CellAgent: LLM-Driven Multi-Agent Framework  for Natural Language-Based Single-Cell Analysis
Authors: Yihang Xiao
Jinyi Liu
YAN ZHENG
Shaoqing Jiao
Jianye HAO
Xiaohan Xie
Limingzhi
Ruitao Wang
Fei Ni
Yuxiao Li
Zhen Wang
Xuequn Shang
Zhijie Bao
Changxiao Yang
Jiajie Peng
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
LLM Agent
Single-cell RNA sequencing
Spatial transcriptomics
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data analysis are pivotal for advancing biological research, enabling precise characterization of cellular heterogeneity. However, existing analysis approaches require extensive manual programming and complex tool integration, posing significant challenges for researchers. To address this, we introduce CellAgent, an autonomous, LLM-driven approach that performs end-to-end scRNA-seq and spatial transcriptomics data analysis through natural language interactions. CellAgent employs a multi-agent hierarchical decision-making framework, simulating a “deep-thinking” workflow to ensure that analytical steps are logically coherent and aligned with the overarching research goal. To further enhance its capabilities, we develop sc-Omni, a high-performance, expert-curated toolkit that consolidates essential tools for scRNA-seq and spatial transcriptomics analysis. Additionally, we introduce a self-reflective optimization mechanism, enabling automated, iterative refinement of results through specialized evaluation methods, effectively replacing traditional manual assessments. Benchmarking against human experts demonstrates that CellAgent achieves significant improvement in efficiency across multiple downstream applications while maintaining excellent performance comparable to existing approaches and preserving natural language interactions. By translating high-level scientific questions into optimized computational workflows, CellAgent represents a step toward a new, more accessible paradigm in bioinformatics, allowing researchers to perform complex data analyses autonomously. In lowering technical barriers, CellAgent serves to advance the democratization of the scientific discovery process in genomics.

Published as a conference paper at ICLR 2026
CELLAGENT: LLM-DRIVEN MULTI-AGENT FRAME- WORK FOR NATURAL LANGUAGE-BASED SINGLE-
CELL ANALYSIS
Yihang Xiao**, Jinyi Liu Yan Zheng"*, "hangin, Jiao Jianye Hao" Xiaohan Xie Mingzhi Li2, Ruitao Wang", Fei Ni2, Yuxiao Li Zhen Wang'. Xuequn Shang*. Zhijie Bao Changxiao Yang' Jiajie Peng!.3i
AT for Science Interdisciplinary Research Center, School of Computer Science,
Northwestern Polytechnical University
"College of Intelligence and Computing, Tianjin University
"Key Laboratory of Big Data Storage and Management, Ministry of Industry and Information Technology, Northwestern Polytechnical University
"School of Cybersecurity, Northwestern Polytechnical University
ji anye hww@t edu. cn
jiajiepenggnnpu.ecducc
ABSTRACT
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data analysis are pivotal for advancing biological research, enabling precise charac- terization of cellular heterogeneity. However, existing analysis approaches re- quire extensive manual programming and complex tool integration, posing sig- nificant challenges for researchers. To address this, we introduce CellAgent, an autonomous, LLM-driven approach that performs end-to-end scRNA-seq and spa- tial transcriptomics data analysis through natural language interactions. CellA- gent employs a multi-agent hierarchical decision-makkin framework, simulating theep-thinking workflow ensure that analytical steps are logically coher- ent and aligned with the overarching research goal. To further enhance its ca- pabilities, develop sc-Omni, high-performance, expert-curated toolkit that consolidates essential tools for seRNA-seq and spatial transcriptomics analysis. Additionally, we introduce self-reflective optimization mechanism, enabling au- tomated, iterative refinement of results through specialized evaluation methods, effectively replacing traditional manual assessments. Benchmarking against hu- man experts demonstrates that CellAgent achieves significant improvement in efficiency across multiple downstream applications while maintaining excellent performance comparable to existing approaches and preserving natural language interactions. By translating high-level scientific questions into optimized com- putational workflows, CellAgent represents step toward new, more accessible paradigm in bioinformatics, allowing researchers to perform complex data analy- ses autonomously. In lowering technical barriers, CellAgent serves advance the democratization of the scientific discovery process in genomics.
INTRODUCTION
In recent years, the rapid development of technologies such as single-cell RNA sequencing (scRNA- seq) and spatial transcriptomics (ST) has revolutionized molecular biology, enabling scientists vestigate biological systems with unprecedented precision and depth (Luecken et al. 2025 Heumos al.) 2023b). These biological technologies have produced many large-scale datasets that require advanced computational tools to extract meaningful biological information efficiently.
'denotes equal contribution. 'Corresponding authors.

---

Published as a conference paper at ICLR 2026
Although current tools have significantly improved the efficiency and accuracy of scRNA-seq/ST data analysis (Sethi et al. 2024 Szatata et al. 2024), the manual and flexible application of various tools to analyze complex and massive single-cell sequencing data carries high labor costs. For ex- ample, researchers need to meticulously choose the appropriate tools, while fine-tuning appropriate hyperparameters that suited the unique characteristics of the input data (Lukas et al.) (023)
This requirement for dual expertise in both computational programming and fundamental biology increases the complexity and cost of performing single-cell data analysis tasks (Zhou et al. 2024),
creating significant barrier that inhibits the discovery of biological mechanisms. Therefore, there is an urgent need for a more automated, natural language interaction- based,, and functionally integrated analysis tool, which may enable users to direct complex analyses through natural language, thereby reducing technical barriers, improving data processing efficiency, and further promoting knowledge discovery with single-cell RNA-seq and spatial transcriptomics technologies.
To address these challenges, we introduce CellAgent, a multi-agent hierarchical framework (Fig- ure D that simulates "deep-thinking" workflow to ensure coherent, goal-oriented task execution. This framework is orchestrated by three specialized agents: a high-level Planner that decomposes complex user requests into manageable subtasks, an Executor that carries out the analysis by gen- erating and running code, and an Evaluator that assesses the quality of the results. The efficacy of this architecture is then built upon several core innovations designed to work in synergy. First, to ground the analytical capabilities of L.LSs in the domain-specific complexities of genomics, we developed sc-Omni, a comprchensive toolkit integrating expert knowledge and a wide array of anal- ysis tools (Table 2). Second, the Evaluator drives a self-reflective optimization mechanism, which leverages automated evaluation methods for various analysis tasks to iteratively refine outcomes, re- placing subjective manual assessments. Finally, to ensure seamless cooperation, we design a global and local integrative memory control mechanism for systematic storage and efficient retrieval of his- torical information, optimizing overall task performance. Compared to traditional tools, CellAgent supports natural language interaction and enables fully automated, unattended task execution.
Extensive benchmarking demonstrates that CellAgent's performance in task completion, quality, and efficiency, is comparable to that of human experts, and in certain aspects, it even surpasses human experts. Specifically, CellAgent achieves average execution success rate of more than 96% across over 60 datasets. Furthermore, CellAgent demonstrates competitive performance across various downstream tasks, such cell-type annotation, batch correction, trajectory inference, spa- tial domain identification, and spatial transcriptomics imputation. In summary, CellAgent achieves ofate-of-the-art results on several key bioinformatics tasks, all driven through the dialogue- based manner as in 2 To promote collaboration and facilitate efficient single-cell RNA sequencing and spatial transcriptomics data analysis, we provide an interactive online platform for CellAgent, enabling seamless integration with the broader research community.
RELATED WORK
Recently, the impressive capabilities of LLMs have spurred the development of LLM--riven au- tonomous AI agents, which have successfully automated tasks across various scientific domains. For instance, ChatMOF (Kang & Kim 2024) is an agent designed for materials science that lever- ages LLMs extract key information from natural language inputs to perform tasks such as data retrieval, property prediction, and the generation of metal-organic framework (MOF) structures. Similarly, ChemCrow (M. Bran et al. 2024) acts as a chemistry agent, augmenting an LLM by inte- grating a suite of expert- designd tools to autonomously plan and execute complex organic synthesis and drug discovery workflows.
Although computational frameworks such as Scanpy (Wolf et al.) 2018). Squidpy (Palla et al., 2022, Seurat (Hao et al. (202T), and Ve (Lopez et al.] (2018). together with single-cell foundation models like scGPT (Cui al.) (0224) and cellPLM (Wen et al. 2023). have substantially advanced scRNA- seq and spatial transcriptomics analysis, they still require domain experts to manually construct complex code pipelines. In parallel, general- LLMs such as GPT-4 and agentic frameworks
like AutoGen (Wu et al. 2023) lack the domain-specific knowledge needed for robust biological analysis.
Recent efforts have explored complementary directions in developing LLM-driven agents for biomedical research. Biomni (Huang et al. 2025). general-purpose biomedical agent that inte-
2

