@inproceedings{shi-etal-2026-aircoder,
    title = "{AIRC}oder: Adaptive Integration of Multi-dimensional Retrieval for Repository-level Code Completion",
    author = "Shi, Chuanqi  and
      Gao, Miao  and
      Gao, Zhiqiang",
    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.1166/",
    pages = "25458--25470",
    ISBN = "979-8-89176-390-6",
    abstract = "Repository-level code completion relies on retrieval strategies to select relevant context from large codebases. Most existing methods employ single-dimensional retrieval based on textual similarity or dependency existence, leading to inconsistent performance across completion scenarios. Even task-adaptive and hybrid methods incur high computational costs while failing to explicitly consider structural hierarchy for retrieving functionally similar code. We introduce **AIRCoder**, a multi-dimensional retrieval framework that combines eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy. By proposing a structure-preserving chunking strategy and lightweight fusion module, AIRCoder learns context-dependent weights to adaptively integrate retrieval metrics for each query. Experiments on CrossCodeEval and RepoEval demonstrate that AIRCoder achieves an average improvement of **4.63{\%}** in exact match over the best baseline, with **10.2$\times$** higher efficiency and strong cross-language generalization across Python, Java, C{\#}, and TypeScript."
}