SAG (SQL‑Retrieval Augmented Generation) is a structured retrieval framework that indexes documents as event‑entity pairs, forming latent hyperedges that preserve n‑ary relations without building a global knowledge graph. At query time, shared entities act as join keys, dynamically creating a query‑scoped neighborhood of related events while keeping each evidence chunk intact. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that SAG outperforms existing dense‑retrieval baselines, achieving the highest recall and end‑to‑end QA performance, especially as reasoning‑chain complexity grows.
By Yuchao Wu, Junqin Li, Xingcheng Liang, Yongjie Chen, Yinghao Liang, Linyuan Mo, Guanxian Li
arXiv:2608.12129v2 Announce Type: replace
Abstract: While retrieval-augmented generation (RAG) has proven effective at giving LLMs access to external knowledge, mainstream dense-retrieval implementat...
By Yuchao Wu, Junqin Li, XingCheng Liang, Yongjie Chen, Yinghao Liang, Linyuan Mo, Guanxian Li
Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus. We ask whether a single model-inter...
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:2610.08463v1 Announce Type: cross
Abstract: Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an...
By Edan Kinderman, Elad Hoffer, Yochai Blau, Brian Chmiel, Ron Banner, Daniel Soudry, Boris Ginsburg
arXiv:2609.07050v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularl...
By JungMin Yun, YoungBin Kim
DualView is an efficient dual‑view cascaded reranking framework designed for multi‑hop document reranking in question answering. It combines a Local Scorer that uses stacked cross‑attention for fine‑grained query‑document relevance with a Global Scorer that models inter‑document dependencies via Transformer‑based context aggregation. An Adaptive Gate dynamically fuses these views based on query semantics, achieving high recall and low latency on benchmarks such as MuSiQue.
By Litong Zhang, Jiaxin Li, Kuo Zhao
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
Hi-Q is a new framework for multi‑hop question answering that refines queries hierarchically based on evidence retrieved from a corpus. At each node it tests whether the current query unit is supported by evidence; if not, the node is expanded using a dependency‑preserving binary operator and verified for semantic coverage. The resulting query tree grows according to corpus support signals, and Hi‑Q achieves state‑of‑the‑art performance on three multi‑hop QA benchmarks, outperforming both iterative retrieval and graph‑based baselines without constructing a corpus‑wide graph.
By Jueun Kim, Sungho Park, Wook-Shin Han
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
The paper introduces ORDER, a task‑conditioned retrieval‑augmented generation framework that dynamically adapts both indexing and retrieval strategies to each incoming query. It first clusters questions to learn cluster‑specific chunking, metadata filtering, and reranking settings, then routes queries to the appropriate pre‑built index via nearest‑centroid assignment. Additionally, a supervised query router predicts relevant collections and a Uniform Multi‑source Sampler distributes the retrieval budget evenly across selected sources, yielding superior performance on heterogeneous historical archives compared to existing RAG systems.
By Aur\'elien Pellet (LRE), Julien Perez, Marie Puren
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