arXiv AI

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.

arXiv AI

Jun 17

FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow

arXiv:2606. 17856v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) is effective for knowledge-intensive and multi-hop query tasks; however, many existing methods primarily seed entity-based graphs and rely on implicit semantic relevance propagation.

By Bihao Zhan, Zongsheng Cao, Jie Zhou, Bo Zhang, Liang He

rag efficiency benchmarks safety

arXiv AI

Jun 17

A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation

arXiv:2606. 18075v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundamental limitation: entity-centric and chunk-centric approaches operate on representations anchored to original text without true knowledge fusion.

By Haoyang Zhong, Yifei Sun, Antong Zhang, Chunping Wang, Lei Chen, Yang Yang

llms rag nlp

arXiv AI

Aug 11

KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs

arXiv:2608. 09779v1 Announce Type: cross Abstract: Answering complex conditional questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) remains a challenge, particularly in domain-specific contexts where general-purpose LLMs and RAG tend to underperform.

By Ghanshyam Verma, Simanta Sarkar, Devishree Pillai, Hotaka Shiokawa, Yourong Xu, Fiona Veazey, Peter Hubbert, Hui Su, Paul Buitelaar

llms rag nlp

arXiv AI

1d ago

GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG

arXiv:2608. 15056v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs, which can introduce noisy evidence, weaken multi-hop reasoning, and increase unsupported generation.

By Zafar Ali, Asad Khan, Aalia Malik, Pavlos Kefalas

llms rag multimodal

arXiv AI

Jun 29

Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering

arXiv:2606. 28076v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts.

By Yongxue Shan, Meihan Wu, Cundi Fang, Jie Peng, Xiaodong Wang

nlp

arXiv AI

Jun 12

Uncertainty-Aware Hybrid Retrieval for Long-Document RAG

arXiv:2606. 13550v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) depends critically on the quality and granularity of retrieved evidence.

By Hoin Jung, Xiaoqian Wang

rag nlp benchmarks

arXiv AI

Jul 23

HyGRL: Adaptive Hybrid Graph Reasoning for Multi-Entity Questions

arXiv:2607. 19398v1 Announce Type: new Abstract: Multi-entity compositional questions pose significant challenges to existing retrieval-augmented language models.

By Junyi Wang

llms rag reinforcement-learning efficiency benchmarks

arXiv AI

Jul 14

CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps

arXiv:2607. 10562v1 Announce Type: new Abstract: Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge.

By JungMin Yun, JuneHyoung Kwon, YoungBin Kim

llms nlp

arXiv AI

Jun 6

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.

By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a

llms rag agents nlp fine-tuning benchmarks safety

arXiv AI

Jun 30

SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning

arXiv:2606. 28447v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop reasoning tasks.

By Houyuan Qin, Rong Wu, Qinyuan Qin, Botian Shi, Jingjing Qu, Yang Sun, Pinlong Cai

rag

arXiv AI

Aug 5

DocTrace: Towards Traceable Long Document VQA via Hierarchical Evidence Graph Reasoning

arXiv:2608. 03292v1 Announce Type: new Abstract: Long Document Visual Question Answering (LongDocVQA) requires Multimodal Large Language Models (MLLMs) to locate, integrate, and reason over heterogeneous document elements distributed across multiple pages.

By Le Xiang, Zhicheng Guan, Hong Chen, Xiaocong Lin, Zhenghua Lei, Teng Hu, Bolei He, Long Zeng

llms rag agents nlp fine-tuning multimodal benchmarks

arXiv AI

Jun 2

TCAR-Gen: Temporal Graph Retrieval with Evidence Fusion for Knowledge-Grounded Generation

arXiv:2606. 00029v1 Announce Type: cross Abstract: Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal case narratives.

By Sidra Nasir, Muhammad Noman Zahid, Rizwan Ahmed Khan

llms rag nlp benchmarks

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