Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG
- Published
- Source
- arXiv
- Paper number
- 725
- Field
- AI / General
- arXiv ID
- 2607.22319
Key points
- It diagnoses the three major problems in LLM-based data integration, namely hallucination, label dependence, and cost, as stemming from a lack of knowledge grounding.
- It lays out an evolution path from Naive RAG to GraphRAG and KG-RAG and then to Agentic RAG.
- In Agentic RAG, it proposes a structure in which five agents, Planner, Retriever, Reasoner, Decision, and Evaluator, collaborate.
- It identifies major research challenges such as memory-retrieval knowledge conflict, batch retrieval noise, and agent initialization.
- It emphasizes that a new benchmark is needed to evaluate reliability through evidence-based reasoning, cost efficiency, and autonomy at the same time.
Paper links
External research summaries. These are not HDATF publications or measured product results.