VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge

arXiv:2608.07994 · cs.AI, cs.IR · Submitted 2026-08-18 · Read on arXiv

Wenqi Chen, Haofei Yang, Rui Yang, Fangming Li

cs.AI, cs.IR

Submitted: 2026-08-18

Updated: 2026-08-19

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• RAG — 115× in this paper · explained in 3 episodes
• Knowledge Graph — 16× in this paper · explained in 8 episodes
• Retrieval-Augmented Generation — 11× in this paper
• Accuracy — 10× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Localization — 7× in this paper · explained in 2 episodes
• Answer Accuracy — 5× in this paper
• Energy Saving — 5× in this paper
• Fault Management — 5× in this paper
• Personalization — 5× in this paper · explained in 2 episodes
• Retrieval Recall — 5× in this paper
• Knowledge graphs — 4× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 4× in this paper · explained in 21 episodes
• Semantic Similarity — 4× in this paper · explained in 2 episodes
• Sufficiency — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Ablation Study — 3× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Graph Construction — 3× in this paper · explained in 3 episodes
• Hallucination — 3× in this paper · explained in 4 episodes
• Hierarchical Structure — 3× in this paper · explained in 3 episodes
• Multi-hop Reasoning — 3× in this paper · explained in 3 episodes
• Papers — 3× in this paper · explained in 3 episodes
• Unified Framework — 3× in this paper · explained in 6 episodes
• Augmentation — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Experience Assurance — 2× in this paper
• General Dataset — 2× in this paper
• Information Gap — 2× in this paper · explained in 2 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 4 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Coherence — 1× in this paper · explained in 2 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Generator — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Hybrid Architecture — 1× in this paper · explained in 2 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Language Models (LLMs) — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 9 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences
• Complex QA and language models hybrid architectures, Survey
• A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization
• Retrieval-Augmented Generation for Large Language Models: A Survey
• Context-as-AI-Service: Surfacing Cross-File Dependency Chains for LLM-Generated Developer Documentation
• A Survey of Multimodal Retrieval-Augmented Generation
• Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
• BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents
• C-Pack: Packed Resources For General Chinese Embeddings
• NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
• Qwen3 Technical Report
• ReAct: Synergizing Reasoning and Acting in Language Models
• A Survey of Large Language Model Agents for Question Answering
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora

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