Search: “rag”

Automating Multi-Hop RAG Evaluation via TRIAD: From Context Extraction to Validated Dataset Generation

arXiv:2608.21558v1 Announce Type: cross Abstract: Recent advances in LLMs and the adoption of RAG systems in industry have created a need for domain-specific question-answer datasets that can assess R

25 Aug 2026

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Research

SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation

DGX agent

arXiv:2509.20377v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has significantly improved the performance of large language models (LLMs) on knowledge-intensive tasks i

researcharxiv-cs-ai

24 Aug 2026

Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs

arXiv:2608.12675v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries. Existing priva

14 Aug 2026

Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting

arXiv:2603.14709v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches

researcharxiv-cs-lg

16 Jul 2026

PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption

arXiv:2607.00012v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, effectively mitigating their inherent

2 Jul 2026

CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG

arXiv:2602.05143v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a

25 Jun 2026

TA-RAG: Tone-Aware Retrieval-Augmented Generation for Peer-Support Health Communication

arXiv:2606.06794v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) successfully grounds large language model (LLM) outputs in trusted documents, but factual grounding alone is insuff

8 Jun 2026

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

arXiv:2509.20324v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with ex

applicationsarxiv-cs-ai

6 Jun 2026

Anytime-Valid Federated Conformal RAG for LLM Swarms

arXiv:2605.29139v1 Announce Type: cross Abstract: Federated Conformal RAG (FC-RAG) provides distribution-free coverage for a bandwidth-limited swarm of weak language models, but only at a fixed horizo

safetyarxiv-cs-lg

SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation

arXiv:2605.03534v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, but retrieval is not verification: a passage can be topical and still fail t

6 May 2026

mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA

arXiv:2508.05318v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Mo

28 Apr 2026

S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA

arXiv:2604.23783v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds language models in external evidence, but multi-hop question answering remains difficult because iterativ

28 Apr 2026

Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing

arXiv:2604.15771v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a foundational paradigm for grounding large language models in external knowledge. While adaptive re

safetyarxiv-cs-cl

20 Apr 2026

Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs

arXiv:2604.12610v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. Howe

15 Apr 2026

Local Ai

Disco-RAG: Discourse-Aware Retrieval-Augmented Generation

arXiv:2601.04377v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowled

local-aiarxiv-cs-ai

RAGScope: A Leakage-Controlled, Cost-Aware Evidence-Gating Protocol for RAG Hallucination Triage

arXiv:2609.39075v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems need inexpensive ways to route generated answers: accept low-risk outputs, review uncertain ones, and res

1 Oct 2026

Re-ranking and Late Interaction Drive Retrieval Quality: A Controlled Comparison of RAG Strategies for Scientific Question Answering

arXiv:2609.38473v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is now the standard way to ground Large Language Models (LLMs) in external knowledge, yet the design space of ret

1 Oct 2026

Exploratory As-Analyzed No-Detection of Culturally-Marked Predicate-Triggered PII Amplification in a Synthetic-English RAG Probe: A Predicate-Resource-Confounded Audit

arXiv:2608.20351v1 Announce Type: new Abstract: We ask whether stereotype-loaded queries about culturally marked people leak more personal information from a retrieval-augmented generation (RAG) syste

24 Aug 2026

Parametric Knowledge in RAG-SFT for Domain-Specific Document Generation

arXiv:2603.23047v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document questio

19 Aug 2026

HC-RAG: Evidence-Centric Retrieval-Augmented Generation over Heterogeneous Financial Filings

arXiv:2608.12335v1 Announce Type: new Abstract: Financial question answering over annual reports requires more than retrieving semantically similar passages. It often involves identifying relevant com

14 Aug 2026

Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective

arXiv:2608.08445v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to gr

safetyarxiv-cs-ai

11 Aug 2026

GoldenRetriever: Non-Interactive Homomorphic Encrypted Retrieval for Privacy-Preserving RAG

arXiv:2607.29019v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, but existing pipelines typically operate on p

3 Aug 2026

HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering

arXiv:2607.22597v1 Announce Type: new Abstract: Multi-hop question answering requires systems to retrieve evidence from multiple documents and connect scattered facts into a coherent reasoning process

28 Jul 2026

Local Ai

VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy

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 suppo

local-aiarxiv-cs-ai

28 Jul 2026

Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking

arXiv:2607.11933v1 Announce Type: new Abstract: Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-ti

