{"ID":2857850,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.07706","arxiv_id":"2510.07706","title":"Large Language Models Meet Virtual Cell: A Survey","abstract":"Large language models (LLMs) are transforming cellular biology by enabling the development of \"virtual cells\"--computational systems that represent, predict, and reason about cellular states and behaviors. This work provides a comprehensive review of LLMs for virtual cell modeling. We propose a unified taxonomy that organizes existing methods into two paradigms: LLMs as Oracles, for direct cellular modeling, and LLMs as Agents, for orchestrating complex scientific tasks. We identify three core tasks--cellular representation, perturbation prediction, and gene regulation inference--and review their associated models, datasets, evaluation benchmarks, as well as the critical challenges in scalability, generalizability, and interpretability.","short_abstract":"Large language models (LLMs) are transforming cellular biology by enabling the development of \"virtual cells\"--computational systems that represent, predict, and reason about cellular states and behaviors. This work provides a comprehensive review of LLMs for virtual cell modeling. We propose a unified taxonomy that or...","url_abs":"https://arxiv.org/abs/2510.07706","url_pdf":"https://arxiv.org/pdf/2510.07706v1","authors":"[\"Krinos Li\",\"Xianglu Xiao\",\"Shenglong Deng\",\"Lucas He\",\"Zijun Zhong\",\"Yuanjie Zou\",\"Zhonghao Zhan\",\"Zheng Hui\",\"Weiye Bao\",\"Guang Yang\"]","published":"2025-10-09T02:41:30Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.CE\",\"cs.LG\",\"q-bio.CB\"]","methods":"[\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}