New RAG techniques tackle hallucinations and improve efficiency
ByPulseAugur Editorial·[54 sources]·
Researchers are developing new methods to improve Retrieval-Augmented Generation (RAG) systems, which ground large language models with external evidence. Several papers introduce novel techniques to address issues like hallucinations, irrelevant information retrieval, and inefficient processing. These advancements include graph-based expert mixtures, structured critic frameworks for error correction, and mindscape-aware approaches for better long-context understanding. Additionally, new benchmarks are being created to evaluate RAG performance in specialized domains like Canadian law, and methods for quantifying uncertainty in multimodal RAG are being explored.
AI
IMPACT
Advances in RAG aim to reduce hallucinations and improve reasoning, leading to more reliable AI systems across various applications.
RANK_REASON
Multiple research papers introducing new methods and benchmarks for Retrieval-Augmented Generation systems.
Multimodal generative retrieval formulates multimodal retrieval as discrete identifier generation, eliminating the need for explicit similarity search over external embeddings. Existing approaches construct identifiers via residual quantization and decode them with trie-constrain…
Multimodal generative retrieval formulates multimodal retrieval as discrete identifier generation, eliminating the need for explicit similarity search over external embeddings. Existing approaches construct identifiers via residual quantization and decode them with trie-constrain…
arXiv cs.AI
TIER_1English(EN)·Hiren Madhu, Ngoc Bui, Ali Maatouk, Leandros Tassiulas, Smita Krishnaswamy, Menglin Yang, Sukanta Ganguly, Kiran Srinivasan, Rex Ying·
arXiv:2602.07739v2 Announce Type: replace-cross Abstract: Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space. However, natural language exhibits hierarchical st…
In recommender systems, generative retrieval typically uses an encoder-decoder setup: an encoder processes a user interaction history, and an autoregressive decoder then generates recommended items. In large-scale streaming services, active users accumulate very long histories ov…
In recommender systems, generative retrieval typically uses an encoder-decoder setup: an encoder processes a user interaction history, and an autoregressive decoder then generates recommended items. In large-scale streaming services, active users accumulate very long histories ov…
arXiv:2604.12138v2 Announce Type: replace-cross Abstract: This position paper argues that Retrieval-Augmented Generation systems exhibit a systematic factual bias-optimizing for epistemic uncertainty reduction while ignoring the aleatoric uncertainty inherent in opinion-rich cont…
arXiv:2606.04231v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend ofte…
arXiv cs.CL
TIER_1English(EN)·Zhichao Xu, Zongyu Wu, Yun Zhou, Aosong Feng, Kang Zhou, Sangmin Woo, Kiran Ramnath, Yijun Tian, Xuan Qi, Weikang Qiu, Lin Lee Cheong, Haibo Ding·
arXiv:2510.13272v3 Announce Type: replace Abstract: Inspired by the success of reinforcement learning (RL) in Large Language Model (LLM) training for domains like math and code, recent work has begun training LLMs to dynamically plan, query, and reason with search engines as tool…
arXiv:2606.04302v1 Announce Type: new Abstract: Key-value (KV) caching accelerates inference of large language models (LLMs) by reusing past computations for generated tokens. Its importance becomes even greater in long-context applications such as retrieval-augmented generation …
arXiv cs.CL
TIER_1English(EN)·Mingyan Wu, Han Yang, Omer Ben-Porat, Yftah Ziser·
arXiv:2606.02245v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) typically assumes that external knowledge is free, but many high-quality sources are paywalled, licensed, restricted, or otherwise costly to access. We introduce cost-aware RAG, a setting where r…
arXiv cs.CL
TIER_1English(EN)·Mateusz \'Smigielski (Department of Artificial Intelligence, Faculty of Information and Communication Technology, Wroc{\l}aw University of Science and Technology, Wroc{\l}aw 50-370, Poland), Micha{\l} Rajkowski (Department of Artificial Intelligence, Fac…·
arXiv:2606.00881v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has demonstrated significant capabilities in enhancing the performance of Large Language Models (LLMs). One of the key tasks in RAG systems is the chunking process. Traditionally, fixed-size chun…
arXiv cs.CL
TIER_1English(EN)·Alexander Martin, William Walden, Reno Kriz, Dengjia Zhang, Kate Sanders, Eugene Yang, Chihsheng Jin, Benjamin Van Durme·
arXiv:2510.24870v2 Announce Type: replace Abstract: We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a prevalent source of information online, it is essential for RAG systems to integrate i…
