Abstract: Technology novelty search is critical for evaluating the innovation of research projects and patentability,where rigorous logical comparison poses significant challenges to generative artificial intelligence.Traditional retrieval-augmented generation pa-radigms frequently lose micro-level process features and topological correlations when processing long-form technical documents,as a result of the semantic smoothing effect.Meanwhile,in contexts where underlying evidence is insufficient,large language mo-dels are vulnerable to interference from parametric prior knowledge,which triggers cascading hallucinations and in turn causes factual deviations in novelty assessment conclusions.To address these issues,this paper proposes SRG-RAG,a retrieval-augmented generation framework integrating graph reranking and self-reflection mechanisms.Firstly,a multi-dimensional intent decoupling model is designed to improve initial screening precision.Secondly,by dynamically extracting technical feature subgraphs and introducing a topological differential scoring algorithm,fine-grained reranking of highly confusable technical schemes is achieved.Finally,a self-reflective evaluator based on pseudo-relevance feedback theory is constructed to implement closed-loop error correction via dynamic query rewriting.Experimental results on the self-built topological alignment benchmark TopoNovelty-Bench show that self-reflective retrieval-augmented generation,powered by Qwen2.5-72B-Instruct,reduces the factual hallucination rate from 22.4% to 6.2%,significantly enhancing the evidential validity and accuracy of novelty search reports.This work provides a reliable paradigm for high-fidelity generation tasks in vertical domains.

Key words: Retrieval-augmented generation, Technology novelty search, Graph reranking, Self-reflection mechanism, Topological alignment