arXiv:2606. 09105v1 Announce Type: new Abstract: Generating novel, feasible, and high-quality research ideas is an important yet challenging task in scientific discovery.
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arXiv:2606. 18075v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundamental limitation: entity-centric and chunk-centric approaches operate on representations anchored to original text without true knowledge fusion.
arXiv:2609.12791v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, hetero...
HyGRAIL is a framework for discovering scientific hypotheses in incomplete knowledge graphs by combining a graph neural network (GNN) triage with large language model (LLM) review. The GNN scores candidate hypotheses and routes only ambiguous cases to the LLM, which receives structured evidence from the graph converted into natural language. Experiments on MatKG show HyGRAIL achieves the highest F1 score, improves over baselines, and cuts LLM calls by over 54%.
Scientific knowledge graphs organize entities and relations extracted from scientific literature, but they remain inherently incomplete. Missing typed links in such graphs can therefore represent plau...
The paper investigates whether large language models (LLMs) can construct knowledge graphs (KGs) from documents in a zero‑shot, schema‑free manner. It introduces the Detail‑to‑Abstract Hierarchical Knowledge Graph (D2A‑HKG) framework, which splits KG construction into extraction, splitting, and abstraction stages, and evaluates the resulting graphs semantically and structurally. Using seven leading LLMs, the authors benchmark zero‑shot KG construction on CMW‑Lit—a dataset of pediatric mental‑wellness research—and find that state‑of‑the‑art LLMs generally produce relevant, document‑faithful triples with limited hallucination, though their extraction behaviors vary across stages. The study releases CMW‑Lit and the generated graphs as resources for future research and downstream knowledge‑intensive applications.
ConvergeWriter introduces a bottom‑up, data‑driven framework for long‑form document generation that first retrieves exhaustive knowledge from a source corpus and clusters it into distinct knowledge groups. These clusters then guide the creation of a hierarchical outline and the final text, ensuring the output is strictly grounded in the retrieved material and traceable to its sources. Experiments on 14B and 32B LLMs show that this approach matches or surpasses state‑of‑the‑art baselines, especially in scenarios requiring high factual fidelity and structural coherence.