Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv AI

Jun 9

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

arXiv:2605. 17301v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice.

By Chenyu Wang, Yueyuan Li, Yingmin Liu, Yang Shu

llms rag benchmarks

arXiv AI

Jun 9

Larch: Learned Query Optimization for Semantic Predicates

arXiv:2606. 07923v1 Announce Type: cross Abstract: With the advent of Large Language Models (LLMs), many database systems introduced semantic operators that enabled analytical queries over unstructured data (e.

By Fuheng Zhao, Pawel Liskowski, Zihan Li, Benjamin Han, Puxuan Yu, Varich Boonsanong, Dimitris Tsirogiannis, Anupam Datta

llms rag reinforcement-learning data-engineering

arXiv Machine Learning

Jun 9

AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction

arXiv:2606. 08161v1 Announce Type: new Abstract: As capacitance extraction accuracy of rule-based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep-learning-based 2D capacitance models.

By Jiechen Huang, Hector R. Rodriguez, Dingcheng Yang, Zuochang Ye, Yibo Lin, Wenjian Yu

llms rag fine-tuning efficiency

arXiv AI

Jun 9

A multi-agent system for spine MRI report generation from multi-sequence imaging

arXiv:2606. 08897v1 Announce Type: cross Abstract: Spinal pathology is a leading cause of pain and disability worldwide.

By Zhiping Xiao, Junwei Yang, Gongbo Sun, Han Zhang, Hanwen Xu, Yi Yao, Zachary D. Miller, William E. King III, Mohammed M. Kanani, Jalal B. Andre, Sammy Chu, Ming Zhang, Paul E. Kinahan, Nathan M. Cross, Sheng Wang

rag agents multimodal benchmarks

arXiv AI

Jun 9

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation

arXiv:2606. 07526v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities.

By Lin Mu, Guoji Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

llms rag benchmarks

arXiv Machine Learning

Jun 9

Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings

arXiv:2602. 18364v2 Announce Type: replace-cross Abstract: Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models.

By Sreejith Sreekumar, Nir Weinberger

llms rag

arXiv Machine Learning

Jun 9

Learning to Optimize by Differentiable Programming

arXiv:2601. 16510v3 Announce Type: replace-cross Abstract: Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost.

By Liping Tao, Xindi Tong, Chee Wei Tan

rag

arXiv Machine Learning

Jun 9

Public Machine Learning Solver Framework for Novices in the Machine Learning Domain

arXiv:2606. 08212v1 Announce Type: new Abstract: Solving machine learning problems is complex and typically reserved for experts.

By Lokman Saleh, Hafedh Mili, Mounir Boukadoum

rag fine-tuning

arXiv AI

Jun 9

ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding

arXiv:2606. 07524v1 Announce Type: cross Abstract: The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for provenance auditing, security analysis, and model selection.

By Zirui Wang, Yusen Hou, Shaofeng Liang, Bowen Tian, Yanlin Zhang, Wenshuo Chen, Yutao Yue

llms rag nlp benchmarks safety

arXiv AI

Jun 9

BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

arXiv:2606. 07528v1 Announce Type: cross Abstract: Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment.

By Naveen Bera, Pulijala Sai Nikhila, Kondaguduru Abhiram, Shaik Gayaz Ali, Shoaib Sadiq Salehmohamed, Shaik Mohammed Omar, Jinal Prashant Thakkar, Hansika Aredla, Shalmali Ayachit

llms rag benchmarks safety

arXiv AI

Jun 9

Kernel Affine Hull Machines as Compute-Efficient Encoders for Frozen Semantic Spaces

arXiv:2605. 02950v2 Announce Type: replace-cross Abstract: Transformer-based semantic encoders are effective for retrieval, but in many deployments the recurring bottleneck is online query encoding rather than offline corpus indexing.

By Mohit Kumar, Somayeh Kargaran, Bernhard A. Moser, Manuela Gei{\ss}

llms rag benchmarks

arXiv AI

Jun 9

Decoupling Semantics and Logic: A Training-Free Coarse-to-Fine Pipeline for Video Retrieval-Augmented Generation

arXiv:2606. 07924v1 Announce Type: cross Abstract: This paper presents our system description for the 2nd Workshop on Multimodal Augmented Generation via MultimodAl Retrieval (MAGMaR).

By Jiaxin Dai, Zehang Wei, Jiamin Yan, Xiang Xiang

llms rag agents computer-vision efficiency multimodal safety

arXiv AI

Jun 9

Projection and Quantisation: A Unifying View of Learning to Hash, from Random Projections to the RAG Era

arXiv:2510. 04127v2 Announce Type: replace-cross Abstract: Approximate nearest neighbour (ANN) search underpins large-scale retrieval, increasingly within the retrieval-augmented generation pipelines that ground large language models, yet the methods that address it have multiplied across communities until they are seldom read as a single field.

By Sean Moran

llms rag benchmarks

arXiv AI

Jun 9

Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions

arXiv:2604. 08304v3 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.

By Yuming Xu, Mingtao Zhang, Zhuohan Ge, Haoyang Li, Nicole Hu, Yongqi Zhang, Zhiyuan Wen, Jason Chen Zhang, Qing Li, Lei Chen

llms rag agents robotics multimodal

arXiv Machine Learning

Jun 9

The Injection Paradox: Brand-Level Suppression in Safety-Trained LLM Recommendations via RAG Context Injection

arXiv:2606. 09204v1 Announce Type: new Abstract: We present a reproducible failure mode of safety training in RAG-based LLM recommendation -- the Injection Paradox -- in which prompt injections embedded in retrieved documents backfire against the attacker, suppressing the target brand below the injection-free baseline.

By Hyunseok Paeng

llms rag safety

arXiv AI

Jun 9

Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting

arXiv:2606. 08630v1 Announce Type: cross Abstract: Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates.

By Jiahui Huang, Ao Luo, Lei Liu, Hongwei Zhao, Tengyuan Liu, Ruibo Guo, Bo Wang, Zhao Wang, Bin Li

rag

arXiv AI

Jun 9

Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering

arXiv:2606. 07523v1 Announce Type: cross Abstract: Legal domains in high-resource languages like English have widely adopted artificial intelligence for legal question answering.

By Samir Wagle, Abiral Adhikari, Reewaj Khanal, Batsal Bhandari, Prashant Manandhar, Praveen Acharya, Bal Krishna Bal

llms rag nlp

arXiv AI

Jun 9

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

arXiv:2605. 12213v2 Announce Type: replace Abstract: LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context.

By Jiazhou Liang, Armin Toroghi, Yifan Simon Liu, Faeze Moradi Kalarde, Liam Gallagher, Scott Sanner

llms rag agents

arXiv Machine Learning

Jun 9

LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization

arXiv:2606. 08993v1 Announce Type: new Abstract: We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization.

By Binh Nguyen, Trinh Tran, Truong X. Nghiem

rag benchmarks

arXiv AI

Jun 9

GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model

arXiv:2512. 20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and high-fidelity speech.

By Haoyang Li, Xuyi Zhuang, Azmat Adnan, Ye Ni, Wei Rao, Shreyas Gopal, Eng Siong Chng, Boon Siew Han, Yuanjin Zheng

llms rag reinforcement-learning safety

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