arXiv AI
LLMs Interpret, Embeddings Organize, Graphs Emerge: Agent-Driven Compilation of Scientific Knowledge
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Agents-K1: Towards Agent-native Knowledge Orchestration
arXiv:2606. 13669v1 Announce Type: new Abstract: Current LLM-based research agents have advanced through agent orchestration, yet largely overlook scientific knowledge orchestration.
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Valhalla: A Layered Knowledge-State and Service-Governance Framework for Long-Term Scientific Knowledge Work
arXiv:2608. 15193v1 Announce Type: cross Abstract: As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity.
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PEARL: Auditable Repair for Scientific Reasoning Graph Extraction
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arXiv Computation and Language
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HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs
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arXiv AI
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Ontology-Guided Knowledge Graph Construction from Structured Metadata and Climate Science Publications
The paper presents an ontology‑guided framework that uses specialized agents and large language models to extract, verify, and link factual triples from scientific publications, ensuring semantic consistency and traceability. Applied to 2,000 climate science papers, the system produced an ontology‑grounded knowledge graph with 133,990 nodes and 755,308 relations, achieving high agreement with human annotators and a 98.96% correctness rate among validated triples. The pipeline and resulting graph are publicly released for community use.