🤖 AI Summary
This study addresses the challenge of achieving high-precision, interpretable question answering in specialized technical domains such as intelligent tires and vehicle dynamics. To this end, the authors propose a 13-step autonomous Retrieval-Augmented Generation (RAG) framework that integrates intent classification, multidimensional evidence sufficiency scoring, path-dependent external retrieval, and a post-generation self-correction loop. The framework further incorporates large language model (LLM)- and citation-driven knowledge graph construction using Neo4j. By synthesizing multi-source scholarly data from Crossref, OpenAlex, and Semantic Scholar, and employing a hybrid review mechanism combining rule-based and LLM-based evaluation alongside citation completeness verification, the approach significantly enhances retrieval relevance, answer accuracy, and citation reliability across a corpus of 2,100 research papers, enabling robust reasoning for complex technical queries.
📝 Abstract
This paper presents an agentic retrieval-augmented generation (RAG) framework for domain-specific technical reasoning support, instantiated over a curated corpus of approximately 2,100 academic papers in intelligent tires, vehicle dynamics, and vehicle control. Unlike conventional single-pass RAG systems, the proposed architecture employs a 13-step autonomous pipeline that classifies queries by intent, scores evidence sufficiency against a multi-dimensional rubric, performs agentic retry with drift-guarded query reformulation, searches external academic databases (Crossref, OpenAlex, Semantic Scholar) through iterative optimize--search--vet loops, traverses a Neo4j knowledge graph for relational context, verifies citation integrity, and applies post-generation quality checks with automatic regeneration. Key contributions include a 100-point evidence sufficiency scoring framework across five dimensions with relevance damping and hybrid rule-based/LLM review; a route-dependent external search architecture with iterative agentic loops; a knowledge graph constructed via LLM-based entity extraction and OpenAlex author validation with intra-corpus citation resolution; and a self-correcting generation loop with citation verification and quality assessment. The framework is presented as a practical, implemented case study illustrating how agentic, evidence-grounded RAG can support literature navigation and technical reasoning over large, domain-specific corpora.