TechGraphRAG：面向技术文献推理的智能图增强RAG框架
TechRAG: Evidence-Gated Multimodal Agentic RAG for Technical Literature Reasoning
AI总结：
提出一种13步自主流水线的智能检索增强生成框架，通过证据充分性评分、知识图谱遍历和自校正生成，支持领域特定技术文献推理。
摘要：
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，并在包含约2100篇智能轮胎、车辆动力学和车辆控制领域学术论文的精选语料库上进行了实例化。与传统的单次RAG系统不同，所提出的架构采用13步自主流水线：按意图分类查询，基于多维评分标准评估证据充分性，执行带有漂移防护查询重构的智能重试，通过迭代优化-搜索-验证循环搜索外部学术数据库（Crossref、OpenAlex、Semantic Scholar），遍历Neo4j知识图谱以获取关系上下文，验证引用完整性，并在自动重新生成后应用后生成质量检查。主要贡献包括：一个跨五个维度、带有相关性衰减和混合规则/LLM审查的100分证据充分性评分框架；一个具有迭代智能循环的路径依赖外部搜索架构；一个通过基于LLM的实体提取和OpenAlex作者验证以及语料库内引用解析构建的知识图谱；以及一个带有引用验证和质量评估的自校正生成循环。该框架作为一个实际实施的案例研究，展示了智能、基于证据的RAG如何支持大型特定领域语料库上的文献导航和技术推理。
英文摘要：
This paper presents an agentic multimodal retrieval-augmented generation (RAG) framework for domain-specific literature reasoning, instantiated on a curated corpus of several thousand papers in intelligent tires, vehicle dynamics, vehicle control, sensing, estimation, and machine learning. Unlike conventional single-pass RAG systems, the proposed architecture uses an autonomous, evidence-gated pipeline that classifies query intent, generates separate text and visual query rewrites, performs hybrid text retrieval with FAISS and BM25 followed by cross-encoder reranking, expands evidence through graph-guided chunk traversal over a Neo4j knowledge graph, and retrieves visual document evidence using ColSmol late-interaction embeddings with MUVERA fixed-dimensional encoding, approximate nearest-neighbor search, and MaxSim reranking. The framework scores evidence sufficiency using a 100-point rubric with hybrid rule-based/LLM review, retries retrieval through drift-guarded reformulation, searches external academic databases through optimize--search--vet loops, merges and deduplicates multimodal evidence, verifies citation integrity, and generates cited answers through Planner, Researcher, Writer, and Critic agents with self-correcting revision. Key contributions include: (i) a scalable multimodal retrieval architecture combining text, graph, and visual evidence over 40,000 document pages; (ii) an interpretable evidence sufficiency and retry mechanism; (iii) a multi-agent generation pipeline with evidence mapping and critic-driven revision; (iv) a domain knowledge graph with LLM-based entity extraction, OpenAlex author validation, and intra-corpus citation resolution; and (v) a route-dependent external search architecture for targeted literature expansion. The result is a practical, evidence-gated, multimodal agentic RAG architecture for technical reasoning over specialized research corpora.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
