A research team has published an agentic RAG framework that grades its own retrieved evidence before it will generate an answer.
The system, called TechRAG, targets a specific corpus: several thousand academic papers on intelligent tires, vehicle dynamics, vehicle control, sensing, and related fields. Rather than doing a single retrieval pass and generating whatever it finds, TechRAG runs a multi-stage pipeline. It classifies the incoming query, rewrites it separately for text and visual retrieval, then pulls results from multiple indexes — including FAISS vector search, BM25 keyword matching, and a Neo4j knowledge graph that expands results by traversing citation and entity connections. The combined evidence is then scored against a 100-point rubric. If it doesn't pass, the system reformulates the query and tries again before any answer-generation agent touches the results.
This addresses a well-documented failure mode: single-pass RAG answers confidently even when retrieved evidence is thin, because nothing in the loop tells it to stop. An evidence-sufficiency gate is a direct structural fix for that problem, at least within a bounded domain. The system is also multimodal — it retrieves figures and tables from document images, not just text, which matters for technical literature where the actual data often lives in charts that plain text extraction misses.
The framework covers roughly 40,000 document pages across its corpus, which sounds substantial until you remember that a typical enterprise knowledge base can run into the millions — whether this evidence-grading loop holds up at that scale is a question the paper doesn't answer.