arXiv AI

Hybrid Retrieval-Augmented Generation with Knowledge Graph Expansion, RRF Fusion, and Per-Chunk Grounded Evaluation for Enterprise Document Search

Hybrid Retrieval-Augmented Generation with Knowledge Graph Expansion, RRF Fusion, and Per-Chunk Grounded Evaluation for Enterprise Document Search describes DocuSearch, an offline multi‑agent system designed for telecom network operations. The system combines semantic vector search, BM25 full‑text search, and knowledge‑graph neighbor expansion, merges the results via Reciprocal Rank Fusion, and reranks with a cross‑encoder before pruning with Maximal Marginal Relevance. A per‑chunk evaluation loop ensures only grounded answers are returned, achieving Precision@10 of 0.69, Recall@10 of 0.79, and an 89.6% grounding rate—improvements of 15, 16, and 18.4 percentage points over a dense‑only baseline.

arXiv Machine Learning

1d ago

Multi-Agent Retrieval-Augmented Generation for Efficient Cloud Knowledge Base Search in Telecom SNOC Environment

The paper introduces Athena for Cloud Knowledge Base, a fully offline multi‑agent Retrieval Augmented Generation (RAG) system tailored for Vodafone Idea’s Telecom SNOC environment. It combines dense E5 Large V2 embeddings, BM25 sparse retrieval, and Knowledge Graph expansion, fusing results with Weighted CombSUM, cross‑encoder reranking, and MMR to produce a diverse evidence set. The framework further verifies each chunk with an LLM before response generation, discarding weak evidence and falling back to multi‑chunk evaluation when necessary, achieving an MRR@10 of 0.910 and an EM of 78.4% on a 4,200‑document corpus.

By Harish Saragadam, Sudhanshu Sharma, Ipsha Routray

llms rag agents

arXiv AI

Aug 25

From Association to Causation: Improving Retrieval Precision of Retrieval-Augmented Generation via Causal Relations and an Attention Mechanism

arXiv:2608.21702v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds LLM generation on retrieved documents, but the standard terminal retrieval stage--dense-vector similarity,...

By Jing Liu, Yongxing Qi, Muchen Jiang, Chengnan Hu, Qingqing Peng, Haoming Wang, Yuqing Wang, Yang Yu, Xu Zhang, Ting Wu

llms rag

arXiv Computation and Language

Aug 27

Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG

Corpus2Skill is a retrieval architecture that transforms an enterprise knowledge base into a hierarchical skill directory, enabling an LLM agent to navigate from high-level summaries to specific documents and backtrack when necessary. On an enterprise customer‑support benchmark, it outperforms single‑shot dense, hybrid, hierarchical‑retrieval, and agentic RAG baselines in answer quality and grounding, with a moderate cost tradeoff. An eleven‑dataset study shows that corpus navigation excels on single‑domain corpora with a recoverable topical taxonomy but is less effective on open‑domain factoid pools or homogeneous‑tabular corpora, providing a design guideline for knowledge‑grounded systems.

By Yiqun Sun, Pengfei Wei, Lawrence B. Hsieh

llms rag agents benchmarks

arXiv AI

Jun 30

Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?

arXiv:2606. 28367v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is routinely extended with methods meant to improve retrieval: query expansion, hierarchical and cross-document summarization, graph-based expansion, per-query routing, rank fusion, and corrective re-retrieval.

By Sadanand Singh, Allam Reddy, Manan Chopra

rag nlp benchmarks

arXiv AI

Jun 8

Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval

arXiv:2605. 06647v2 Announce Type: replace-cross Abstract: Retrieval-augmented agents are increasingly the interface to large knowledge bases, yet most treat retrieval as a black box: they issue exploratory queries, inspect snippets, and reformulate until evidence emerges.

By Zeyu Yang, Qi Ma, Jason Chen, Anshumali Shrivastava

llms rag agents reinforcement-learning fine-tuning benchmarks

arXiv AI

3d ago

PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation

arXiv:2608.29753v1 Announce Type: new Abstract: Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be rea...

By Haokun Deng, Xunkai Li, Hongchao Qin, Rong-Hua Li

rag nlp benchmarks

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 28

A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility

arXiv:2607. 24663v1 Announce Type: cross Abstract: Scientific user facilities accumulate decades of operational knowledge that no single search index covers: electronic logbooks, technical documents, internal wikis, operations chat messages, maintenance records, and live control-system data.

By Rajat Sainju, Dariusz Jarosz, Hairong Shang, Michael Prince, Ryan M. Aydelott, Mathew J. Cherukara, Yine Sun, Michael D. Borland

llms rag agents

arXiv AI

Jul 28

VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy

arXiv:2607. 23006v1 Announce Type: cross Abstract: Scientific question answering requires a retrieval system to solve two distinct problems: identifying which papers are relevant and locating the supporting evidence within those papers.

By Xinyan Zhong, Yuwei Shi, Yuqi Wei, Chen Shen, Tianhang Zhou, Zhenghao Wu

llms rag agents nlp benchmarks

arXiv AI

Jun 16

TechRAG: Evidence-Gated Multimodal Agentic RAG for Technical Literature Reasoning

arXiv:2606. 01613v2 Announce Type: replace-cross Abstract: This paper presents an agentic multimodal retrieval-augmented generation (RAG) framework for domain-specific literature reasoning, instantiated on a curated corpus of several thousand papers in intelligent tires, vehicle dynamics, vehicle control, sensing, estimation, and machine learning.

By Kanwar Bharat Singh

llms rag agents multimodal

arXiv AI

Aug 19

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

The paper introduces DEPT, a method that trains a single decoder-only large language model to both expand queries and encode documents for retrieval. By preserving document embeddings close to their initial cached values while allowing gradients to flow through the generator, DEPT stabilizes retrieval targets and enables efficient index reuse and online hard‑negative mining. Experiments on the BEIR benchmark with Qwen3‑4B‑Instruct‑2507 and LLaMA‑3.2‑3B‑Instruct show that DEPT outperforms training‑free, independently trained, and staged unified baselines, with ablations confirming the benefits of preservation, whitening, end‑to‑end expansion training, and online negatives.

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

llms rag benchmarks

arXiv AI

Aug 5

SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG

arXiv:2608. 03860v1 Announce Type: cross Abstract: We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19.

By Kaysarul Anas Apurba, Md. Hasibul Hasan, Rofiqul Alam Shehab, Asab Azad

rag nlp benchmarks

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