---

Published as a conference paper at ICLR 2026
Interactive Diagnosis
givinn single call data.
Singkord
CellAgent
description language Singlo cell cata for analyzing
Task Decomposition
Step 1: Quality control and No malizzto highly
Planner
petes (IVGG) Step S: Col Type Annotorion
Sc-Omni
0
Sub-task Completion
for single-ool data
Executor
squidpy
FACT
Useful tooks the cureett step: "Colmarier", bapptttt". we can use Cellmarker
Wa' we JE dher
CellTypist
TANGRAAM
scanpy
Tools decument
Froe scleet Separt Aretatorrelaan Instintiates tollmuree
Code: Sandbox
ainobatiin
type
Sandbox excouting Python R codo.
CONDA
clastee Toereter)
Evaluntion reults
The best solutton for this step
Memory Control
Evaluator
informaiion stap
(
code Estep $3
Figure Schematic of the CellAgent Framework. Users interact with CellAgent via natural language to obtain high-quality, automated analysis results tailored to their specific needs. Then the framework operates hierarchically, with a high-level Planner that performs fine-grained task decomposition based input data characteristics and user queries. In the lower-level execution phase, subtasks are completed sequentially. An Executor selects optimal tools from the sc-Omni toolkit to generate and execute code. An Evaluator then rigorously assesses the outcomes, proposing refinements needed. This self-reflective optimization loop iterates to enhance precision, and the final results from all subtasks are synthesized to meet the user's requirements.
grates LLM reasoning with retrieval-augmmenté planning and code execution, facilitates the effi cient formulation and empirical validation of scientific hypotheses. In parallel, domain-specialized agents such as AutoBA (Zhou et al. 2024) and BioMANIA (Dong et al. 2023} aim lower pro- gramming barriers and automate single-cell data analysis. However, these approaches primarily focus on improving task execution success rates rather than enhancing analysis quality. critical limitation remains the absence of mechanisms to automatically evaluate the biological relevance of results, which constrains their ability to autonomously select suitable algorithms and optimize hyperparameters across heterogeneous datasets, a challenge that has been consistently emphasized in prior studies (Heumos et al.) 2023a Rao et al.) 2021). In contrast, CellAgent addresses this gap by explicitly integrating biological relevance assessment into its automated workflow through a collaborative architecture, where dedicated Evaluator Agent and a self-reflective optimization mechanism jointly ensure high-quality, biologically meaningful outcomes.
3

---

Published as a conference paper at ICLR 2026
single-cell transcriptomics data
spatial transcriptomicc data
Tibbaty vinii pers
codly nuner duaeen
alll umuide de musoer enprentin moedo Leen dute
th edick Tedyy gullal sdal ae gou and
ratoon petracennts (tatter
strs seen cer marttdeet
Scar www.o Cogerta accumm cow.illooaerr staderr
redetim
giret spger Merrt scr schesoo 6'/
pinct Whoelaans ceeeror perteesii
(pnder)
Example of using CellAgent for single-cell RNA sequencing and spatial transcrip- tomics. The user provides a dataset, CellAgent plans tasks, executes code, and generates results.
Figure 2:
3 METHODS
The CellAgent framework automates and enhances single-cell and spatial transcriptomics analysis through a structured, multi-agent workflow, as illustrated in Figure [] The system built upon a hierarchical architecture that simulates a "deep-thinking" process, ensuring that complex analytical goals are decomposed and executed logically, and can be viewed as hierarchical decision process over the space of analysis pipelines. First, high-level Planner agent interprets a user's natural language request and formulates a coherent, multi-step analytical plan (Section B.T). Next, each step of this plan processed within a core operational loop where Executor and Evaluator agents collaborate to not only perform the analysis but also iteratively optimize the results for quality and accuracy (Section (32).. In this view, the Planner is responsible for task decomposition, the Executor for instantiating conerete candidate pipelines, and the Evaluator for scoring and selecting among these candidates. This entire workflow is supported by a suite of essential modules including a comprehensive toolkit, memory system, and secure code sandbox that provide the necessary infrastructure for a robust and biologically sound analysis, which we detail in Section [3.3] This integrated approach enables CellAgent autonomously produce high-quality, reproducible results, reliably completing tasks and interacting with the user via natural language, as demonstrated in Figure
3.1 TASK PLANNING AND HIERARCHICAL DECOMPOSITION
The initial phase of the CellAgent workflow is managed by the Planner, an LLM-driven agent that functions as the system's 's high-level architect. Its primary responsibility is to translate a user's ab- stract analytical goal into concrete, structured task plan. The Planner's system prompt (pKys is in- fused with expert knowledge of scRNA-seq and spatial transcriptomics workflows to ensure logical and sound task decomposition. This knowledge includes codified best-practices, such as the stan- dard order of operations for common analyses (e.g., quality control must precede normalization), typical parameter ranges, and awareness of which downstream tasks require specific upstream pre-
4

---

Published as a conference paper at ICLR 2026
processing steps. In practice, this stage determines the high-level structure of the analysis pipeline that subsequent agents will refine and execute.
Upon receiving user's gquuest-comprisngg the task description (Wask). the dataset (D), and any optional preferences (treq), the Planner first inspects a summary of the dataset, eh( to ground its strategy in the data's specifie characteristics. It then generates sequence of discrete, ordered subtasks. Formally, this decomposition process by the Planner defined as:
f t2 ..., in f A Aplysp Uask Mreq uDD xb
(1)
This hierarchical decomposition is crucial, it transforms a broad objective into a manageable series of steps, setting clear and logical foundation for the subsequent execution and optimization phase.
3.2 TASK EXECUTION VIA SELF-REFLECTIVE OPTIMIZATION MECHANISM
Following planning, each subtask passed to the core operational engine of CellAgent, which executes the analysis and iteratively optimizes the outcome. This phase is a dynamic collaboration between the Executor and Evaluator agents, governed by a Self-Reflective Optimization mechanism.
The process begins with the Executor agent, which implements the subtask t.. It consists of two components: Tool Selector and Code Programmer. The Tool Selector, AL queries the available toolset 'T to identify the most appropriate tools, Tr,, for the current step:
T + A4 ALLM
(Pays: Ureqq T,t,)
(2)
Using the selected tools and their documentation, the Code Programmer, A generates executable Python code (c₂) and textual analysis (w;). This process is cotext-aware, leveraging a memory module M that contains the successful code from previous steps to ensure coherence:
(Ca, w;) < - (Pays Uusk: Uneq up ( (D) M, t, Doc(Tr.)
(3)
In case of an execution error E(c;), the Executor autonomously performs self-correction to produce a valid code version.
Upon successful generation of result, the Evaluator agent, ALLM assesses the outcome and pro- vides natural language feedback if it deems revisions are necessary. Subsequently, when presented with multiple candidate solutions { the Evaluator selects the optimal one c, as the final output for the step:
ALILMM (Weys Breq, uD, 4, (d)), 11
This evaluation is driven by the Self-Reflectin Optimization Mechanism, comeestone of Cel- IAgett. The process initiates by executing a suite established algorithms for the given task, after which the Evaluator module, powered by GPT-4o, systematically assesses each method's output. The evaluation criteria are context-dependent and multifaceted, incorporating quantitative perfor- mance metrics (e.g., Accuracy Score for imputation and iLISI for batch correction), qualitative visual assessments guided by domain knowledge (e.g., for trajectory continuity and spatial domain coherence), and the synthesis of heterogeneous evidence (e.g., for cell type annotation). To ensure objectivity and mitigate biases from self-circularity, the Evaluator only receives anonymized out- puts, task-specific metrics, and diagnostic plots for each candidate run, but never the underlying prompts or tool names produced by the Executor. CellAgent then selects the highest-scoring algo- rithm to produce the final result. detailed breakdown of the optimization process for each task and the above fairness safeguards is available in the Appendix
3.3
KEY SUPPORTING MODULES
The planning and execution workflow of CellAgent is critically dependent on a set of modules that provide essential infrastructure for robustness, efficiency, and security.
Memory Control A key limitation of LLMs is their stateless nature. To overcome this, CellA- gent integrates a dedicated memory module with a dual-architecture. This is based on the principle that its analytical subtasks largely self-contained, depending on the final, validated outcome of
5

