{"ID":2853068,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16715","arxiv_id":"2510.16715","title":"Right Answer at the Right Time - Temporal Retrieval-Augmented Generation via Graph Summarization","abstract":"Question answering in temporal knowledge graphs requires retrieval that is both time-consistent and efficient. Existing RAG methods are largely semantic and typically neglect explicit temporal constraints, which leads to time-inconsistent answers and inflated token usage. We propose STAR-RAG, a temporal GraphRAG framework that relies on two key ideas: building a time-aligned rule graph and conducting propagation on this graph to narrow the search space and prioritize semantically relevant, time-consistent evidence. This design enforces temporal proximity during retrieval, reduces the candidate set of retrieval results, and lowers token consumption without sacrificing accuracy. Compared with existing temporal RAG approaches, STAR-RAG eliminates the need for heavy model training and fine-tuning, thereby reducing computational cost and significantly simplifying deployment.Extensive experiments on real-world temporal KG datasets show that our method achieves improved answer accuracy while consuming fewer tokens than strong GraphRAG baselines.","short_abstract":"Question answering in temporal knowledge graphs requires retrieval that is both time-consistent and efficient. Existing RAG methods are largely semantic and typically neglect explicit temporal constraints, which leads to time-inconsistent answers and inflated token usage. We propose STAR-RAG, a temporal GraphRAG framew...","url_abs":"https://arxiv.org/abs/2510.16715","url_pdf":"https://arxiv.org/pdf/2510.16715v1","authors":"[\"Zulun Zhu\",\"Haoyu Liu\",\"Mengke He\",\"Siqiang Luo\"]","published":"2025-10-19T05:00:04Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"RAG\"]","has_code":false}