Retrieval-Augmented Generation (RAG) typically assumes that external knowledge is free, but many high-quality sources are paywalled, licensed, restricted, or otherwise costly to access. We introduce cost-aware RAG, a setting where retrieved evidence is assigned access-cost tiers …
arXiv:2411.19463v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, practitioners face significant challenges when making RAG deployment decisions. While…
arXiv cs.CL
TIER_1English(EN)·Zheng Yuan, Chuang Zhou, Linhao Luo, Siyu An, Di Yin, Xing Sun, Xiao Huang·
arXiv:2605.31010v1 Announce Type: new Abstract: Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably introduce irrelevant information that may mislead generat…
arXiv:2605.30497v1 Announce Type: new Abstract: RAG-based legal assistants have been growing in popularity, but LLM hallucinations remain a key issue and potentially undermines justice. While benchmarks have been developed to evaluate progress, many rely on synthetic queries rath…
Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably introduce irrelevant information that may mislead generation for complex reasoning. Inspired by the condi…
arXiv:2603.26668v2 Announce Type: replace-cross Abstract: As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This p…
arXiv cs.AI
TIER_1English(EN)·Gyumin Kim, Juhwan Park, Jaeha Kim, Seunggyun Han, Kyungrak Son, Ikbeom Jang·
arXiv:2605.28837v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have demonstrated remarkable capabilities, their reliability is significantly compromised by hallucinations. Existing intrinsic self-correction methods attempt to address this, but often fail due…
arXiv:2512.17220v2 Announce Type: replace Abstract: Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior knowledge, interpret new information, and integrate evidence dispersed across a docu…
Retrieval Augmented Generation (RAG) improves the question answering capabilities of Large Language Models (LLMs) by incorporating external knowledge and has recently been extended to multimodal settings through Vision-Language Models (VLMs) that integrate visual and textual info…
Multi-modal Retrieval-Augmented Generation (MMRAG) has emerged as a powerful paradigm for enhancing Multimodal Large Language Models in knowledge-intensive question answering by integrating external visual, textual, and structural knowledge. However, existing MMRAG frameworks suf…
arXiv cs.AI
TIER_1English(EN)·Larissa Guder, Jo\~ao Pedro de Moura, Arthur Accorsi, Gustavo Losch do Amaral, Maur\'icio Cec\'ilio Magnaguagno, Felipe Meneguzzi, Marcio Sorraglia Pinho, Dalvan Griebler·
arXiv:2605.27445v1 Announce Type: cross Abstract: Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually selec…
arXiv cs.AI
TIER_1English(EN)·Syed Huma Shah (Duke University)·
arXiv:2605.27494v1 Announce Type: cross Abstract: Modern retrieval-augmented generation(RAG) deployments increasingly rely on caching to reduce token cost and time-to-first-token(TTFT). Prefix-level KV reuse is now standard in serving stacks such as vLLM, and chunk-level and posi…
arXiv:2604.08304v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but this access path also introduces security risks that existing work often conflates with inherent LLM flaws. We frame se…
arXiv cs.AI
TIER_1English(EN)·Ruben Belo, Marta Guimar\~aes, Cl\'audia Soares·
arXiv:2605.27444v1 Announce Type: cross Abstract: The rapid expansion of space activities has led to an unprecedented accumulation of technical documentation, operational guidelines, and scientific literature, creating challenges for timely decision-making in space operations. Ef…
arXiv cs.AI
TIER_1English(EN)·Tianhao Gao, Kai Yang, Yiyang Li·
arXiv:2605.27432v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has emerged as a paradigm for grounding large language models in external knowledge, yet most existing RAG systems assume centralized knowledge access and ample computation. These assumptions b…
arXiv cs.AI
TIER_1English(EN)·Tetsuya Sakai, Jina Lee, Hanpei Fang, Young-In Song·
arXiv:2605.26400v1 Announce Type: cross Abstract: We propose a framework for evaluating structured generative search summaries that are placed atop organic web search results. A structured summary, generated by a large language model, typically consists of an overview, several se…
arXiv cs.AI
TIER_1English(EN)·Yu-Chen Den, Yung-Yu Shih, Zhi Rui Tam, Kuan-Yu Chen, Pu-Jen Cheng, Yun-Nung Chen, Eugene Yang·
arXiv:2605.26902v1 Announce Type: cross Abstract: Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge, However, this design makes corpus expansion costly: adding new documents requires updating model parameters to encode new…
arXiv:2605.26356v1 Announce Type: new Abstract: In-context learning has recently been linked to implicit gradient descent in linear self-attention models, suggesting that context can induce a forward-pass update. Retrieval-augmented generation (RAG) also relies on context, but re…
