Scholar-KG

Agents-K1: End-to-end knowledge orchestration pipeline for agent-native scientific knowledge graphs

Agents-K1 is a new pipeline that converts raw scientific documents into structured knowledge graphs for use by LLM-based research agents, addressing the gap where existing systems reduce papers to abstracts and flat citation edges. The system integrates a multimodal parser, a 4B information-extraction model trained with GRPO, and a tri-source agent interface combining web search, graph retrieval, and cross-document traversal. The authors process 2.46 million scientific papers to produce Scholar-KG, releasing a one-million-paper subset. Experiments show improvements in scientific information extraction, knowledge graph construction, and multi-hop reasoning.