arXiv AI

RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answering

arXiv:2606. 02488v1 Announce Type: new Abstract: Multi-hop question-answering systems often use expensive retrieval on every question.

arXiv AI

Jun 3

Cost-Aware Query Routing in RAG: Empirical Analysis of Retrieval Depth Tradeoffs

arXiv:2606. 02581v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and end-to-end latency.

By Sanjay Mishra

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arXiv AI

Jul 8

Modality Relevance is not Modality Utility: Post-hoc Selective Modality Escalation for Cost-Aware Multimodal RAG

arXiv:2607. 05438v1 Announce Type: cross Abstract: Multimodal retrieval-augmented generation (RAG) grounds a generator in evidence drawn from heterogeneous modalities -- text, tables, and images.

By Xue Li, Yiming Gai

llms rag multimodal

arXiv Machine Learning

Aug 11

SAGE: SLO-Aware Adaptive Retrieval for Production RAG Systems

arXiv:2608. 08237v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems in production operate under strict service level objectives (SLOs) on tail latency and infrastructure cost.

By Muhammad Faizan Raza (Luna), Shuo (Luna), Yang, Satish Mahadevan Srinivasan

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arXiv AI

Jun 30

ConCise: Training-Free Conclusion-Chain State Compression for Cost-Efficient Multi-Step RAG Services

arXiv:2606. 28361v1 Announce Type: cross Abstract: Multi-step retrieval-augmented generation (RAG) has been widely deployed as LLM-powered web services for complex question answering, where iterative retrieval-reasoning rounds deliver strong multi-hop accuracy.

By Kuan Yan, Zhiqing Tang, Tian Wang, Weijia Jia

llms rag nlp efficiency

arXiv AI

Jul 14

STEC: Evidence Compression for Deep Search in Open-domain Multi-Hop QA

arXiv:2607. 10795v1 Announce Type: new Abstract: In open-domain multi-hop question answering (QA), LLM-based search agents offer a promising approach to knowledge-intensive QA by combining retrieval with reasoning.

By Xinkang Li, Rong Jiang, Xin Song, Ye Wang, Yue Han, Changjian Li

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arXiv AI

Jun 12

Rethinking RAG in Long Videos: What to Retrieve and How to Use It?

arXiv:2606. 13141v1 Announce Type: new Abstract: Retrieval-augmented generation is moving beyond text into long, egocentric video, where systems must select query-relevant chunks across multiple modalities and temporal granularities.

By Yuho Lee, Jisu Shin, Nicole Hee-Yeon Kim, Jihwan Bang, Juntae Lee, Kyuwoong Hwang, Fatih Porikli, Hwanjun Song

rag benchmarks

arXiv AI

Aug 11

TreeHop: Efficient Embedding-Level Query Rewriter

arXiv:2504. 20114v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multiple document chunks.

By Zhonghao Li, Kunpeng Zhang, Jinghuai Ou, Shuliang Liu, Xuming Hu

llms rag nlp

arXiv AI

Jun 30

Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation

arXiv:2606. 29328v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) typically treats context selection as ranking chunks against a single query embedding.

By Bingxue Zhang, Jianying Jia, Feida Zhu

rag multimodal benchmarks

arXiv AI

Jul 29

Salient Knowledge Pathways: Sparse Cross-Modal Routing for Efficient Knowledge-Intensive Multimodal Question Answering

arXiv:2607. 25422v1 Announce Type: new Abstract: Knowledge-intensive multimodal question answering (KI-MMQA) sits at the intersection of three expensive primitives: long visual token sequences, dense retrieval over large external corpora, and full cross-modal fusion.

By Noor Islam S. Mohammad, Ulu\u{g} Bayaz{\i}t

nlp efficiency multimodal benchmarks

arXiv AI

Jun 4

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

arXiv:2606. 04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text.

By Mengao Zhang, Xiang Yang, Chang Liu, Tianhui Tan, Ke-wei Huang

llms rag nlp benchmarks data-engineering

arXiv AI

Jun 30

Query-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge Graphs

arXiv:2606. 30133v1 Announce Type: cross Abstract: Retrieval-augmented generation built on knowledge graphs (Graph RAG) outperforms flat passage retrieval on multi-hop question answering by leveraging graph structure.

By Illia Makarov, Mykola Glybovets

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arXiv AI

Jul 29

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.

By Chandan Kumar Sah, Xiaoli Lian, Li Zhang

llms rag nlp benchmarks

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