arXiv AI
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 2
MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.
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.
arXiv Machine Learning
Jun 9
GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation
arXiv:2603. 24925v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources.
arXiv AI
Jun 6
A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning
arXiv:2601. 21162v2 Announce Type: replace-cross Abstract: Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing evidence through relational structure.
arXiv Machine Learning
Sep 15
Theseus in the Graph: Towards Traceable Multi-Hop Graph Navigation
arXiv:2609.14528v1 Announce Type: cross Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-langua...
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.