The paper introduces EAR, an Entity‑Aware Partitioning approach that improves retrieval‑augmented generation for multiple‑choice question answering by extracting normalized surface anchors from questions, answers, and the corpus. EAR retrieves local windows around matching anchors and can attach a larger parent passage via an extractive summary, reducing retrieved words by 37.5‑40.2% compared to fixed‑size chunks. Experiments on a cleaned MMLU‑style subset with Mistral, Gemma, and DeepSeek show modest accuracy changes, none statistically significant, highlighting EAR’s methodological contribution of compact, inspectable retrieval units.
By Cenab Batu Bora, Oylum Alatl{\i}, Sebnem Bora, Oguz Dikenelli
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
arXiv:2608. 15535v1 Announce Type: cross Abstract: We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models (LLMs).
By Rinit Jain, Tirthraj Mahajan, Advait Joshi, Raviraj Joshi
arXiv:2608. 12138v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings.
By Praveen Reddy, Charuta Mandke, Suvrankar Datta, Sarah Khan, Siddharth Reddy Anthireddy, Shitij Arora, Vishal Singh
arXiv:2608.21252v1 Announce Type: cross
Abstract: Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationshi...
By Xuanyu Meng, Jiashuo Sun, Jash Rajesh Parekh, Jiawei Han
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
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
The paper introduces three retrieval methods for Polish statutory law that use language‑model annotations attached to articles as surrogates. The methods—ASCR, ASCR‑H, and DTF—vary in cost and quality, with ASCR‑H achieving the highest rank‑one accuracy on bar exam questions, while DTF offers competitive performance with lower latency and cost. Extensive evaluation against 14 baselines on 300 exam questions demonstrates significant improvements in head‑rank accuracy and discusses limitations such as coverage asymmetry and negative results for lemmatisation, pseudo‑relevance feedback, and query rewriting.
By Orkun Yi\u{g}it Cengiz
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
QESChunker introduces a supervised approach to construct static chunk indices for retrieval-augmented generation, using question–evidence pairs to guide the process. It unifies non‑overlapping and overlapping chunking under a single utility‑maximization objective, enabling exact polynomial‑time optimization for non‑overlap and a greedy approximation for overlap. Experiments on two self‑constructed and three public datasets show that both variants outperform existing methods in complete‑evidence recall across most settings.
By Yifan Zhao, Qianyang Zhao, Xinyu Guan, kai wei, Yuming Deng
Viveka-Insight is a bilingual resource and open‑source pipeline for Swami Vivekananda’s complete works, providing a structure‑preserving parse of 32,694 paragraphs and 168,842 sentences, a cross‑lingual concept graph with 8,362 language‑agnostic concepts, a bilingual alias inventory of 60,850 surface forms, and a human‑annotated set of 200 paragraph‑concept edges. The resource enables citation‑grounded retrieval across the English and Bengali corpora, achieving Recall@10 of 0.86 for known‑item cross‑lingual queries and demonstrating concept‑extraction precision of 0.60 (up to 0.71 with confidence filtering). The design is intended to be transferable to other multilingual classical corpora.
By Tamal Maharaj
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