{"ID":3084863,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T05:00:38.846751169Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05724","arxiv_id":"2606.05724","title":"Narrative Knowledge Weaver: Narrative-Centric Retrieval-Augmented Reasoning for Long-Form Text Understanding","abstract":"Long-form narrative QA requires reasoning over evolving story worlds rather than isolated passages: answers may depend on earlier goals, changing character states, social relations, causal triggers, temporal position, and later consequences. Existing retrieval and graph-augmented generation methods improve evidence access, but their units--chunks, entities, relations, summaries, or tool actions--do not directly encode how evidence functions in a story. We introduce Narrative Knowledge Weaver(NKW), a source-grounded framework that aligns textual evidence, atomic facts, canonical graph structure, entity profiles, interactions, episodes, and storylines. At query time, NKW uses text, graph, and narrative tools with post-retrieval reading skills to assemble evidence and audit actor, scope, polarity, state, and temporal constraints. Across STAGE, FairytaleQA, and QuALITY, NKW is strongest on screenplay-level story-world QA while remaining competitive on more passage-centered benchmarks. Ablations, question-type analyses, graph-asset statistics, and case studies show complementary benefits for character, scene, temporal, causal, and narrative-progression reasoning.","short_abstract":"Long-form narrative QA requires reasoning over evolving story worlds rather than isolated passages: answers may depend on earlier goals, changing character states, social relations, causal triggers, temporal position, and later consequences. Existing retrieval and graph-augmented generation methods improve evidence acc...","url_abs":"https://arxiv.org/abs/2606.05724","url_pdf":"https://arxiv.org/pdf/2606.05724v1","authors":"[\"Qiuyu Tian\",\"Fengyi Chen\",\"Yiding Li\",\"Youyong Kong\",\"Fan Guo\",\"Yuyao Li\",\"Jinjing Shen\",\"Zhijing Xie\",\"Yiyun Luo\",\"Xin Zhang\",\"Yingce Xia\",\"Zequn Liu\"]","published":"2026-06-04T05:30:11Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[]","has_code":false}