A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Ruichen Zhang, Hongyang Du, Yinqiu Liu, et al.
Proposes an interactive AI framework with pluggable LLM and RAG modules for next-generation network management and optimization, demonstrating effective optimization problem design.
Boci Peng, Yun Zhu, Yongchao Liu, et al.
The first comprehensive survey of Graph Retrieval-Augmented Generation (GraphRAG), formalizing its workflow into graph-based indexing, graph-guided retrieval, and graph-enhanced generation.
Yutao Zhu, Huaying Yuan, Shuting Wang, et al.
A comprehensive survey of how large language models are integrated into information retrieval systems, covering query rewriters, retrievers, rerankers, readers, and search agents.
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MKG-RAG-Bench is a benchmark for evaluating retrieval in multimodal knowledge graph-augmented generation, addressing the gap left by unstructured multimodal RAG.
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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Seakr introduces self-aware knowledge retrieval to enable adaptive retrieval augmented generation, dynamically deciding when and what to retrieve.
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A survey on knowledge-oriented retrieval-augmented generation, organizing methods that integrate structured knowledge into RAG systems.
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Introduces ALR, a lightweight method for retrieving domain-specific LLM agents from an LLM Agents Factory based on user queries.