---

Published as a conference paper at ICLR 2026
Table Comparison of multiple tasks. cell type annotation, batch correction, trajectory inference, spatial domain identification, and spatial imputation.
CellAgent
scGPT
SCSA
ScType
Celltypist
CellMaker
Cell Type Annotation
Average
0.85
0.77
0.47
0.59
0.75
0.58
CellAgent
seVI
Liger
Harmony
Combat
ScGPT
Batcheureetion
0.67
0.66
0.65
0.60
0.56
0.50
Batch Correction
caseevvvaaiion
0.65
0.61
0.58
0.62
Overallgawe
0.67
0.66
0.65
0.60
0.57
0.57
CellAgent
Slingshot
Scorpius
Page tree
Page
RSteID/StemID
Corast
0.48
0.48
0.43
0.49
0.44
0.21
0.83
0.71
0.48
0.33
0.22
Trujectory Inference
WeoTnetures
0.70
0.77
0.64
0.69
0.74
0.56
Edg
0.88
0.88
0.83
0.61
0.14
0.20
Overallaeor
0.50
0.47
0.44
0.40
0.07
0.06
CellAgent
BayesSpace
DeppST
SEDR
spaGCN
stLcarn
Domain Identification ARI
0.47
0.46
0.47
0.44
0.38
0.38
CellAgent
Tangram
SpaGE
gim
manoSpaRe
RANKPce
0.90
0.80
0.65
0.35
0.40
RANKuMSE
0.90
0.80
0.75
0.65
0.35
0.45
Spatial Imputation
RANKSSIM
0.90
0.80
0.80
0.70
0.35
0.35
RANK
0.85
Accuracy.rom
0.88
0.75
0.73
0.71
0.41
0.40
the preceding one, not on intermediate trial-and-error processes. A global memory stores only the final code of each completed substep (M < to store only code is deliberate: bioinformatics analysis, code possesses high information tropy, concisely encapsulating complex data transformations. This strategy allows for the transfer of comprehensive contextual information with minimal token footprint. In contrast, local memory operates as a short-term workspace for the Executor within a single subtask. It captures the real- time execution trace, including all generated code snippets (both correct and incorrect), resulting eгror messages, and self-correction iterations. This allows the agent to learn from mistakes in real- time and avoid repeating errors before the local memory is discarded upon successful completion of the subtask. This liscard-after-us strategy ensures that only the clear and successful analysis path is retained in the global memory, preventing interference from the intermediate trial-and history and thereby maintaining a concise and efficient context for subsequent steps.
as illustrated Fig. [ The decision
Tool Retrieval To provide comprehensive analytical capabilities, CellAgent is equipped with a diverse toolkit, sc-Omni (T), ), for scRNA-seq and spatial transcriptomics analysis. This toolkit con- solidates tools for over 15 distinct analytical tasks, ranging from preliminary analysis like quality control advanced applications such trajectory inference and spatial domain identification. All tools are registered within the framework, allowing the Tool Selector to query the available functions and provide curated list of relevant options the Code Programmer for each subtask. To ensure precise usage, each tool is implemented documentation via docstrings. This self-documenting design enables the programmer to dynami- cally retrieve detailed specifications, including parameters and return values, during runtime. This capability is essential for generating code that is both syntactically and semantically correct.
Python class or function, accompanied by standardized
Code Sandbox ensure the security and reliability code execution, CellAgent implements a Code Sandbox, which isolates the generated code during execution. This is implemented through Jupyter Notebook Conversion (nbconvert), where data loading and each step the generated code are executed within structured Jupyter Notebook environment. This approach effectively decouples the execution of single-cell data analysis from the core CellAgent framework, reducing potential risks associated with direct execution while improving system robustness. Additionally, by leveraging notebook-bast execution strategy, CellAgent enhances result management, facilitates workflow reproducibility, and ensures structured and traceable execution for all analysis tasks.

---

Published as a conference paper at ICLR 2026
4 EXPERIMENTS
To comprehensively evaluate CellAgent, we conducted experiments on representative downstream tasks in both single-cell RNA sequencing and spatial transcriptomics. To ensure fair and compa- rable evaluation, we standardized preprocessing across all downstream tasks, controlling for both the number of cells and the set of gene expression features. For scRNA-seq, assessed hatch correction, cell type annotation, and trajectory inference; for spatial transcriptomics, we examined spatial domain identification and imputation (Table D. We further benchmarked CellAgent against human experts and established frameworks in terms of both quality and computational efficiency across these tasks, and compared the performance of alternative agents on the same benchmarks (Appendix E. and E.2). In addition, we performed ablation studies to isolate the impact of key de- sign choices. Specifically, we systematically evaluated CellAgent under different base models and analyzed the effect of enabling or disabling the memory optimization mechanism (Appendix [F.3) These results provide deeper insights into the robustness, efficiency, and adaptability of CellAgent across diverse settings.
4.1 SINGLE-CELL RNA SEQUENCE DOWNSTREAM TASKS
Batch correction aims to adjust for technical variation across different batches of scRNA-seg data, ensuring that technical discrepancies do not distort biological signals (Haghverdi et al. 2018). To evaluate CellAgent, we compared it with other advanced methods across five datasets covering ma- jor tissues such as lung, pancreas, and perirhinal cortex, with varying numbers of cells, genes, and batch origins (Table 4 of Appendix). Ten metrics were used to assess batch effect removal and biological variance preservation, and an overall score was calculated as a weighted sum (see Ap- pendix D.I. CellAgent outperformed other methods, achieving the highest average overall score (0.67), batch correction score (0.67), and bio-conservation score (0.66) across all datasets (Fig. [15 of Appendix). Visualization of human pancreas dataset further demonstrates that CellAgent effec- tively aligns cells of the same type across batches while preserving distinct separation of different cell types, particularly alpha, beta, and ductal cells (Fig. 16 and Fig. of Appendix).
To evaluate CellAgent for cell-type annotation, we compared its predictions with other advanced methods across six datasets spanning diverse tissues, organs, and sequencing technologies, includ- ing Smart-seq2, 10X Chromium, and Drop-seq (Table [ of Appendix). Using the Cell Ontology (CL), predicted annotations were categorized as "fully match." "partially match." " or "mismatch," corresponding to consistency scores of I, 0.5, and O. The average consistency score was then calcu- lated per dataset (see Appendix D.2). CellAgent achieved an average score of 85%. On the human PBMC dataset, it further refined existing annotations by subdividing CD8+ T cells into naive cyto- toxic T cells and CD&+ T cells (Fig-B)) This refinement is biologically meaningful because naive cytotoxic T cells represent distinct subset of CD8+ T cells that have not yet encountered their specific antigen and remain undifferentiated into functional effector cytotoxic T cells (Lam et al.) 2024). Additionally, CellAgent generated differential expression and marker gene visualizations, enabling intuitive interpretation of cluster identities and potential biological differences (Fig. 13)
Trajectory inference is intended to determine the pattern of a dynamic process experienced by cells and model cell state transitions (Trapnell et al. 2014). To assess the performance of CellAgent in trajectory inference, we compared it against five established trajectory inference methods across eight different datasets with gold-standard trajectory information (Saelens et al. 2019). We utilized four distinct types of metrics to evaluate different aspects of trajectories, and the overall score was calculated as a weighted average (details in Appendix D.3. CellAgent achieved the highest overall score of 0.50 (Table D]. Notably, in the "Aging HSC Kowalczyk" dataset, CellAgent successfully reconstructed the developmental trajectory from long-term hematopoietic stem cells (LT-HSC) to short-term hematopoietic stem cells (ST-HSC), and ultimately to multipotent progenitors (MPP) (Fig.Bp). This aligns with existing studies that describe gradual transition from hematopoietic stem cells with high proliferative and renewal potential to those with a progressive loss of such potential, but a gain of differentiated features (Quesenberry et al. 2014). Additionally, CellAgent generated heatmap of the top 20 differentially expressed genes along the trajectory (Fig. [4] in the Appendix), capturing the gradual upregulation of CD48 and MPO gene expression during the hematopoietic stem cell differentiation process, consistent with their known roles in immune cell maturation and myeloid differentiation (de Haan& & Lazare 2018). The finding supports the potential of CellAgent
7

---

Published as a conference paper at ICLR 2026
groud
Callggert
out
EDM oel
Nake CDV+ T coll
odt
Homatoppiede ston ool
LIMAA
Dendrtsc colls
LMM472
Magatarycppt
Moneoyto CD14
Monooytcs
Monocyaa, FOGRSA
cell
NK cols
Plasmactood denartic
Plasmacytoid Dendrit colls
b
UMAPI
UMA?
Col grouss Iground (ue)
Oct groupe (DoMMgot))
Pooss dodine (Cellggent)
Groups
LT-HBC
ST-HBC
10
Figure 3: Results of CellAgent in single-cell transcriptomics analysis. a, UMAP visualization of cell type annotation from the human PBMC dataset, colored according to the cell types annotated in the original study and the cell types predicted by CellAgent. b, The trajectory UMAP plot of original cell grouping, reconstructed cell grouping based on trajectory milestones by CellAgent, and pseudotime for the Aging HSC Kowalczyk dataset, going from LT-HSC, ST-HSC MPP.
in identifying key genes that play critical roles in cell fate determination. These results highlight the effectiveness of CellAgent as a powerful tool for both trajectory inference and identification of key regulatory genes.
4.2 SPATIAL TRANSCRIPTOMICS DOWNSTREAM TASKS
Spatial domain identification aims to delineate distinct tissue regions exhibiting spatial coherence based spatial transcriptomic data (Hu et al.
2021). To evaluate CellAgent, we compared it with
other advanced methods across 12 dorsolateral prefrontal cortex (DLPFC) slice datasets initially annotated by Maynard et al. (Maynard et al. 2021), where manual annotations delineated cortical layers (L1-L6) and white matter (WM). CellAgent achieved the highest average ARI score (0.47) across all datasets with a smaller interquartile range, indicating superior performance and robustness (Table Visualization on slice 151673 (3639 spots. 33,538 genes) shows that CellAgent accurately captured the intricate spatial organization of the tissue, including the differentiation of cortical layers and the white matter regions, producing smoother, biologically consistent spatial domains (Fig. 4b).
Spatial transcriptomics imputation focuses on estimating the missing gene expression over the mea- sured spots (Song et al., (003). To evaluate CellAgent, we compared it with other advanced meth- ods across seven datasets from multiple sequencing platforms, including 10X Visium, Slideseq V2, and seqFISH (Table 8 of Appendix). Performance was assessed using four metrics combined into an overall Accuracy Score (AS) (Li al. 2022). CellAgent outperformed other methods across all evaluation metrics, including Pearson correlation coefficient (PCC), root mean square error (RMSE), structural similarity index (SSIM), and Jensen-Shannon divergence (JS). CellAgent achieved the highest average AS of 0.88, outperforming the suboptimal method, Tangram, by 17% (Table D. It also demonstrated robust performance across diverse datasets. Specifically, on the mouse cortex dataset, the predicted expression of Igsf21, a marker gene in the mouse cortex (Tasic et al. 2018), closely aligned with the true expression patterns (Fig. 4b). Among the predicted genes, Ptgfmm Zyx, and Rprm showed the highest Moran's 's scores and were identified as top spatially variable genes. In particular, Rprm was predicted to be highly expressed in the L5/L6 layers of the cortex, consistent with its critical role neuronal synaptic signaling and plasticity (Tasic et al. 2018).
4.3 HUMAN CENTERED EVALUATION
CellAgent enables natural language-drr -riven analysis of scRNA-seg and spatial transcriptomics data. To systematically evaluate performance, we benchmarked CellAgent across eight major single-cell and spatial transcriptomics tasks on over 60 datasets (Tables 3 in Appendix), including preprocess-
8

