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.

翻译：本文提出了一种面向特定领域技术推理支持的智能检索增强生成（RAG）框架，该框架基于包含约2100篇智能轮胎、车辆动力学及车辆控制领域学术论文的精选语料库实现。与传统的单次检索RAG系统不同，所提出的架构采用包含13个步骤的自主化流水线：通过意图分类查询、基于多维评估标准对证据充分性进行评分、执行带漂移防护查询重构的智能重试、通过迭代式"优化-检索-验证"循环搜索外部学术数据库（Crossref、OpenAlex、Semantic Scholar）、遍历Neo4j知识图谱获取关系上下文、验证引用完整性，并实施带自动重生成的生成后质量检查。主要贡献包括：涵盖五个维度的百分制证据充分性评分框架（含相关性衰减机制与混合规则/大语言模型审核）；具有迭代式智能循环的路径依赖型外部搜索架构；基于大语言模型实体提取和OpenAlex作者验证（含语料内引用解析）构建的知识图谱；以及包含引用验证与质量评估的自校正生成循环。该框架作为已实现的实践案例研究，展示了智能化的、基于证据的RAG如何支持大规模特定领域语料的文献导航与技术推理。