15 Jul 2026

Harmonia: End-to-End RAG Serving Optimization

arXiv:2505.07833v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipe

9 Jun 2026

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG

arXiv:2606.00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence. Large lang

researcharxiv-cs-lg

2 Jun 2026

Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm

arXiv:2603.26668v2 Announce Type: replace-cross Abstract: As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the

P^2RAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top-k Retrieval

arXiv:2603.14778v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enables large language models to use external knowledge, but outsourcing the RAG service raises privacy c

applicationsarxiv-cs-ai

RAISE: RAG Design as an Architecture Search Problem

arXiv:2605.30029v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems expose numerous design choices spanning query rewriting, chunking, retrieval depth, reranking, and context

GraphSteal: Structural Knowledge Stealing from Graph RAG via Traversal Reconstruction

arXiv:2605.28645v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora, Grap

applicationsarxiv-cs-cl

28 May 2026

Agents

RAG-Coding: Enhancing LLM Medical Coding with Structured External Knowledge

arXiv:2605.27377v1 Announce Type: cross Abstract: We present RAG-Coding, an agentic method for automated ICD-10-CM coding. RAG-Coding orchestrates four large language model (LLM) agents and grounds th

agentsarxiv-cs-ai

28 May 2026

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)

arXiv:2510.06719v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application i

13 May 2026

From Clouds to Hallucinations: Atmospheric Retrieval Hijacking in Remote Sensing Vision-Language RAG

arXiv:2605.07273v1 Announce Type: cross Abstract: Multimodal RAG systems increasingly rely on vision-language retrievers to ground visual queries in external textual evidence. Existing adversarial stu

11 May 2026

FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning

arXiv:2605.01495v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding responses in external knowledge during inference. However, conve

5 May 2026

When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering

arXiv:2601.19827v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) extends large language models (LLMs) beyond parametric knowledge, yet it is unclear when iterative retrieval-re

5 May 2026

Tutorials

A Case Study on the Impact of Anonymization Along the RAG Pipeline

arXiv:2604.15958v1 Announce Type: cross Abstract: Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported u

tutorialsarxiv-cs-cl

20 Apr 2026

How Retrieved Context Shapes Internal Representations in RAG

arXiv:2602.20091v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) by conditioning generation on retrieved external documents, but the effec

17 Apr 2026

UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards

arXiv:2604.14967v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) extends Large Vision-Language Models (LVLMs) with external visual knowledge. However, existing visual RAG systems t

safetyarxiv-cs-cv

17 Apr 2026

Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage

arXiv:2603.08819v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks l

15 Apr 2026

Agents

Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation

arXiv:2602.02007v3 Announce Type: replace-cross Abstract: Agent memory systems often adopt the standard Retrieval-Augmented Generation (RAG) pipeline, yet its underlying assumptions differ in this set

agentsarxiv-cs-ai

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems

arXiv:2604.09666v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) and its graph-based extensions (GraphRAG) are effective paradigms for improving large language model (LLM) reason

Domain-Specific Data Generation Framework for RAG Adaptation

arXiv:2510.11217v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external ret

applicationsarxiv-cs-ai

BELIEFRAG: Making Adaptive RAG State-Aware under Evolving Evidence

arXiv:2609.39139v1 Announce Type: new Abstract: Adaptive RAG uses signals such as confidence, relevance, support, and retrieval quality to decide when to search or correct evidence. In multi-step retr

1 Oct 2026

REVA: Reusable Evidence View Aggregation for Context-Efficient RAG Serving

arXiv:2609.11209v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves knowledge-intensive large language model (LLM) applications by conditioning generation on retrieved docu

11 Sept 2026

Better Together: Complementary Query Rewriting Under a Strong RAG Baseline

arXiv:2609.05637v2 Announce Type: replace Abstract: A popular way to improve Retrieval-Augmented Generation (RAG) is to rewrite the user's question into several variants and search with all of them. W

applicationsarxiv-cs-cl

10 Sept 2026

A Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence Gathering

arXiv:2609.04981v1 Announce Type: new Abstract: Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. However, they face key limitations in evidence

7 Sept 2026

Compression Beyond the Uncompressed: A Two-Stage Training Recipe for Soft Context Compression in RAG

arXiv:2609.05152v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retrieved context inflates the input and degrades