---

Published as a conference paper at ICLR 2026
a
b
groud
ColMggnn
ground
Cal Agent
Layer2
120 L4
Layyed
Laysed
Layer5
Layert
LSA4
Hight
WM
c
d
Plafin
Zyx
Rpm
Plyin
Zyc
0.28
Ror
Collll
028
Gate2
0.5
Naced
024
Pig
024
LSLB
Keund
Hight
0.00
0.05
0.10
0.20
0.5
0.32
Figure 4: Results of CellAgent in spatial transcriptomics analysis. Visualization of the ground truth and CellAgent-identiffed spatial domains in slice 151673 of the DLPFC. b, The ground truth spatial distribution of the gene Igsf21 and the predicted distribution by CellAgent on the mouse cortex dataset. c, The top ten spatially variable genes were identified after imputation by CellAgent on the mouse cortex dataset. These genes were ranked based on Moran's 's I spatial autocorrelation scores. d, The spatial distribution of top three highly variable genes predicted by CellAgent.
ing, clustering, cell type annotation, batch correction, trajectory inference, spatial domain identifi- cation, spatial imputation, spatially variable gene identification, and neighborhood analysis. CellA- gent was compared with human experts for efficiency and quality. On average, it completed tasks in 8 minutes versus 13 minutes for experts and achieved slightly higher quality scores (0.25 in- crease) (Fig. 55 and b). Compared to GPT-4, CellAgent@3 3, which supports up to three iterations of self-debugging, achieved average execution success rate of 96%, far exceeding GPT-4's 24%. Usability assessment with 20 participants (10 experts, 10 novices) showed that 75% rated CellAgent as highly facilitative, surpassing GPT-4 (60%) and an online web server (30%) (Fig. and d). In summary, compared with online servers, single-cell foundation models, task-specific methods, and GPT-4, CellAgent offers high-quality analysis, no manual coding, self-optimizing execution, and natural language interaction (Fig. 6 in Appendix), demonstrating its potential to advance scRNA- seq and spatial transcriptomics data analysis.
5 CONCLUSION
Single-cell RNA sequencing and spatial transcriptomics data analysis essential for driving bi- ological discoveries. However, conducting high-quality data analysis requires profound domain knowledge and proficient programming skills, and most existing tools lack support for natural lan- guage interaction and have limitations in delivering high-quality automated analysis of single-cell and spatial transcriptomies data. To address these limitations, we introduce CellAgent, tonomous, LLM-driven framework that integrates natural language interactions, a multi-agent hier- making system, and a self-reflective optimization mechanism to automate single- cell RNA sequencing data analysis. This approach simulates a 'deep-thinking workflow, system- atically decomposing complex tasks, intelligently selecting tools, and iteratively refining results to achieve high-quality data analysis. Compared to single-cell foundation models, online Webserver, and other tools, CellAgent offers greater flexibility, broader task coverage, and an enhanced user experience through natural language interactive, dialogue-based approach. Through extensive experiments on a diverse set of datasets, CellAgent has demonstrated both competitive execution efficiency and analytical quality, highlighting its potential for automated data analysis. In summary, CellAgent is a versatile and scalable tool that reduces the complexity and cost of single-cell RNA sequencing and spatial transcriptomies data analysis. The development of Cell will establish
archical

---

Published as a conference paper at ICLR 2026
CalAgeriggs
1.0
Task 7-
0.0
Task
0.6
Task
Teak
0.4
Teek
0.2
Task
5
Efficenccy [min]
15
20
25
30
0.0
Task 1
Task
Task
Took
Task
Task 6
Task
Task 8
DelAggergg
-PP74
CeelAgeent
Agergig
Tiak
CollAgent
75.0%
Higly Faciltative
Task
Taok
0..0%
Modorately Faciitiaive
Task
Task
Wetserver 0%
Sighdy Factitativo
Task
Quality (0-10)
Figure 5: Performance comparison. a, Comparison of the efficiency of CellAgent and Human Expert on eight tasks involving scRNA-seq and spatial transcriptomies data analysis. Efficiency was assessed in minutes (Table B in Appendix). b, Comparison of the quality of CellAgent and Human Expert on eight tasks involving scRNA-seq and spatial transcriptomics data analysis. Quality was rated a scale from 0 to 10, with evaluations conducted by evaluators (n=5). c, Comparison of the success rates of GPT-4, CellAgent, and CellAgent @ (representing the best outcome after up to three attempts) across the tasks. Assessment of the facilitation of CellAgent, GPT-4, and Online Webserver on scRNA-seq and data analysis, as evaluated by 20 participants.
new paradigm in bioinformatics, broadening the role of generative Al in scientific discovery and potentially leading novel insights into biological systems.
ETHICS STATEMENT
The development and application of CellAgent were guided by principles of responsible Al and scientific integrity. All datasets utilized in this study for benchmarking and demonstration pub- licly available and have been previously published, ensuring that no new private or sensitive patient data collected. Our primary goal is democratize single-cell and spatial transcriptomics anal- ysis. making these powerful technologies more accessible to researchers who may lack extensive computational expertise. CellAgent designed as an assistive tool to augment, not replace, human expertise.
Human participation was essential for evaluating the performance and usability of framework. To benchmark the performance of CellAgent against current practices, we invited human experts participate in assessments efficiency and quality. All participating experts were provided with comprehensive information regarding the research objectives, evaluation procedures, and data usage, and gave their informed consent prior to their involvement. All collected performance data (e.g., time to complete tasks) and quality scores were fully anonymized. In the paper, results presented solely the form of aggregated statistics, ensuring that no personally identifiable information is disclosed. The participation of these experts was entirely voluntary, and they retained the right to withdraw at any stage without reason.
To ensure responsible deployment and mitigate potential risks, the CellAgent platform is intended for research purposes only. The authors declare no competing interests, and the research was con- ducted without any sponsorship that could have unduly influenced its outcomes.
10

---

Published as a conference paper at ICLR 2026
REPRODUCIBILITY STATEMENT
We have taken several steps ensure the reproducibility of our work. We offer an online webpage accessible / cn/
Code and Service: The complete sc-Omni toolkit and the source code for CellAgent are open- source and available at hettttss: :ttp:::://iitii /ttptttttttu./../// com 23ATBox/cee lagent.
Data: All datasets used in our experiments are publicly available from their original sources, which are cited throughout the manuscript.
The core logic of the CellAgent framework, including the roles and interactions of the Planner, Executor, and Evaluator agents, described in detail in the Methods section. The spe- cific prompts and interaction flows that govern the agents' behaviors are illustrated with examples in Appendix E, Figures 9-12. The LLM used for all agents is GPT-4o. While the model itself is propri- etary, detailed descriptions of the prompts and the agentic framework should enable researchers to replicate the system's behavior with similar state-of-the-an L.Mss. The evaluation metrics for all downstream tasks are standard in the field and are explicitly defined with their corresponding formulas in Appendix D.
Methodology:
6 ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (Grant Nos. 62422605, 92370132, 62433016, 62072376, and 62533021), the National Key Research and Development Pro- gram of China (Grant No. 2024YFE0210000).
REFERENCES
Maayan Baron, Adrian Veres, Samuel L Wolock, Aubrey L Faust, Renaud Gaujoux, Amedeo Vetere, Jennifer Hyoje Ryu, Bridget K Wagner, Shai Shen-Orr, Allon M Klein, et al. single-cell tran- scriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure. Cell systems, 3(4):346-33 360, 2016.
Maren Büttner, Zhichao Miao, F Alexander Wolf, Sarah A Teichmann, and Fabian Theis. A test metric for assessing single-cell ma-seq batch correction. Nature methods, 16(1):43-49 2019.
Haotian Cui, Chloe Wang, Hassaan Maan, Kuan Pang, Fengning Luo, Nan Duan, and Bo Wang. scgpt: toward building a foundation model for single-cell multi-omics using generative ai. Nature Methods, pp. 1-11, 2024.
Gerald de Haan and Scka Simone Lazare. Aging of hematopoietic stem cells. Blood. The Journal of the American Society of Hematology, 131(5):479-444 2018.
Zhengyuan Dong, Han Zhou, Yifan Jiang, Victor Zhong, and Yang Young Lu. Simplifying bioin- formatics data analysis through conversation. bioRxiv, 2023-10, 2023.
Martin Guilliams and Charlotte Scott. Liver macrophages health and disease. Immunity, 55(9): 1515-1529, 2022.
Laleh Haghverdi, Aaron TL Lun, Michael D Morgan, and John C Marioni. Batch effects single- cell ma-sequencing data corrected by matching mutual nearest neighbors. Nature biotechnol- ogy, 36(5):4211-22, 2018.
Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen William M Mauck, Shiwei Zheng, Andrew Butler, Maddie J Lee, Aaron J Wilk, Charlotte Darby. Michael Zager, et al. Integrated analysis of multimodal single-cell data. Cell, 184(13):3573-358) 2021.
Lukas Heumos, Anna C Schaar, Christopher Lance, Anastasia Litinetskaya, Felix Drost, Luke Zap- pia, Malte Lücken, Daniel Strobl, Juan Henao, Fabiola Curion. et al. Best practices for single-cell analysis across modalities. Nature Reviews Genetics, 24(8):550-572 2023a.
11

