Instead of feeding agents flat paper summaries, this work structures scientific knowledge into queryable graphs with explicit entities, claims, and evidence—making it easier for AI systems to perform multi-step scientific reasoning and fact-checking.
Agents-K1 builds agent-friendly knowledge graphs from scientific papers by extracting entities, claims, evidence, and relationships across full documents—not just abstracts. The system combines a multimodal parser, an information-extraction model trained with reinforcement learning, and a unified interface for searching and traversing knowledge.