# LLM4Cell: A Survey of Large Language and Agentic Models for Single-Cell Biology

arXiv: https://arxiv.org/abs/2510.07793 · HTML: https://mlsanity.com/paper/2510.07793/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 13 of 13)

- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning](https://mlsanity.com/paper/2306.04371/) (2023)
- [Large Language Models in Plant Biology](https://mlsanity.com/paper/2401.02789/) (2024)
- [scAgent: Universal Single-Cell Annotation via a LLM Agent](https://mlsanity.com/paper/2504.04698/) (2025)
- [GeneMamba: An Efficient and Effective Foundation Model on Single Cell Data](https://mlsanity.com/paper/2504.16956/) (2025)
- [CellVerse: Do Large Language Models Really Understand Cell Biology?](https://mlsanity.com/paper/2505.07865/) (2025)
- [ChatCell: Facilitating Single-Cell Analysis with Natural Language](https://mlsanity.com/paper/2402.08303/) (2024)
- [scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling](https://mlsanity.com/paper/2404.06153/) (2024)
- [QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis](https://mlsanity.com/paper/2406.14307/) (2024)
- [scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection](https://mlsanity.com/paper/2506.20697/) (2025)
- [Spatial Coordinates as a Cell Language: A Multi-Sentence Framework for Imaging Mass Cytometry Analysis](https://mlsanity.com/paper/2506.01918/) (2025)
- [Cell2Text: Multimodal LLM for Generating Single-Cell Descriptions from RNA-Seq Data](https://mlsanity.com/paper/2509.24840/) (2025)
- [Towards Applying Large Language Models to Complement Single-Cell Foundation Models](https://mlsanity.com/paper/2507.10039/) (2025)

## Built on by (most cited first, 3 of 3)

- [ES-Merging: Biological MLLM Merging via Embedding Space Signals](https://mlsanity.com/paper/2603.14405/) (2026)
- [scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing](https://mlsanity.com/paper/2606.13007/) (2026)
- [Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations](https://mlsanity.com/paper/2606.24940/) (2026)

## Abstract (arXiv)

Large language models (LLMs) and emerging agentic frameworks are beginning to transform single-cell biology by enabling natural-language reasoning, generative annotation, and multimodal data integration. However, progress remains fragmented across data modalities, architectures, and evaluation standards. LLM4Cell presents the first unified survey of 58 foundation and agentic models developed for single-cell research, spanning RNA, ATAC, multi-omic, and spatial modalities. We categorize these methods into five families-foundation, text-bridge, spatial, multimodal, epigenomic, and agentic-and map them to eight key analytical tasks including annotation, trajectory and perturbation modeling, and drug-response prediction. Drawing on over 40 public datasets, we analyze benchmark suitability, data diversity, and ethical or scalability constraints, and evaluate models across 10 domain dimensions covering biological grounding, multi-omics alignment, fairness, privacy, and explainability. By linking datasets, models, and evaluation domains, LLM4Cell provides the first integrated view of language-driven single-cell intelligence and outlines open challenges in interpretability, standardization, and trustworthy model development.