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.
By Harish Saragadam, Sudhanshu Sharma, Meghana Pujari
Q2D-Web is a new large‑scale benchmark for agentic Retrieval‑Augmented Generation (RAG) systems, featuring a 190 million‑document web corpus and 70 k machine‑reformulated search queries in ten languages. It supplies three sets of relevance judgments—agent citations, production rankings, and a combined set enriched with LLM‑based labels—to evaluate first‑stage retrievers. Experiments on 13 retrievers show consistent ranking across judgment sets but significant variation across domains, languages, and query types, and demonstrate that a carefully sampled sub‑corpus can approximate full‑corpus evaluation with minimal loss in Recall@1000.
RIT‑RAG is a retrieval‑augmented generation framework that combines content retrieval with structural navigation of documents. It builds a tree for each document from its table of contents or sitemap, then uses retrieved chunk positions to induce manageable sub‑trees that an LLM agent can navigate, read selectively, and reformulate queries. Experiments on financial, scientific, and customer‑support benchmarks show RIT‑RAG outperforms vanilla, graph‑based, and agentic baselines, achieving up to 11.4‑point accuracy gains on the new EntQABench of 2.84 million technical‑documentation webpages.
By Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan, Yatin Nandwani, Sachindra Joshi, Dinesh Raghu
arXiv:2610.14428v1 Announce Type: new
Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpor...
By Istok Bako, Bonan Min, Tiffany Shao, Claire Cheng, Yashar Mehdad
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
arXiv:2609.37226v1 Announce Type: cross
Abstract: Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as...
By Soyeong Jeong, Sujay Kumar Jauhar, Sung Ju Hwang, Andrew Joohun Nam
The paper introduces STAIR, a retrieval system that uses a document’s Table of Contents to guide large language models in accessing global structure, thereby reducing hallucinations in Retrieval Augmented Generation. Experiments with a fine‑tuned Differentiable Search Index show that ToC‑based retrieval yields a low hallucination rate (<0.05%) and improves Recall@1 to 82.6% on the newly released SearchTome benchmark, outperforming baselines like BM25, DPR, and Mistral. The authors also release SearchTome, a diverse dataset of 18 books across six domains, to encourage further research in ToC‑based retrieval.
By Vineet Kumar, Meghanadh Pulivarthi, vishwajeet kumar, Jaydeep Sen, Riyaz Ahmad Bhat, Sachindra Joshi
arXiv:2606.11350v2 Announce Type: replace
Abstract: Retrieval-augmented generation (RAG) degrades when applied to large, heterogeneous document collections. In RAG systems, dense similarity becomes l...
By Nabaraj Subedi, Ahmed Abdelaty, Shivanand Venkanna Sheshappanavar
arXiv:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.
By Guanming Xiong, Penghui Zhang
Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature). As humans interact with these corpora, they na...
arXiv:2610.10091v1 Announce Type: cross
Abstract: Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literatu...
By Peter Baile Chen, Geoffrey X. Yu, Xinming Liu, Samuel Madden, Dan Roth, Jacob Andreas, Doug Downey, Michael Cafarella
The paper introduces AtlasNav, a persistent multi‑view corpus‑navigation framework that organizes a corpus into a Corpus Atlas, enabling large‑language‑model agents to navigate efficiently under finite interaction budgets. AtlasNav reduces online inference cost by 30.21% and achieves 92.05% strict accuracy on BrowseComp‑Plus, while earlier and more rapidly realizing required evidence compared to dynamic‑workspace methods. The approach also transfers well to other corpora such as PhantomWiki and heterogeneous enterprise knowledge bases, demonstrating that effective agentic search relies on both accessible evidence and a reusable corpus representation.
By Hongyu Guo, Zhiyu Zheng, Zhao Cao