The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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.
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.
The paper introduces ElephantBench, a closed‑book knowledge probe with 1,094 multi‑account factual questions generated via an auditable graph‑based pipeline that pulls documents from a low‑exposure web corpus and identifies naturally occurring disagreements. Across 32 large language models, even the best model only recovers both divergent accounts on 52.4% of questions, and most models recall one account while omitting the other, indicating persistent epistemic myopia. The study shows that scaling model size and inference‑time reasoning improves recall but does not eliminate incompleteness, and that exposure imbalance in the corpus biases models toward the dominant account.
LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent. Existing retrieval-augmented approaches pre-materialize evidence via fixed chunking, embeddings, or persistent indexes: effective for lookup, yet costly, stale-prone, and committed to a granularity before the query is known.
arXiv:2608. 16185v1 Announce Type: cross Abstract: LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent.