MKG-RAG-Bench is a cross-domain benchmark for retrieval in multimodal knowledge graph-augmented generation, constructed via LLM curation from two MKGs with aligned QA datasets.
Simple is effective: The roles of graphs and large lan- guage models in knowledge-graph-based retrieval-augmented generation
8 Pith papers cite this work. Polarity classification is still indexing.
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SAGE is a self-evolving agentic graph-memory engine that dynamically constructs and refines structured memory graphs via writer-reader feedback, yielding performance gains on multi-hop QA, open-domain retrieval, and long-term agent benchmarks.
A full-paper multimodal knowledge-graph pipeline with a GRPO-trained 4B extractor and tri-source agent CLI reports improved multi-hop scientific reasoning, alongside a released one-million-paper knowledge graph.
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.
MF-CKGE separates temporal old and new knowledge into distinct embedding spaces with semantic decoupling and adaptive importance scoring to improve continual link prediction.
BIP turns event streams into autonomous insights by modeling journeys as absorbing Markov chains, extracting facts via knowledge graphs, and generating narratives constrained to verified data.
NeuroSymActive claims state-of-the-art KGQA accuracy (WebQSP 87.1, CWQ 62.5 Hits@1) by coupling differentiable neural-symbolic reasoning with uncertainty-guided MCTS and active human queries.
Graphs can help LLMs reduce hallucinations, boost reasoning via prompting techniques, and better process structured data.
citing papers explorer
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MKG-RAG-Bench: Benchmarking Retrieval in Multimodal Knowledge Graph-Augmented Generation
MKG-RAG-Bench is a cross-domain benchmark for retrieval in multimodal knowledge graph-augmented generation, constructed via LLM curation from two MKGs with aligned QA datasets.
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SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory
SAGE is a self-evolving agentic graph-memory engine that dynamically constructs and refines structured memory graphs via writer-reader feedback, yielding performance gains on multi-hop QA, open-domain retrieval, and long-term agent benchmarks.
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Agents-K1: Towards Agent-native Knowledge Orchestration
A full-paper multimodal knowledge-graph pipeline with a GRPO-trained 4B extractor and tri-source agent CLI reports improved multi-hop scientific reasoning, alongside a released one-million-paper knowledge graph.
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Retrieval-Augmented Generation with Graphs (GraphRAG)
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.
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Multi-Faceted Continual Knowledge Graph Embedding for Semantic-Aware Link Prediction
MF-CKGE separates temporal old and new knowledge into distinct embedding spaces with semantic decoupling and adaptive importance scoring to improve continual link prediction.
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Behavioral Intelligence Platforms: From Event Streams to Autonomous Insight via Probabilistic Journey Graphs, Behavioral Knowledge Extraction, and Grounded Language Generation
BIP turns event streams into autonomous insights by modeling journeys as absorbing Markov chains, extracting facts via knowledge graphs, and generating narratives constrained to verified data.
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NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
NeuroSymActive claims state-of-the-art KGQA accuracy (WebQSP 87.1, CWQ 62.5 Hits@1) by coupling differentiable neural-symbolic reasoning with uncertainty-guided MCTS and active human queries.
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Position: How can Graphs Help Large Language Models?
Graphs can help LLMs reduce hallucinations, boost reasoning via prompting techniques, and better process structured data.