arXiv Computation and Language October 28, 2026 By Yifan Zhao, Qianyang Zhao, Xinyu Guan, kai wei, Yuming Deng

QESChunker: A Single Objective Unifies Overlapping and Non-Overlapping Chunking for RAG

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv AI

Sep 16

ORDER: Task-Conditioned Routing for Retrieval-Augmented Generation

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

rag benchmarks

arXiv AI

Aug 5

Lightweight Chunk Selection for Mobile Retrieval-Augmented Generation

arXiv:2608. 03148v1 Announce Type: cross Abstract: RAG improves the factual grounding of LLM by incorporating external knowledge, but deploying RAG on mobile and edge devices remains challenging because retrieved context increases computation and memory.

By Sicong Chang, Yidan Shen, Wen Yu, Jiefu Chen, Xin Fu, Renjie Hu

llms rag safety

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 Machine Learning

Sep 30

Relevance Is Not Sufficient Evidence: Detecting Evidence Gaps Before Generation in RAG

arXiv:2609.37469v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) grounds large language models in external sources, but retrieved passages often name the right entities without...

By Suting Chen, Peichun Hua, Yunming Xiao

llms rag benchmarks

arXiv Computation and Language

Oct 21

The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering

arXiv:2610.26951v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunk...

By Debopriyo Banerjee, Owais Aijaz, Ahmer Tabassum, Zhuohan Xie, Preslav Nakov, Veselin Stoyanov

llms rag nlp benchmarks

arXiv AI

Aug 18

LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents

arXiv:2608. 16185v1 Announce Type: cross Abstract: LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent.

By Xingjun Wang, Gongsheng Li, Qi Fan, Yunlin Mao, Luyan Su, Yingda Chen

llms rag agents

One email a morning, machine-written

Related stories

ORDER: Task-Conditioned Routing for Retrieval-Augmented Generation

Lightweight Chunk Selection for Mobile Retrieval-Augmented Generation

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

Relevance Is Not Sufficient Evidence: Detecting Evidence Gaps Before Generation in RAG

The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering

LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents