[Paper] Agents-K1: Towards Agent-native Knowledge Orchestration

Source: arXiv - 2606.13669v1

Overview

Current LLM-based research agents have advanced through agent orchestration, yet largely overlook scientific knowledge orchestration. Existing works often reduce papers to abstracts, surface mentions, and flat \texttt{cites} edges, omitting key entities, claims, evidence, mechanisms, and method lineages essential for scientific reasoning. To this end, we introduce \textbf{Agents-K1}, an end-to-end knowledge orchestration pipeline that converts raw documents into agent-native scientific knowledge graphs. Agents-K1 integrates three components under a unifying theoretical foundation: a multimodal parser whose five-module schema captures entities, multimodal evidence, citations, and typed inter-entity relations across the full paper rather than abstracts alone; a 4B information-extraction backbone trained with GRPO under a rule-based reward; and a graphanything CLI, a tri-source agent interface that unifies web search, multimodal graph retrieval, and cross-document traversal. On top of this, we process 2.46 million scientific papers across six subjects to produce \textbf{Scholar-KG}, of which we release a one-million-paper subset, and the full Scholar-KG is accessible via the SCP link below. The same pipeline can be extended to general-domain corpora and to schema-conformant data synthesis. Extensive experiments demonstrate that Agents-K1 achieves superior performance in scientific information extraction, knowledge graph construction, and multi-hop scientific reasoning.

Key Contributions

This paper presents research in the following areas:

• cs.AI

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.AI.

Authors

• Zongsheng Cao
• Bihao Zhan
• Jinxin Shi
• Jiong Wang
• Fangchen Yu
• Zhijie Zhong
• Zijie Guo
• Tianshuo Peng
• Zhuo Liu
• Yi Xie
• Xiang Zhuang
• Yue Fan
• Runmin Ma
• Shiyang Feng
• Xiangchao Yan
• Anran Liu
• Peng Ye
• Wenlong Zhang
• Shufei Zhang
• Chunfeng Song
• Fenghua Ling
• Jie Zhou
• Liang He
• Bo Zhang
• Lei Bai

Paper Information

• arXiv ID: 2606.13669v1
• Categories: cs.AI
• Published: June 11, 2026
• PDF: Download PDF