arXiv cs.AI
TIER_1English(EN)·Zhe Yu, Wenpeng Xing, Chen Ye, Xuyang Teng, Bo Yang, Changting Lin, Meng Han·
arXiv:2605.27157v1 Announce Type: new Abstract: Retrieval-augmented LLMs are deployed for tasks where evidence quality determines action safety, yet evaluation protocols assume that single-turn robustness predicts robustness when evidence accumulates across turns. We show this as…
arXiv cs.IR (Information Retrieval)
TIER_1English(EN)·Syed Huma Shah·
Modern retrieval-augmented generation(RAG) deployments increasingly rely on caching to reduce token cost and time-to-first-token(TTFT). Prefix-level KV reuse is now standard in serving stacks such as vLLM, and chunk-level and position-independent reuse have been pushed further by…
Retrieval-augmented LLMs are deployed for tasks where evidence quality determines action safety, yet evaluation protocols assume that single-turn robustness predicts robustness when evidence accumulates across turns. We show this assumption is fundamentally incorrect. Models exhi…
Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge, However, this design makes corpus expansion costly: adding new documents requires updating model parameters to encode new document-docid associations incurs repeated train…
Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge, However, this design makes corpus expansion costly: adding new documents requires updating model parameters to encode new document-docid associations incurs repeated train…
arXiv:2605.25379v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has become the standard way to ground large language models in external knowledge, but many systems still organize evidence as flat chunks and retrieve it through largely unstructured search. Thi…
We propose a framework for evaluating structured generative search summaries that are placed atop organic web search results. A structured summary, generated by a large language model, typically consists of an overview, several sections with section titles, and a list of source d…
Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components. In this work, we pr…
The rapid expansion of space activities has led to an unprecedented accumulation of technical documentation, operational guidelines, and scientific literature, creating challenges for timely decision-making in space operations. Effective management in space operations requires to…
The growing adoption of Retrieval-Augmented Generation (RAG) has led to a rise in adversarial attacks. Existing defenses, relying on semantic analysis or voting, face a trade-off between high computational cost and limited robustness under strong poisoning attacks. Their fundamen…
Retrieval-Augmented Generation (RAG) improves reliability of large language models by incorporating external knowledge, but the retrieval process can introduce bias that propagates to generated outputs. This issue is particularly challenging in top-k settings, where multiple docu…
arXiv:2605.29602v1 Announce Type: new Abstract: Multi-modal Retrieval-Augmented Generation (MMRAG) has emerged as a powerful paradigm for enhancing Multimodal Large Language Models in knowledge-intensive question answering by integrating external visual, textual, and structural k…
arXiv:2605.25039v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong performance in natural language processing but often generate factual errors when relying solely on parametric knowledge. Retrieval-Augmented Generation (RAG) mitigates these errors by…
دليل RAG Cookbook من IBM يفتح باب بناء نماذج توليد مدعومة بالاسترجاع بسهولة. 🔹 **نقطة أساسية:** دمج قاعدة معرفة محلية مع نموذج توليد يرفع دقة الردود حتى على بيانات ضخمة. 🔹 خطوات سريعة: - تجهيز البيانات وبناء ال Knowledge Base - إعداد النموذج وتدريبه - اختبار الأداء وتطبيقات واقعي…
دليل IBM الجديد "RAG Cookbook" يشرح كيف نبني أنظمة استرجاع معتمد على نماذج اللغة الكبيرة. 🔹 **مفتاح النجاح:** دمج محرك بحث محلي مع نموذج LLM يرفع دقة النتائج ويقلل زمن الاستجابة، مع الحفاظ على سرية البيانات. 🔹 مثال عملي: تخزين embeddings محليًا ثم طلب توليد إجابات من نموذج AI فقط…
تحدّث IBM عن أحدث نسخة من كتيب “RAG Cookbook” - دليل عملي لكيفية دمج Retrieval‑Augmented Generation لتخزين واسترجاع البيانات في نماذج الذكاء الاصطناعي. 🔑 أهم نقطة: إن أمثلة عليه تُظهر خطوة بخطوة كيف تُحسّن البنية التحتية، ومحركات البحث، وإعدادات الأبعاد لتحسين دقة الاستجابة مع ال…
"BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A" BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system for biomedical question answering. Paper: https:// arxiv.or…
<p>RAG sounds complicated.</p> <p>It's not.</p> <p>But a lot of introductions to RAG make it sound more mysterious than it actually is. They use terms like "semantic search" and "vector embeddings" and "retrieval pipeline" before explaining what the actual problem is.</p> <p>So l…
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