@inproceedings{hong-etal-2026-hiras,
    title = "{H}i{RAS}: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution",
    author = "Hong, Hanhua  and
      LI, Yizhi  and
      Chen, Jiaoyan  and
      Ananiadou, Sophia  and
      Li, Xiaoli  and
      Kim, Jung-jae  and
      Lin, Chenghua",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.377/",
    pages = "7624--7652",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental results. However, existing approaches still use fixed sequential agent pipelines with weak global coordination, which limits their robustness and overall performance. In this work, we propose Hierarchical Research Agent System (HiRAS), a hierarchical multi-agent framework for end-to-end paper reproduction that employs supervisory manager agents to coordinate specialised agents across fine-grained stages. We also identify limitations in the reference-free evaluation of the Paper2Code benchmark and introduce Paper2Code-Extra (P2C-Ex), a refined protocol that incorporates repository-level information and better aligns with the original reference-based metric. We conduct extensive evaluation, validating the effectiveness and robustness of our proposed methods, and observing improvements, including {\ensuremath{>}}10{\%} relative performance gain above the previous state-of-the-art using open-source backbone models and significantly reduced hallucination in the evaluation. All code and data will be made publicly available."
}