Executable Knowledge Graphs for Replicating AI Research

• URL: http://arxiv.org/abs/2510.17795v1
• Date: Mon, 20 Oct 2025 17:53:23 GMT
• Title: Executable Knowledge Graphs for Replicating AI Research
• Authors: Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen,
• Abstract summary: Executable Knowledge Graphs (xKG) is a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature.<n>Code will released at https://github.com/zjunlp/xKG.
• Score: 65.41207324831583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to insufficient background knowledge and the limitations of retrieval-augmented generation (RAG) methods, which fail to capture latent technical details hidden in referenced papers. Furthermore, previous approaches tend to overlook valuable implementation-level code signals and lack structured knowledge representations that support multi-granular retrieval and reuse. To overcome these challenges, we propose Executable Knowledge Graphs (xKG), a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature. When integrated into three agent frameworks with two different LLMs, xKG shows substantial performance gains (10.9% with o3-mini) on PaperBench, demonstrating its effectiveness as a general and extensible solution for automated AI research replication. Code will released at https://github.com/zjunlp/xKG.

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