{"id":"ccdd175f-55dd-4f8c-aaff-5978495ab0be","arxiv_id":"2608.00006","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":8,"one_line_summary":"Vector-based retrieval-augmented generation outperformed knowledge-graph RAG on SME cybersecurity questions across LLaMA, Mistral, and Qwen.","lead":"This paper compares two ways of adding outside knowledge to large language models (vector search and knowledge-graph search) for small-business cybersecurity questions. The authors report that vector search produced more accurate, complete answers with far less hallucinated content on their 31-document Australian SME corpus.","discovery_kind":"new_application","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-04T01:48:08.941715+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}