A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Zulun Zhu, Tiancheng Huang, Kai Wang, et al.
A comprehensive survey of graph-based approaches that organize external knowledge to enhance retrieval-augmented generation in large language models.
Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, et al.
A systematic survey of Graph-based Retrieval-Augmented Generation (GraphRAG) for customizing large language models, covering techniques, applications, and future directions.
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LinearRAG proposes a linear graph-based retrieval-augmented generation method for efficient retrieval on large-scale corpora.
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FaithfulRAG introduces fact-level conflict modeling to improve context faithfulness in retrieval-augmented generation, with potential extensions to multimodal RAG and trustworthy AI.
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A survey examining trustworthiness challenges in retrieval-augmented generation for large language models, covering evaluation, robustness, and open problems.
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A survey on knowledge-oriented retrieval-augmented generation, organizing methods that integrate structured knowledge into RAG systems.
Ryan Sze-Yin Chan, Federico Nanni, Tomas Lazauskas, et al.
This paper presents a retrieval-augmented reasoning system using a lean Qwen2.5-Instruct model fine-tuned with synthetic reasoning traces, achieving near-frontier performance on domain-specific queries while remaining locally deployable.
Hieu Tran, Zonghai Yao, Junda Wang, et al.
RARE enhances LLM reasoning accuracy and factual integrity by integrating retrieval-augmented actions into Monte Carlo Tree Search, enabling open-source models to rival GPT-4.
Saman Marandi, Yu-Shu Hu, Mohammad Modarres
This paper presents a framework for automated construction of Dynamic Master Logic models as knowledge graphs from technical documentation using retrieval-augmented large language models, validated on a nuclear reactor system.
Bajaj, Payal, Campos, Daniel, Craswell, Nick, et al.
This paper presents a dataset for the Advertisement in Retrieval-Augmented Generation task at Touché 2025, using segments from MS MARCO V2.1 and queries from Webis Generated Native Ads 2024.
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This paper introduces MM-PoisonRAG, a novel attack framework that disrupts multimodal retrieval-augmented generation systems through local and global knowledge poisoning.
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This paper introduces M^3KG-RAG, a multimodal RAG framework that leverages a knowledge graph to deliver query-relevant and answer-useful context for multi-hop multimodal question answering.