@inproceedings{dip-etal-2026-llm4cell,
    title = "{LLM}4{C}ell: Taxonomy and Evaluation of {LLM} and Agentic Models for Single-Cell Biology",
    author = "Dip, Sajib Acharjee  and
      Zafor, Adrika  and
      Paul, Bikash Kumar  and
      Shuvo, Uddip Acharjee  and
      Emon, Muhit Islam  and
      Wang, Xuan  and
      Zhang, Liqing",
    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.1942/",
    pages = "41913--41954",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) and emerging agentic frameworks are beginning to influence single-cell biology by enabling natural-language interfaces, generative annotation, and multimodal data integration. However, progress remains fragmented across data modalities, model families, and evaluation practices. LLM4Cell presents a unified survey of 58 foundation and agentic models developed for single-cell research, spanning RNA, ATAC, multi-omic, and spatial modalities. We organize these methods into five families foundation, text-bridge, spatial/multimodal, epigenomic, and agentic and map them to eight key analytical tasks, including annotation, trajectory inference, perturbation modeling, and drug-response prediction. Drawing on over 40 public datasets, we analyze benchmark coverage, data diversity, and ethical or scalability constraints, and synthesize reported capabilities across ten domain-level dimensions related to biological grounding, multimodal alignment, fairness, privacy, and interpretability. By explicitly linking datasets, modeling paradigms, and evaluation domains, LLM4Cell provides an integrated perspective on language-driven single-cell analysis and highlights open challenges in standardization, interpretability, and trustworthy model development."
}