@inproceedings{hui-etal-2026-agentgc,
    title = "{A}gent{GC}: Evolutionary Learning-based Lossless Compression for Genomics Data with {LLM}-driven Multiple Agent",
    author = "Hui, Sun  and
      Yanfeng, Ding  and
      Ma, Huidong  and
      Xu, Chang  and
      Jin, Keyan  and
      Zu, Lizheng  and
      Zhong, Cheng  and
      Liu, Xiaoguang  and
      Wang, Gang  and
      Cai, Wentong",
    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.131/",
    pages = "2740--2757",
    ISBN = "979-8-89176-395-1",
    abstract = "Lossless compression has made significant advancements in Genomics Data (GD) storage, sharing and management. Current learning-based methods are non-evolvable with problems of low-level compression modeling, limited adaptability, and user-unfriendly interface. To this end, we propose AgentGC, the first evolutionary Agent-based GD Compressor, consisting of 3 layers with multi-agent named Leader and Worker. Specifically, the 1) User layer provides a user-friendly interface via Leader combined with LLM; 2) Cognitive layer, driven by the Leader, integrates LLM to consider joint optimization of algorithm-dataset-system, addressing the issues of low-level modeling and limited adaptability; and 3) Compression layer, headed by Worker, performs compression decompression via a automated multi-knowledge learning-based compression framework. On top of AgentGC, we design 3 modes to support diverse scenarios: CP for compression-ratio priority, TP for throughput priority, and BM for balanced mode. Compared with 14 baselines on 9 datasets, the average compression ratios gains are 16.66{\%}, 16.11{\%}, and 16.33{\%}, the throughput gains are 4.73x, 9.23x, and 9.15x, respectively."
}