Framework compares RAG approaches for semi-structured data
A paper published on June 25, 2026 presents a framework for comparing regular, Graph, Modular, and Agentic RAG on semi-structured knowledge bases, using nine standardized scenarios.
Published on June 25, 2026, the paper presents a framework for evaluating regular, Graph, Modular, and Agentic RAG on semi-structured knowledge bases. It implements nine standardized scenarios and compares the approaches; the available summary does not detail the results for each scenario.
The proposal offers a structured way to compare variants under data and domain constraints. To verify the findings, consult the original paper and check how it defines the scenarios, evaluation criteria, and reported results. If you use AI to study or apply the method, avoid submitting identifiable or confidential data without appropriate authorization and controls.