@inproceedings{luo-etal-2026-makes,
    title = "What Makes {AI} Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations",
    author = "Luo, Yujie  and
      Yu, Zhuoyun  and
      Wang, Xuehai  and
      Zhu, Yuqi  and
      Zhang, Ningyu  and
      Wei, Lanning  and
      Du, Lun  and
      Zheng, Da  and
      Chen, Huajun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-short.70/",
    pages = "841--861",
    ISBN = "979-8-89176-391-3",
    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 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."
}