A new retrieval‑augmented generation framework can reason over thousands of technical papers on intelligent tires and vehicle control.
The system, described in a recent arXiv preprint, adds several layers to the usual RAG pipeline. First, it classifies a user’s intent and rewrites the query for text and images separately. It then pulls documents using FAISS, BM25 and a cross‑encoder, expands the evidence graph‑wise in Neo4j, and pulls visual snippets with ColSmol embeddings and MUVERA encoding. A 100‑point rubric judges whether the retrieved evidence is sufficient; if not, the pipeline retries with reformulated queries and even searches external databases. Generation is split among Planner, Researcher, Writer and Critic agents that map evidence to citations and self‑correct.
The novelty lies in stitching together multimodal evidence and an automated sufficiency check. For safety‑critical fields like automotive control, being able to cite both text and schematics while flagging weak evidence could reduce reliance on unchecked AI hallucinations. The approach also shows how a knowledge graph can keep citations honest inside a closed corpus.
In short, the evidence‑gated multimodal RAG offers a more accountable way to query specialized research. Its next hurdles are scaling beyond a few thousand papers, proving robustness on real‑world diagnostic tasks, and integrating with industry literature pipelines.