---

Published as a conference paper at ICLR 2026
Lukas Heumos, Anna C Schaar, Christopher Lance, Anastasia Litinetskaya, Felix Drost, Luke Zap- pia, Malte Lücken, Daniel Strobl, Juan Henao, Fabiola Curion, et al. Best practices for single-cell analysis across modalities. Nature Reviews Genetics, 24(8):550-577 2023b.
Brian Hie, Bryan Bryson, and Bonnie Berger. Efficient integration of heterogeneous single-cell transcriptomes using scanorama. Nature biotechnology. 37(6):685-691, 2019.
Wenpin Hou and Zhicheng Ji. Assessing gpt-4 for cell type annotation in single-cell ma-seq analysis.
Nature Methods, pp. 1-4, 2024.
Jian Hu, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J Irwin, Edward B Lee, Russell T Shinohara, and Mingyao Li. Spagen: Integrating gene expression, spatial location and histology identify spatial domains and spatially variable genes by graph convolutional network. Nature methods, 18( 18(11):1342-133 2021.
Kexin Huang, Serena Zhang, Hanchen Wang, Yuanhao Qu, Yingzhou Lu, Yusuf Roohani, Ryan Li, Lin Qiu, Gavin Li, Junze Zhang, et al. Biomni: A general-purposs biomedical ai agent. biorxiv, 2025.
Lawrence Hubert and Phipps Arabie. Comparing partitions. Journal of classification, 2:193-218, 1985.
W Evan Johnson, Cheng Li, and Ariel Rabinovic. Adjusting batch effects microarray expression data using empirical bayes methods. Biostatistics, 3(1):118-12) 2007.
Yeonghun Kang and Jihan Kim. Chatmof: an artificial intelligence system for predicting and gen- erating metal-organic frameworks using large language models. Nature Communications, 15(1): 4705, 2024.
Ilya Korsunsky, Nghia Millard, Jean Fan, Kamil Slowikowski. Fan Zhang. Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-ru Loh, and Soumya Raychaudhuri. Fast, sensitive and accurate integration of single-cell data with harmony. Nature methods, 1289 2019.
Nora Lam, YoonSeung Lee, and Donna L Farber. A guide adaptive immune memory. Nature Reviews Immunology, 24(1 1):810-829) 2024.
Nathan Lawlor, Joshy George, Mohan Bolisetty, Romy Kursawe, Lili Sun, Sivakamasundari, Ina Kycia, Paul Robson, and Michael L Stitzel. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-sperreeee -seecicc expression changes in type 2 diabetes. Genome research, 27(2):208-222, 2017.
Bin Li, Wen Zhang, Chuang Guo, Hao Xu, Longfei Li, Minghao Fang, Yinlei Hu, Xinye Zhang, Xinfeng Yao, Meifang Tang, et al. Benchmarking spatial and single-cell transcriptomics integra- tion methods for transcript distribution prediction and cell type deconvolution. Nature methods, 19(6):62-677, 2022.
Jialin Liu, Chao Gao, Joshua Sodicoff, Velina Kozareva, Evan Z Macosko, and Joshua D Welch. Jointly defining cell types from multiple single-cell datasets using liger. Nature protocols, 15(11): 3632-3663 2020.
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael Jordan, and Nir Yosef. Deep generative modeling for single-cell transeriptomics. Nature methods, 15(12):1053-1055 2018.
Malte D Luecken, Maren Büttner, Kridsadakorn Chaichoompu, Anna Danese, Marta Interlandi, Michaela F Müller, Daniel C Strobl, Luke Zappia, Martin Dugas, Maria Aoloéé-Tatche et al. Benchmarking leles- data integration in single-cell genomics. Nature methods, 19(1):41-500 2022.
Malte D. Luecken, Scott Gigante, Daniel B. Burkhardt, Robrecht Cannoodt, Daniel C. Strobl, Niko- lay S. Markov, Luke Zappia, Giovanni Palla, Wesley Lewis, and Daniel Dimitrov. Defining and benchmarking open problems in single-cell analysis. Nature Biotechnology, 43(7), 2025.
12

---

Published as a conference paper at ICLR 2026
Heumos Lukas, Schaar Anna C., Lance Christopher, Litinetskaya Anastasia, Drost Felix. Zappia Luke, Lücken Malte D., Strobl Daniel C., Henao Juan, and Curion Fabiola. Best practices for single-cell analysis across modalities. Nature reviews. Geneties, 2023.
Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller. Augmenting large language models with chemistry tools. Nature Machine Intelli- gence, pp. 1-11,2024,
Evan Z. Macosko, Anindita Basu, Rahul Satija, James Nemesh, Karthik Shekhar, Melissa Goldman, Itay Tirosh, Allison R Bialas, Nolan Kamitaki, Emily M Martersteck, et al. Highly parallel gedome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161(5):1202- 1214, 2015.
Kristen R Maynard, Leonardo Collado-Torres, Lukas M Weber, Cedric Uytingco, Brianna Barry, Stephen R Williams, Joseph L Catallini, Matthew N Tran, Zachary Besich, Madhavi Tippani, et al. Transcriptome-scat spatial gene expression in the human dorsolateral prefrontal cortex. Nature neuroscience, 24(3):425-436, 2021.
Tara McCray, Julian V Pacheco, Candice C Loitz, Jason Garcia, Bethany Baumann, Michael J Schlicht, Klara Valyi-Nagy, Michael Abern, and Larisa Nonn. Vitamin d sufficiency enhances differentiation of patient-derived prostate epithelial organoids. Iscience, 24(1), 2021.
David W McKellar, Lauren D Walter, Leo T Song, Madhav Mantri, Michael FZ Wang, Iwijn Vlaminek, and Benjamin D Cosgrove. Strength in numbers: Large-scale integration single- cell transcriptomic data reveals rare, transient muscle progenitor cell states in muscle regeneration. BioRxiv, pp. 2020-12, 2020.
Jeffrey R Moffitt, Dhananjay Sambah-Mukku Stephen Eichhorn, Eric Vaughn, Karthik Shekhar, Julio D Perez, Nimrod D Rubinstein, Junjie Hao, Aviv Regev, Catherine Dulac, et al. Molecular, spatial, and functional single-cell profiling the hypothalamic preoptic region. Science, 362 (6416) (6416)eaaau3324 2018.
Mauro J Muraro, Gitanjali Dharmadhikari, Dominic Grün, Nathalie Groen, Tim Dielen, Erik Jansen, Leon Van Gurp, Marten A Engelse, Francoise Carlotti, Eelco Jp De Koning, et A single-cell transcriptome atlas of the human pancreas. Cell systems, 3(4):385-394, 2016.
Giovanni Palla, Hannah Spitzer, Michal Klein, David Fischer, Anna Christina Schaar, Louis Benedikt Kuemmerle, Sergei Rybakov, Ignacio Ibarra, Olle Holmberg, Isaac Virshup, et al. Squidpy: scalable framework for spatial omics analysis. Nature methods, 19(2):171-178, 2022.
Franziska Paul, Ya' Ya'ara Arkin, Amir Giladi, Diego Adhemar Jaitin, Ephraim Kenigsberg, Hadas Keren-Shaul, Deborah Winter, David Lara-Astiaso, Meital Gury, Assaf Weiner, et al. Transerip- tional heterogeneity and lineage commitment in myeloid progenitors. Cell, 163(7):1663-1677, 2015.
Fabian Pedregosa, Gaël Varoquaux. Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-leam: Machine learning in python. the Journal of machine Learning research, 12:2825-288 2830, 2011.
Peter Quesenberry, Laura Goldberg, Jason Aliotta, and Mark Dooner. Marrow hematopoietic stem cells revisited: they exist continuum and are not defined by standard purification approaches; then there are the microvesicles. Frontiers in oncology. 4:56, 2014.
Anjali Rao, Dalia Barkley, Gustavo S França, and Itai Yanai. Exploring tissue architecture using spatial transcriptomics. Nature, 596(7871):211223)) 2021.
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, and Yvan Saeys. A comparison of single-cell trajectory inference methods. Nature biotechnology. 3765)-447-554 2019.
Ása Segerstolpe, Athanasia Palasantza, Pernilla Eliasson, Eva-Marie Andersson,
Andréasson, Xiaoyan Sun, Simone Picelli, Alan Sabirsh. Maryam Clausen, Magnus K Bjursell, et al. Single-cell transcriptome profiling of human pancreatic islets in health and type diabetes. Cell metabolism, 5444),593-607, 2016.
13

---

Published as a conference paper at ICLR 2026
Raman Sethi, Kok Siong Ang, Mengwei Li, Yahui Long, Jingjing Ling, and Jinmiao Chen. ezsin- glecell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists. Nature Communications, 15(1). 2024.
Kimberly Siletti, Rebecca Hodge, Alejandro Mossi Albiach, Ka Wai Lee, Song-Lin Ding, Lijuan
Hu, Peter Lönnerberg, Trygve Bakken, Tamara Casper, Michael Clark, et al. Transcriptomic diversity of cell types across the adult human brain. Science, 382(6667) 2023.
Tianci Song, Charles Broadbent, and Rui Kuang. Gntd: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations. Nature communications, 14(1):8276, 2023.
Artur Szatata, Karin Hrovatin, Sören Becker, Alejandro Tejada-Lapuerta, Haotian Cui, Bo Wang, and Fabian J Theis. Transformers in single-cell omics: review and perspectives. Nature methods, 21 (188):1430-144 2024.
Bosiljka Tasic Zizhen Yao, Lucas T Graybuck, Kimberly A Smith, Thuc Nghi Nguyen, Darren Bertagnolli, Jeff Goldy, Emma Garren, Michael Economo, Sarada Viswanathan, et al. Shared and distinet transcriptomic cell types across neocortical areas. Nature, 563(7779):72--78 2018.
Cole Trapnell, Davide Cacchiarelli, Jonna Grimsby, Prapti Pokharel, Shugiang Li, Michael Morse, Niall J Lennon, Kenneth Livak, Tarjei S Mikkelsen, and John L Rinn. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature biotechnology, 32(4):381-386, 2014.
Hongzhi Wen, Wenzhuo Tang, Xinnan Dai, Jiayuan Ding, Wei Jin, Yuying Xie, and Jiliang Tang. Cellplm: pre-training of cell language model beyond single cells. bioRxiv, pp. 2023-10, 2023.
Hongzhi Wen, Wenzhuo Tang, Xinnan Dai, Jiayuan Ding, Wei Jin, Yuying Xie, and Jiliang Tang- Cellpim: Pre-training of cell language model beyond single cells. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenRe- view.net, 2024. URL httptttpps///pppeeeeeeee
net forumridddoooooo BXXFFDDDDeuuududdd
F Alexander Wolf, Philipp Angerer, and Fabian Theis. Scanpy: large-scale single-cell gene pression data analysis. Genome biology, 19:1-5, 2018.
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. Autogen: Enabling next-gen LLM applications via multi-agent conversation framework. CoRR, abs/2308.08 2023. doi: (0.48550AARXIV.2308 08155. (ttttts::: /doi wrg/10...1000./... (0055000008500001.. arxiv 2308 08155
48550/
Yurong Xin, Jinrang Kim, Haruka Okamoto, Min Ni, Yi Wei, Christina Adler, Andrew J Murphy, George D Yancopoulos, Calvin Lin, and Jesper Gromada. Rna sequencing of single human islet cells reveals type 2 diabetes genes. Cell metabolism, 24(4):608-655, 2016
Chuan Xu, Martin Prete, Simone Webb, Laura Jardine, Benjamin J Stewart, Regina Hoo, Peng He, Kerstin B Meyer, and Sarah A Teichmann. Automatic cell-type harmonization and integration across human cell atlas datasets. Cell, 186(26):5876-889) 2023.
Grace XY Zheng, Jessica M Terry, Phillip Belgrader, Paul Ryvkin, Zachary W Bent, Ryan Wilson, Solongo Ziraldo, Tobias D Wheeler, Geoff McDermott, Junjie Zhu, et al. Massively parallel digital transcriptional profiling of single cells. Nature communications, 8(1):10049) 2017.
Juexiao Zhou, Bin Zhang, Guowei Li, Xiuying Chen, Haoyang Li, Xiaopeng Xu, Siyuan Chen, Wenjia He, Chencheng Xu, and Liwei Liu. An ai agent for fully automated multi-omic analyses. Advanced Science, 11(4)), 2024.
Yan Zhou, Dong Yang, Qingcheng Yang, Xiaobin Lv, Wentao Huang, Zhenhua Zhou, Yaling Wang, Zhichang Zhang, Ting Yuan, Xiaomin Ding, et al. Single-cell mna landscape of intratumoral het- erogeneity and immunosuppressive microenvironment advanced osteosarcoma. Nature com- munications, 1112:2322, 2020.
14

---

Published as a conference paper at ICLR 2026
CONTENTS
INTRODUCTION
RELATED WORK
3
METHODS
3.1
Task Planning and Hierarchical Decomposition
3.2
Task Execution via Reflectee Optimization Mechanism
3.3
Key Supporting Modules
EXPERIMENTS
Single-cell RNA Sequence Downstream Tasks
4.2
Spatial Transcriptomics Downstream Tasks
4.3
Human Centered Evaluation
8
5
CONCLUSION
ACKNOWLEDGMENT
11
The Use of LLMS
17
B Details of CellAgent
17
B.1 Framework Design
17
B.2 Evaluator and Participant Backgrounds
17
Self-reflective Optimization mechanism
18
Batch Correction
18
Cell Type Annotation
18
Trajectory Inference
18
spatial domain identification
imputation for spatial transcriptomics
19
C.6 Mechanisms for Mitigating Evaluation Bias and Ensuring Robustness
19
Evaluation Metric Calculations
19
D. I Batch Correction
19
D.2 Cell Type Annotation
D.3 Trajectory Inference
21
D.4 Spatial Domain Identification
22
D.5 Imputation for Spatial Transcriptomics
22
Supplementary Figures
24
Performance comparison of CellAgent with other methods
24
15

---

Published as a conference paper at ICLR 2026
E.2
Performance comparison of CellAgent with other LLM-driven agents
25
Memory Mechanism Optimization across Different Base Models in CellAgent
26
Details of the Planner
27
Details of the Executor
28
E,6
Details of automatic code exception correction by the executor
29
Details of the Evaluation Process)
30
E.8
Results on cell type annotation through dialogue
31
E.9
Results on trajectory inference through dialogue
32
E.
10
The performance of different methods on batch correction task
33
11
The UMAP visualization of CellAgent on five different datasets, colored by batch labels
34
12
The UMAP visualization of CellAgent on live different datasets, colored by cell type
labels
35
E. 13
The performance of different methods for cell type annotation across six datasets
36
14
UMAP visualization of CellAgent-inferne trajectories colored by pseudotime
37
.15
UMAP visualization of CellAgent-inferree trajectories colored by cell type
38
16
Visualization of DLPFC slices datasets
39
17
Predicted spatial domains of CellAgent DLPPC datasets
40
Supplementary Tables
41
The overview the se- Omni toolkit
Overview of the eight major tasks in scRNA seg and spatial transcriptomics analyses
42
Overview of batch correction datasets
43
Overview of cell type annotation datasets
44
FS
Overview of trajectory inference datasets
45
F66
Overview of spatial domain identification datasets
46
F7
Overview of spatial imputation datasets
47
F8
Overview of datasets used experiments
48
16

---

Published as a conference paper at ICLR 2026
A THE USE OF LLMS
In this work, LLMs were used solely in an assistive capacity. Their application was limited to aiding in code debugging and improving the language and clarity of the manuscript. All core scientific contributions, including the design of the framework, experimental results, and their interpretations, the original work of the authors. Any suggestions provided by LLMs were carefully reviewed and verified. The authors take full responsibility for all content presented this paper.
B DETAILS OF CELLAGENT
B.1 FRAMEWORK DESIGN
CellAgent utilizes collaborative framework of multiple expert-level large language models (LLMs) to achieve automated and high-quality analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data. Specifically, CellAgent leverages the expertise of three distinet roles: Planner, Executor, and Evaluator. This structured approach ensures that tasks are accurately decomposed and executed with minimal errors, providing reliable and professional results.
The Planner plays a crucial role in task decomposition. The system prompt explicitly defines the Planner's responsibilities and output format. It incorporates expert knowledge of scRNA-sce -seq and spatial transcriptomics analysis to enhance reliability and minimize hallucinations. The user input includes task descriptions, user preferences, and data details. Based on this information, the Planner accurately interprets user intent and formulates a comprehensive analysis workflow. Subsequently, it decomposes the workflow into sub-tasks and outputs them in JSON format (Figure 9.
The role of the Executor is explicitly defined by the system prompt, which outlines its responsi- bilities, required input, and output format (Figure TO). The Executor integrates essential expert knowledge regarding code generation constraints and toolkit usage to ensure stability. The provided instructions contain a structured task and data description, along with documentation of available tools, thus ensuring consistency with the current step. Based on these instructions, the Executor generates and executes the Python code. the event of errors, it automatically adjusts parameters using exception information to generate executable code (Figure ITI.
The Evaluator assesses code execution outcomes based on a designed self-reflection mechanism (Figure 12). Specifically, it integrates multimodal large language models (GPT-40)) to facilitate the automated assessment of batch correction, trajectory inference, and spatial domain identification. Regarding cell-type annotation, the Evaluator aggregates prediction results from multiple tools to generate the final cell type labels. In the context of spatial transcriptomics imputation, the Evaluator assesses the predictive performance of different imputation methods against known gene expression data in the current dataset. Specifically, it compares the predicted expression values with the actual gene expression to determine the most effective approach, then the selected method is applied to infer the expression of unknown genes, facilitating subsequent analyses.
B.2 EVALUATOR AND PARTICIPANT BACKGROUNDS
To ensure the scientific rigor and generalizability of the qualitative and quantitative assessments in this study (including Human Expert benchmarking, quality ratings, and usability evaluation). we re- cruited 20 researchers with varying levels of computational biology expertise. Ten of them formed the Expert Quality Assessment Panel, which was responsible for independently scoring (0-10) the outputs generated by CellAgent and the Human Experts. This panel consisted of master's 's and doc- toral students with 2-3 years of experience in melle-cell/spatial transcriptomics and relevant pub- lications; among them, five simultaneously served as Human Experts, performing manual analyses to provide benchmark results, while the other five focused on comparative scoring. In addition, an- other ten graduate students who had just begun working with single-cell data (less than one year of experience) participated in the usability evaluation. Together with the aforemeetiineed students, they formed the User Usability Assessment Group (n=20), which was tasked with evaluating the user- friendliness. facilitation, and interactive efficiency of CellAgent, the GPT-4 baseline, and the Online Webserver platform. This design enabled the capture of differential user perceptions and practical
17

---

Published as a conference paper at ICLR 2026
utility across experience levels, thereby ensuring the comprehensiveness and scientific robustness of the evaluation outcomes.
C SELF-REFLECTIVE OPTIMIZATION MECHANISM
C.I BATCH CORRECTION
CellAgent employs a Self-Reflective Optimization mechanism to invoke different batch correction methods (e.g., Harmony, sc and Liger) and determine the most effective method. Since batch correction outcomes cannot be directly interpreted through textual descriptions, the Evaluator quan- titatively assesses batch effect removal using batch correction metrics on batch labels, which are computed based on batch labels, including Graph Connectivity (Luecken et al. (022). PCR Com- parison (Luecken et al., 2022), iLLS iLSShhrpph (Korsunsky et al. 2019), kBET (Luecken et al. 2022 and ASWbatch (Buttner et al. 2019). These metrics comprehensively measure the degree of batch effect elimination across different correction methods. To further assess biological variance conser- vation after batch correction, the Evaluator, powered by GPT-4o, applies Leiden clustering on the corrected data and visualizes the cell distribution by UMAP, where cells colored based on Leiden cluster labels and assigns a 0-10 score based on cluster quality. This score weighted by a default coefficient of 0 and combined with batch correction metrics to compute the final performance score for each method. By default, the optimization process runs three iterations, each invoking a different batch correction algorithm, with CellAgent automatically selecting the gighest-scoring method for final output. To mitigate potential biases in LLM- -ased evaluations, we mask algorithm names and other extraneous details during the assessment. This ensures that the Evaluator's judgment is based solely on visual and performaace-rrlltee criteria rather than any inherent biases from the model's training data.
C.2 CELL TYPE ANNOTATION
CellAgent integrates both database-based annotation tools, such CellMarker 2.0 and ACT, and gene expression-based tools, such as CellTypist, to facilitate cell type annotation. Leveraging self- reflective optimization mechanism, CellAgent autonomously selects and executes multiple annota- tion methods, generating predicted cell type labels. The Evaluator, powered by GPT-44 first collects these annotations together with cluster-level differentially expressed gene (DEG) information and then outputs the final cell type assignments to enhance annotation accuracy.
C.3 TRAJECTORY INFERENCE
The evaluation of trajectory inference follows similar approach to batch correction assessment. Considering the complexity of describing trajectory information through text alone, the Evaluator scores the visualization results of different trajectory inference algorithms and selects the highest- scoring outcome the final output during CellAgent's iterative optimization process. Trajectory inference visualization consists of three concatenated images, each providing distinct insights: (1) UMAP plot with trajectories colored by cell types; (2) a UMAP plot displaying cell type clus- ters optimized based on trajectory milestones; and (3) UMAP plot colored by pseudotime. This multi-layered visualization strategy enhances the semantic richness of trajectory representations, improving interpretability. To further refine the evaluation, expert-curated knowledge inference tasks is incorporated into the Evaluator's prompts, directing the Evaluator's 's attention to the biological validity of developmental trajectories among different cell types in the plots and the continuity of the inferred trajectories. Additionally, to ensure faimness and eliminate bias, algorithm names masked during evaluation, allowing the selection process be driven solely by objective performance metrics.
trajectory
C.4 SPATIAL DOMAIN IDENTIHICATION
CellAgent applies multiple spatial domain identification algorithms, such as DeepST, stLear, and SEDR, autonomously selecting and executing the most suitable method generate spatial domain identification results. The Evaluator, powered by GPT-4o, analyzes spatial domain images predicted by different algorithms, which generated by spatial visualizations based predicted clustering
18

---

Published as a conference paper at ICLR 2026
assignments, alongside original pathological images. Utilizing semain-specifie knowledge of spa- tial partitioning. the Evaluator assesses the clustering quality within identified spatial domains and the smoothness of spatial domain boundaries, assigning scores to the spatial domain images pro- duced by each algorithm on a 0 to 10 scale. The highest-scoring algorithm is then selected, and its corresponding spatial domain image is designated as the final result for spatial domain identification.
C.5 IMPUTATION FOR SPATIAL TRANSCRIPTOMICS
CellAgent initially selects a subset of genes with known expression, which serves as both the training and validation set. These genes, known highly variable genes (HVGs), exhibit high expression variability in both the spatial transcriptomics dataset and the reference single-cell RNA sequencing data. CellAgent then employs range of imputation methods, such as Tangram, gim VI, and SpaGE. To evaluate performance, the Evaluator compares the predicted expression values on the validation genes with their actual expression values. Different algorithms are then assessed based on their performance on this dataset, calculating the Accuracy Score (AS). The scorrng algorithm identified as the most promising. and subsequently, CellAgent invokes this method predict the expression of unknown genes.
C.6 MECHANISMS FOR MITIGATING EVALUATION BIAS AND ENSURING ROBUSTNESS
The Self-Reflective Optimization Mechanism in CellAgent operates by running multiple candidate pipelines for each analysis step, scoring their outputs, and iteratively refining tool choices and hy- perparameters. The tpsk-specific scoring functions and optimization loop are described in detail in the main text; here we focus on how this mechanism is implemented to minimize evaluation bias and ensure robustness.
Because the Evaluator is itself an LLM that both interprets results and selects preferred tools, there a natural risk of evaluation bias and self-cireularity- example, favoring methods whose names are frequent in its pre-training data or implicitly justifying its own earlier decisions. To counteract this, we adopt three complementary safeguards. First, all algorithm identifiers are masked before evaluation: method names, implementation-spee specific strings, and hyperparameter presets replaced by generic labels ( ("Method A/B/C"), preventing the Evaluator from exploiting popularity or brand recognition of specific tools. Second, execution and evaluation memories are strictly separated. The Executor has access to full code, stack traces, and detailed logs for self-debugging, whereas the Evaluator's 's context is restricted (1) dataset-level summary, (2) anonymized candidate identifiers, (3) the corresponding task-specific metrics, and (4) rendered diagnostic plots, so it cannot simply rationalize the underlying code or prompts. Third, we empirically assess cross-model robustness by instantiating CellAgent with GPT-40, Qwen3-Vin VL-235B, and Owen3-Ommi-300 3OB.. Across five core downstream tasks (batch correction, cell-type annotation, trajectory inference, spatial domain iden- tification, and spatial imputation), the aggregate scores vary only modestly and, crucially, the rank- ing of preferred tools remains stable, supporting the claim that the Evaluator's decisions are driven primarily by task-specific metrics and diagnostics rather than idiosyncratic biases of particular backbone.
D EVALUATION METRIC CALCULATIONS
D. BATCH CORRECTION
We adopted the evaluation metric calculations outlined by Luecken et al. (2022). in their benchmark study. Each metric is described below.
NMI. To quantify the concurrence between the cell type labels based on ground truth and the cluster labels obtained from integrated cell embeddings, computed the normalized mutual information (NMI) score. NMI scores of 0 or correspond to uncorrelated clustering or perfect match, respec- tively.
ARL The Rand index compares the overlap of two clusterings; it considers both correct clustering overlaps while also counting correct disagreements between two clusterings. Similar to NMI,
19

