arXiv AI

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.

arXiv AI

Jul 28

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.

By Hong-Yu An, Yun-Jian Zhang, Chen-Wei Liang, Tian-Yi Zhang, Jian Ding, Yi-Lun Wu, Ao-Bo Li, Wei-Cong Su, Saifullah, Mujiangshan Wang

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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

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arXiv AI

Jun 30

Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering

arXiv:2502. 11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing.

By Runxuan Liu, Bei Luo, Jiaqi Li, Baoxin Wang, Ming Liu, Dayong Wu, Shijin Wang, Bing Qin

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arXiv AI

Aug 6

D$^2$F-ReAG: Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation

arXiv:2608. 04444v1 Announce Type: cross Abstract: Large language models (LLMs) often generate inaccurate answers due to their reliance on static internal knowledge.

By Jiaoyang Li, Junhao Ruan, Shengwei Tang, Kaiyan Chang, Zhengtao Yu, Tong Xiao, Jingbo Zhu

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arXiv AI

Jul 17

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning

arXiv:2607. 14114v1 Announce Type: cross Abstract: Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision.

By Haohua Niu, Xingtong Yu, Yang Liu, Junfeng Fang, Xuanting Xie, Jie Tan, Zhongjian Zhang, Hong Cheng, Yuan Fang

llms benchmarks

arXiv AI

Aug 11

Counterfactual Benchmarking and Training for Factuality Consistency and Order-Robust Grounded Reasoning in LLMs over Heterogeneous Knowledge

arXiv:2608. 07838v1 Announce Type: new Abstract: Large language models (LLMs) have increasingly supported response generation grounded in user-provided knowledge spanning heterogeneous structures.

By Shibo Chu, Yuze Liu, Tiehua Zhang, Zhishu Shen, Lianghua He, Haofen Wang, Zhijun Ding

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arXiv AI

Jun 15

Knowledge Graph Enhanced Memory-Augmented Retrieval for Long Context Modeling

arXiv:2606. 14047v1 Announce Type: cross Abstract: Long-context language modeling requires not only extending context windows but maintaining coherent understanding of entity states and relationships across thousands of tokens -- a challenge that semantic similarity alone cannot address.

By Ghadir Alselwi, Basem Suleiman, Hao Xue, Shoaib Jameel, Hakim Hacid, Flora D. Salim, Imran Razzak

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arXiv AI

Aug 6

Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting

arXiv:2604. 12503v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios.

By Shuai Wang, Xixi Wang, Yinan Yu

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arXiv AI

Jul 21

Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge Graph

arXiv:2607. 17266v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing.

By Peiji Yu, Xin Chen, Tianxing Wu

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arXiv AI

Jul 29

MedJudgeRAG: Option-Wise Evidence Judgment with Dynamic Knowledge Graphs for Medical MCQA

arXiv:2607. 24838v1 Announce Type: cross Abstract: In medical multiple-choice question answering (MCQA), Retrieval-Augmented Generation (RAG) can supplement the domain knowledge of language models (LMs).

By Seongwon Seo, Seung Hwan Cho, Young-Min Kim

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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

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arXiv AI

Jul 23

NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering

arXiv:2602. 15353v3 Announce Type: replace-cross Abstract: Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require precise, structured multi-hop inference.

By Rong Fu, Yang Li, Zeyu Zhang, Jiekai Wu, Yaohua Liu, Shuaishuai Cao, Yangchen Zeng, Yuhang Zhang, Xiaojing Du, Simon Fong

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