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 pluggable, paper-centric knowledge base that automatically integrates code snippets and technical insights 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 is available at https://github.com/zjunlp/xKG.

翻译：复现人工智能研究对于大型语言模型（LLM）智能体而言是一项关键而富有挑战性的任务。现有方法通常难以生成可执行代码，这主要归因于背景知识不足以及检索增强生成（RAG）方法的局限性——这些方法未能捕捉到参考文献中隐藏的潜在技术细节。此外，先前的方法往往忽视了有价值的实现级代码信号，并且缺乏支持多粒度检索与重用的结构化知识表征。为克服这些挑战，我们提出了可执行知识图谱（xKG），这是一个可插拔的、以论文为中心的知识库，能够自动整合从科学文献中提取的代码片段和技术洞见。当将xKG集成到三个智能体框架并配合两种不同的LLM使用时，其在PaperBench基准上显示出显著的性能提升（使用o3-mini时提升10.9%），证明了其作为一种通用且可扩展的自动化AI研究复现解决方案的有效性。代码发布于 https://github.com/zjunlp/xKG。