Narrative Knowledge Weaver: Narrative-Centric Retrieval-Augmented Reasoning for Long-Form Text Understanding

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

Narrative Knowledge Weaver (NKW) is a source-grounded framework designed for long-form narrative question answering, addressing limitations of existing Retrieval-Augmented Generation (RAG) methods. Unlike general RAG, NKW aligns textual evidence with atomic facts, canonical graph structures, entity profiles, interactions, episodes, and storylines to encode how evidence functions in evolving story worlds. It uses text, graph, and narrative tools with post-retrieval reading skills to assemble and audit evidence for actor, scope, polarity, state, and temporal constraints. Evaluated across STAGE, FairytaleQA, and QuALITY datasets, NKW demonstrates superior performance on screenplay-level story-world QA, particularly for reasoning over evolving states, relations, temporal order, and causal motivation, while remaining competitive on passage-centered benchmarks. It was tested with LLM backbones including Qwen3, Llama-3.1, and GPT-5.5.

Key takeaway

For AI Scientists and Machine Learning Engineers developing RAG systems for complex narrative understanding, NKW offers a robust approach to overcome limitations of general-purpose RAG. You should consider integrating narrative-centric knowledge modeling, including dynamic character profiles and storyline aggregation, to improve reasoning over evolving story worlds. This framework is particularly beneficial for tasks requiring multi-step causal, temporal, or plot progression analysis, enhancing accuracy on screenplay-level QA.

Key insights

Narrative Knowledge Weaver (NKW) enhances RAG by modeling evolving story worlds through structured narrative assets and multi-channel evidence assembly.

Principles

• Narrative evidence is functional, not just locational.
• Characters and relations are dynamic, not static.
• Source-grounded assets improve long-form QA.

Method

NKW constructs a source-grounded asset bundle (graph, events, facts, profiles, episodes, storylines) and uses a query-time agent with text, graph, and narrative tools, plus post-retrieval reading skills, for evidence assembly.

In practice

• Analyze screenplays for production continuity.
• Track character states across scenes.
• Improve long-form literary interpretation.

Topics

• Narrative Understanding
• Retrieval-Augmented Generation
• Knowledge Graphs
• Long-Form QA
• Screenplay Analysis
• Character State Tracking

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.