arXiv AI July 1, 2026 By Shivam Ratnakar, Yixuan Zhu, Cecilia Cheng, Chaya Vijayakumar

One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG

arXiv:2606. 31156v1 Announce Type: cross Abstract: RAG systems retrieve documents optimized for answering one query at a time.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 4

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

arXiv:2606. 04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text.

By Mengao Zhang, Xiang Yang, Chang Liu, Tianhui Tan, Ke-wei Huang

llms rag nlp benchmarks data-engineering

arXiv AI

Jul 31

GuidedRAG: Semantic Steering of Retrieval-Augmented Generation

arXiv:2607. 26071v1 Announce Type: cross Abstract: In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a dedicated selection stage and semantic steering during retrieval.

By Matthijs Jansen op de Haar, Tobias St\"ahle, Lorenzo Gatti

rag benchmarks safety

arXiv AI

Aug 11

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge

arXiv:2608. 07994v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is essential for enterprise knowledge question answering (QA), particularly in domains with complex product documentation like telecommunications.

By Wenqi Chen, Haofei Yang, Rui Yang, Fangming Li

rag agents nlp

arXiv AI

Jul 22

M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation

arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.

By Xu Sun, Tongkai Xu, Baiheng Xie, Li Huang, Qiang Gao, Kunpeng Zhang

llms rag

arXiv AI

Jun 2

SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval

arXiv:2606. 00822v1 Announce Type: cross Abstract: Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to execution.

By Zicai Cui, Zihan Guo, Weiwen Liu, Weinan Zhang

llms agents benchmarks

arXiv AI

22h ago

DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval

arXiv:2608. 17632v1 Announce Type: cross Abstract: Large language models (LLMs) can both expand underspecified queries and encode text as dense representations, suggesting a unified model for query expansion and retrieval.

By Jingyuan Wang, Richong Zhang, Zhijie Nie, Mingxin Li, Yanzhao Zhang

llms rag benchmarks

Related stories

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

GuidedRAG: Semantic Steering of Retrieval-Augmented Generation

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge

M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation

SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval

DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval