arXiv:2606. 05724v1 Announce Type: cross 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.
arXiv:2607. 05577v1 Announce Type: new Abstract: Long-form fiction writers need memory that answers multi-hop questions about evolving story state: who knows a secret and when they learned it, whether an event preceded the narration that revealed it, whether a setup paid off, and how a relationship shifted.
NS-ST-GraphRAG is a neuro‑symbolic spatio‑temporal GraphRAG framework designed to process long‑form literary narratives by integrating ontology‑guided extraction, deterministic constraint checking, dual temporal coordinates, spatial scene attributes, and dynamic sub‑graph retrieval. It selects the appropriate graph state based on the temporal and spatial scope of a query, grounding generated answers in traceable evidence. The authors also introduce Red‑Chamber‑QA, an open multi‑hop question‑answering benchmark for classical Chinese literature, and report that NS‑ST‑GraphRAG outperforms a frozen‑window baseline and a closed‑book model on a held‑out 120‑question split.
arXiv:2609.00241v1 Announce Type: new Abstract: Long documents often distribute important information across extensive narrative passages and multiple tables, making faithful summarization particular...
arXiv:2607. 09328v2 Announce Type: replace-cross Abstract: Answering complex questions over long documents frequently requires integrating evidence that the source itself disperses naturally across distant passages.
arXiv:2607. 09328v1 Announce Type: cross Abstract: Answering complex questions over long documents frequently requires integrating evidence that the source itself disperses naturally across distant passages.
arXiv:2601. 17226v2 Announce Type: replace-cross Abstract: Counterfactual story retelling exposes LLM shortcomings in constrained narrative solution spaces where they can no longer rely on recalling memorised training data.
arXiv:2602. 15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks.
PonsRAG is a retrieval‑augmented generation framework designed to improve long narrative reasoning. It introduces Triple‑Layer Indexing to connect documents and eliminate cognitive islands, and Coordinated Reasoning to retrieve and integrate evidence across multiple layers. Experiments on four long‑context narrative benchmarks show PonsRAG surpasses the best baseline with an 11.56% relative accuracy gain on multi‑choice tasks.
arXiv:2605.27156v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, particularly for long-tail do...
arXiv:2609.38406v1 Announce Type: new Abstract: Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct mi...
arXiv:2601. 01095v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand temporally unfolding narratives in videos remains underexplored.
The paper presents a detailed examination of narrative elements—agency, setting, and events—within the Dolma web-scale pretraining corpus. Using a framework of 11 interpretable dimensions, the authors hand‑annotated 400 passages, expanded this to a 25,000‑passage LLM‑labeled dataset, and trained NarraBERT models to predict narrative features across 13 million passages, producing the NarraDolma dataset. The study reveals that narrative structure is measurable at scale and that narrative qualities vary unevenly across different data sources, topics, and formats, highlighting gaps in current data curation practices.