@inproceedings{zhao-etal-2026-autoreproduce,
    title = "{A}uto{R}eproduce: Automatic {AI} Experiment Reproduction with Paper Lineage",
    author = "Zhao, Xuanle  and
      Sang, Zilin  and
      Li, Yuxuan  and
      Shi, Qi  and
      Zhao, Weilun  and
      Wang, Shuo  and
      Zhang, Duzhen  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    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 1: Long 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-long.1001/",
    pages = "21920--21942",
    ISBN = "979-8-89176-390-6",
    abstract = "Efficient reproduction of research papers is pivotal to accelerating scientific progress. However, the increasing complexity of proposed methods often renders reproduction a labor-intensive endeavor, necessitating profound domain expertise.To address this, we introduce the paper lineage, which systematically mines implicit knowledge from the cited literature. This algorithm serves as the backbone of our proposed , a multi-agent framework designed to autonomously reproduce experimental code in a complete, end-to-end manner. To ensure code executability, incorporates a sampling-based unit testing strategy for rapid validation. To assess reproduction capabilities, we introduce , a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity. Extensive evaluations on PaperBench and demonstrate that consistently surpasses existing baselines across all metrics. Notably, it yields substantial improvements in reproduction fidelity and final execution performance. The code is available at \url{https://github.com/AI9Stars/AutoReproduce}."
}