evidence scattered across long Web pages. Individual pages often support only some of the required semantic constraints, while clues discovered on one page guide subs…https://arxiv.org/abs/2611.11010https://arxiv.org/abs/2611.11010Sat, 07 Nov 2026 00:00:00 +0000Uday Allu Graph-based retrieval-augmented generation (GraphRAG) improves multi-hop question answering, but deployed systems face two costs that research comparisons rarely measure: building the graph, and removing documents from it. We present Lightfoot, a GraphRAG system in which every entity, link, fact and synonym edge is a d…https://arxiv.org/abs/2611.10894https://arxiv.org/abs/2611.10894Sat, 07 Nov 2026 00:00:00 +0000Subin Kim, Anh-Quan Pham, Dinesh Jayaraman Pre-training large parametric "Vision-Language-Action" (VLA) models on large demonstration datasets has recently yielded promising results for general-purpose manipulation. However, these models still struggle under even small domain shifts relative to the training data, such as changes in camera viewpoint, background,…https://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/2611.10405https://arxiv.org/abs/2611.10405Sat, 07 Nov 2026 00:00:00 +0000Hongyuan Xu, Dezhi Ye, Jiahao Cao, Jie Liu, et al. High-precision text reranking is a critical component in modern search, recommendation, and agentic retrieval-augmented generation (RAG) systems. We present Yuanbao-Reranker, a family of general-purpose dense rerankers spanning 0.6B, 4B, and 8B parameter scales built upon the Qwen3 backbone. To construct diverse and re…https://arxiv.org/abs/2611.10335https://arxiv.org/abs/2611.10335Sat, 07 Nov 2026 00:00:00 +0000Ben Jenkins, Mihaela Cardei Retrieval-augmented generation (RAG) can surface passages that support a claim, contradict it, or fail to address it, yet existing calibrated grounding methods are typically one-sided and model only supporting evidence. We introduce Conformal Evidence Sets (CES), a two-sided framework that returns separate support and …https://arxiv.org/abs/2611.09866https://arxiv.org/abs/2611.09866Fri, 06 Nov 2026 00:00:00 +0000Rochak Dhakal, Chen Zhao, Soup Cobb, Xin Qian, et al. Purpose: Surgical operative reports support procedural documentation, surgical training, performance assessment, and workflow review. However, extracting meaningful information often requires extensive manual review and clinical expertise. Automated surgical scene descriptions could reduce this burden by leveraging rec…https://arxiv.org/abs/2611.09807https://arxiv.org/abs/2611.09807Fri, 06 Nov 2026 00:00:00 +0000Osamudiamen Edogun, Joshua Obigwe, Irfan Mehmood A conversational agent that remembers has to decide what to keep. Retrieval-augmented generation keeps the transcript and searches it; the alternative is to compact each turn into atomic facts and search those. We compare the two on LoCoMo, with both arms answering all 1,985 questions in one window, against the same st…https://arxiv.org/abs/2611.09791https://arxiv.org/abs/2611.09791Fri, 06 Nov 2026 00:00:00 +0000Gabriel Souza Baggio, Gabriel Machado Lunardi, Guilherme Medeiros Machado, Subhankar Maity, et al. Competitive programming problems pose distinctive challenges for Large Language Models, as they demand not only proficient code generation but also advanced algorithmic reasoning. This study presents a Retrieval-Augmented Generation approach specifically designed to enhance the capacity of Large Language Models to addr…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/2611.09407https://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/2611.09298https://arxiv.org/abs/2611.09298Fri, 06 Nov 2026 00:00:00 +0000Oliver Noll, Nicholas Tan Jerome, Alexander Lehnen, Frank Simon, et al. Long-term particle physics experiments face critical operational challenges: monitoring non-stationary machine data streams and preserving tacit expert knowledge. We present the "Helmholtz Co-Pilot," a hybrid Multi-Agent System (MAS) acting as an autonomous experiment steward. The system can be triggered interactively …https://arxiv.org/abs/2611.09232https://arxiv.org/abs/2611.09232Fri, 06 Nov 2026 00:00:00 +0000Lorenzo Mannocci, Francesca Naretto, Lucia Passaro, Anna Monreale Artificial Intelligence (AI) is increasingly embedded in high-risks decision-making systems across domains such as healthcare, finance, and the judicial system. As reliance on predictive models grows, so does the demand for Explainable AI (XAI) to ensure transparency, trust, and interpretability. However, existing XAI …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/2611.07928https://arxiv.org/abs/2611.07928Thu, 05 Nov 2026 00:00:00 +0000Petru Neague, Quinten Stokkink, Martijn de Vos, Rafael Pires, et al. Retrieval-augmented generation (RAG) is a standard approach to enhance the reliability of large language model (LLM) outputs by retrieving documents relevant to a query and supplying them in the LLM prompt. Today's RAG systems collect, index, and serve these documents from centralized stores. In practice, however, docu…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/2611.06715https://arxiv.org/abs/2611.06715Wed, 04 Nov 2026 00:00:00 +0000Zohreh Abbasi Liasi, Batoul Hammoud, Anas Madkoor, Ehsaneddin Asgari Retrieval-augmented generation (RAG) assumes that language models can reason over relations stated in their context rather than fall back on associations memorized during pretraining. We study this evidence-conditioned relational reasoning in isolation, with the facts supplied directly in the prompt rather than retriev…https://arxiv.org/abs/2611.06498https://arxiv.org/abs/2611.06498Wed, 04 Nov 2026 00:00:00 +0000Georgios Feretzakis, Alexandros Papaspyridis Offline retrieval-augmented generation (RAG) on edge devices is updated through signed knowledge packs that carry text and precomputed embedding vectors. A signature proves who published a pack, not that its vectors encode its text. We study this text-vector integrity gap. A verifier that recomputes vectors and accepts…https://arxiv.org/abs/2611.06170https://arxiv.org/abs/2611.06170Wed, 04 Nov 2026 00:00:00 +0000Pragyan Phukan While Large Language Models (LLMs) excel in high-resource languages, their performance often degrades in low-resource languages due to tokenization inefficiencies, generative instability, and linguistic interference. This paper presents AssameseLM-E2B and its instruction-tuned variant, AssameseLM-E2B-it, a 2-billion-pa…https://arxiv.org/abs/2611.05900https://arxiv.org/abs/2611.05900Wed, 04 Nov 2026 00:00:00 +0000Linyao Zheng, Zhifang Mao, Xuhang Shi Personalized retrieval-augmented generation requires extracting and applying user preferences from extended interaction histories. This task is challenging because preference evidence is scattered across past conversations and remains bound to specific temporal and situational contexts. Consequently, isolated fragments…https://arxiv.org/abs/2611.04949https://arxiv.org/abs/2611.04949Tue, 03 Nov 2026 00:00:00 +0000Dayeon Jeong, Kyeongpil Kang · EMNLP 2026 Findings Historical archives are invaluable for multidisciplinary research but remain inaccessible globally due to severe language barriers. While manual translation is prohibitively expensive and time-consuming, simply applying existing machine translation models is inadequate due to unique linguistic nuances and the temporal …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/2611.04264https://arxiv.org/abs/2611.04264Tue, 03 Nov 2026 00:00:00 +0000Janet Sung, Kainan Zhou, Gangzhen Qian, Hang Xiao, et al. External text can affect a retrieval-augmented generation (RAG) system only after the retriever admits it to the model context. We stress this admission step while varying document content, attacker query information, per-target poison budget, and contamination scope. Across 623 Benchmarking Information Retrieval (BEIR…https://arxiv.org/abs/2611.03747https://arxiv.org/abs/2611.03747Mon, 02 Nov 2026 00:00:00 +0000Huan Phong Thai, Thu Anh Ngoc Le, Nhu Huynh Thi Le, Nhan Huu Tran, et al. Natural Language Processing (NLP) has been widely applied in financial domains, enabling automated analysis and retrieval of stock market information. However, traditional NLP-based systems often struggle with maintaining contextual accuracy and generating relevant responses in dynamic financial environments. To addres…https://arxiv.org/abs/2611.03744https://arxiv.org/abs/2611.03744Mon, 02 Nov 2026 00:00:00 +0000Mehrnoush Mohammadi, Kamal Berahmand, Mohammad Morsali, Shazia Sadiq, et al. Knowledge tracing (KT) estimates learners' evolving knowledge states from interaction histories to support adaptive learning. Retrieval-augmented KT improves these estimates using cross-learner behavioral evidence, but existing approaches typically separate evidence retrieval from downstream prediction. This creates a …https://arxiv.org/abs/2611.02883https://arxiv.org/abs/2611.02883Mon, 02 Nov 2026 00:00:00 +0000Zuguang He, Congcong Zheng, Wanyue Chen, Wenya Zhou, et al. Reclamation of saline-alkali land requires management plans that account for soil and water conditions, treatment evidence, and financial constraints. This study develops Wenyao, an agricultural decision-support system integrating domain adaptation with retrieval-augmented generation. A bilingual corpus of 8,664 resear…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/2611.02387https://arxiv.org/abs/2611.02387Mon, 02 Nov 2026 00:00:00 +0000Yibo Wang, Jisheng Dang, Bimei Wang, Jiongpei Hou, et al. Retrieval-augmented generation (RAG) extends large language models with external evidence, yet relevant evidence does not ensure that a generator can use it. Indiscriminate retrieval can degrade task performance while adding retrieval and long-context inference costs. Existing methods neither determine before large-sca…https://arxiv.org/abs/2611.03725https://arxiv.org/abs/2611.03725Sun, 01 Nov 2026 00:00:00 +0000Hira Naveed, Scott Barnett, Stefanus Kurniawan, John Grundy, et al. Retrieval-Augmented Generation (RAG) pipelines are widely used for domain-specific question answering, yet locating faults remains manual and time-consuming. Fault localization is challenging because RAG pipelines consist of interdependent stages, where faults can propagate downstream, masking their origin, or remain u…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/2611.01409https://arxiv.org/abs/2611.01409Sun, 01 Nov 2026 00:00:00 +0000Elyas Irankhah, Shahed Salehzehi, Parisa Hosseini Purpose: Retrieval-augmented generation (RAG) can introduce electrocardiogram (ECG) criteria that lack patient-specific measurement support. This study evaluates the reliability of retrieved diagnostic criteria in AI-generated ECG reports. Methods: We analyzed 1,083 human-validated PTB-XL patient records using a librar…https://arxiv.org/abs/2611.00871https://arxiv.org/abs/2611.00871Sat, 31 Oct 2026 00:00:00 +0000Youmin Ko, Jihong Jeong, Hyunjoon Kim · NeurIPS 2026 Despite advances in retrieval-augmented generation (RAG), suppressing hallucinations during complex reasoning remains a persistent challenge. We identify that existing RAG pipelines suffer from a textual bottleneck: by passing only raw text to the generator, they discard discriminative signals of the retriever. Further…