---

Published as a conference paper at ICLR 2026
compared the cell-type labels with the NMI-optimized clustering computed the integrated dataset. An ARI of 0 or corresponds to random labeling or a perfect match, respectively.
ASW. The silhouutte width assesses the relationship between cell's within-cluster distances and its distances to the closest cluster boundaries. By averaging the silhouette widths of all cells, we calculate the average silhouette width (ASW) score. This score ranges from -1 to where a score of 1 indicates well-separated clusters, while scores from -1 to 0 suggest overlapping clusters and misclassification. For evaluating cell type clustering, we compute the ASW score based on cell type labels, represented as ASW colt. To obtain this score, we utilize the following formula:
ASW call (ASWc + 1)/2
Here, C represents the cell types.
Regarding batch mixing evaluation, we calculate the ASW score considering batch labels and adjust by subtracting This score is denoted as ASW batch- The calculation as follows:
ASW batch = |ASW B
Both ASW cell and ASW batch have values between 0 and 1. Higher scores indicate better cell-type clustering or batch-mixing performance.
Graph connectivity. The graph connectivity metric quantifies the average proportion of cells within each cell type connected through kNN(K- nearest neighbors) graph. For every cell identity in the set C. we compute the size of the largest connected component using kNN among cells exclusively belonging to identity c. This value divided by the total number of cells with identity с to obtain normalized measure. The GraphConn(Graph) connectivity) score is then reported as the average across all cell types:
LCC (GANN)
1
GraphConn
IC|
N
oEC
Here, LCC represents the largest connected component, and N denotes the number of cells of each cell type.
Principal component regression. Principal component regression, derived from PCA. has pre- viously been used to quantify batch effect removal. Briefly, the R² was calculated from a linear regression of the covariate of interest (for example, the batch variable B) onto each principal com- ponent. The variance contribution of the batch effect per principal component was then calculated as the product of the variance explained by the principal component(PC) and the correspond- ing R2 (PC, B). The sum across all variance contributions by the batch effects in all principal components gives the total variance explained by the batch variable as follows:
Var(C B) B)= Var(CIPC) x R (PC
where V Var(CIPC.) is the variance of the data matrix C explained by the ith principal component.
kBET. The kBET algorithm (v.0.99.6, release 4c9dafa) determines whether the label composition of a k nearest neighborhood of cell is similar to the expected (global) label composition. The test is repeated for a random subset of cells, and the results are summarized all tested neighborhoods. Here we use the kBET(extended by Luecken et al. (2022)) to compare integration results on kNN graphs irrespective of the integration output format. This score ranges from O to 1, where a score of indicates successful removal of batch effects, while score of 0 suggests a poor performance.
rejection rate over
Graph LISI. The LISI, diversity score, was proposed to assess both batch mixing(iLISI) and cell- type Separation(cLISI), LISI scores are computed from neighborhood lists per node from integrated KNN graphs. Specifically, the inverse Simpson's 's index determines the number of cells that can be drawn from a neighbor list before one batch is observed twice. Thus, LISI scores range from 1 to N, where N is the total number of batches in the dataset. Here by Luecken et al. (2022) as cLISI where a 0 value corresponds to low cell type separation.
the LISI scores extended
iL.SI Graph, where () value corresponds to low batch integration.
20

---

Published as a conference paper at ICLR 2026
Aggregated metries. The aggregated metric AugBatch computes the average of batch effect moval metrics:
AugBatch = (ASW batch + iLLSIGraph + GraphConn + kBET PCR comparison)/5
The aggregated metric AugBio computes the average of batch effect removal metries:
AugBio (Isolated labels + NMI + ARI + ASW cell cLISIGraph)/5
The Overall score, derived from averaging these metries, serves the final performance indicator:
Overall AugBatch 0.4 AugBio 0.6
D.2 CELL TYPE ANNOTATION
To assess the accuracy of cell type annotations and facilitate comparisons across various methodolo- gies, we employ an average scoring metric (Hou & Ji] (024).. For predicted and actual labels, "fully match" is awarded when they directly align in terms of annotation terms or CL cell ontology names. "partially match" occurs when labels share a general cell type category but differ in specific annota- tions or ontology names. Conversely, "mismatch" is declared when there is a discrepancy in broad cell type categories, annotations, or ontology names. We assign consistency scores of 1, 0.5, and O for "fully match," " "partially match," " and "mismatch" respectively (Hou & Jil 2024). These scores are then averaged across all cell types within each dataset. This average score serves as quan- titative measure of annotation accuracy, enabling robust comparisons between different annotation methods and datasets. This systematic scoring approach ensures that researchers objectively sess the precision of cell type classifications and refine their analytical methods or data interpretation needed.
Averogescore
x "fully match" + 0.5 x "partially match" + +0x "mismatch"
D.3 TRAJECTORY INFERENCE
We adopted the evaluation metric calculations outlined by Saclens et al (Saclens al. 2019). in their benchmark study. Each metric is described below.
Correlation between geodesic distances. cordist measures the correlation between geodesic dis- tances. When the position of cell is the same in both the reference and the prediction, its relative distances to all other cells in the trajectory should also be the same. This observation is the basis for the cordist metric. The geodesic distance the distance a cell has to go through the trajectory space get from one position another. The cordist score ranges from to 1. A higher stronger correlation between geodesic distances.
indicates
Edit distance between two trajectory topologies. We used the edgeflip score to assess the similar- ity in topology between two trajectories, regardless of where the cells were positioned. The edgeflip score defined the minimal number of edges that need be added removed transform one network into the other, divided by the total number of edges in both networks. This problem is equivalent to the maximum common edge subgraph problem, providing a quantitative measure of structural differences between trajectory topologies.
The quality of clustering within the trajectory. F1 branckes used to evaluate the clustering quality of cells within the trajectory. The simplest way to calculate the similarity between the cellular positions of two topologies is by mapping each cell to its closest branch. These clusters of cells can then be compared using one of the many external cluster evaluation measures. The FI score maps two cluster sets by using their shared members based on the Jaccard similarity. It then calculates the Recovery the average maximal Jaccard for every cluster in the first set of clusters. Conversely, the Relevance is calculated based on the average maximal similarity in the second set of clusters (in our case, the prediction). Both the Recovery and Relevance then given equal weight in harmonic
21

---

Published as a conference paper at ICLR 2026
mean (FI). Formally, if C and C' are two cell clusters:
lendll
Jaccard (c, c') cCd
Recovery |C|
1
Σ
max Jaccard (c, (', dec'
CEC
Relevance
max Jaccardc, c')
|C' d'Ec'
aEC
FI
Recevery
To calculate Fl branckes cells are mapped to the closest edge within the trajectory, ensuring that the branch assignments reflect the structural similarity between the two topologies.
The accuracy of dynamical differentially expressed genes. jeanures measures the accuracy of dynamically differentially expressed genes. The main advantage of studying cellular dynamic processes using single-cell data is that the dynamics of gene expression can be analyzed across the whole transcriptome. This enables the construction models such as dynamic regulatory networks and gene expression modules. Such analyses rely on sufficiently accurate cellular ordering to identify dynamically differentially expressed genes. To prioritize the most important differentially expressed features, implemented wCoTJeatuess which weights the correlation using the feature importance scores in the reference. This metric ranges from 0 to 1, where higher score indicates a stronger correlation, reflecting the ability of the method accurately capture the dynamics of gene expression.
Aggregated metric. To provide the overall metric, therefore chose metric which weighs every aspect of the trajectory equally: cor accessing ordering; F1 branches accessing branch assign- ment; edgeflip accessing topology; jootree accessing the accuracy of differentially expressed features. The final overall score for method was thus defined as:
Overall
V CoT X edgeflip F1 branches X WCOT FCOTeeatees
D.4 SPATIAL DOMAIN IDENTIFICATION
To assess the accuracy of spatial domain identification, we use the adjusted rand index (ARI) as evaluation metric. ART is measure of the consistency between clustering results and reference labels, ranging from -1 to A value indicates perfect agreement, 0 indicates random agreement, and negative values indicate disagreement. We use the following formula calculate the ARI:
Eg (T:)
Σ. (
Σ 43)
(G)
ARI
(G) - 1/
where n is the total number samples, ny is the number of samples that belong to both reference cluster and predicted cluster j, is the number of samples in reference cluster and b, is the number samples in predicted cluster j. By calculating the ARI for each dataset, we can quantify the accuracy of spatial domain identification.
D.5 IMPUTATION FOR SPATIAL TRANSCRIPTOMICS
We adopted the evaluation metric calculations outlined by Li et al. (Li et al. 2022 in their bench- mark study. Each metric is described below.
PCC. Quantifies the correlation between the ground truth and predicted spatial expression vectors for each gene. higher Pearson correlation coefficient (PCC) indicates better prediction accuracy:
PCC = E [(ā ū.) (x; u,)]
0:0
where x; and 2; are the spatial expression vectors, u, and ü, are their means, and o; and ã; are their standard deviations.
22

---

Published as a conference paper at ICLR 2026
SSIM. To evaluate the similarity of scaled gene expressions, with values in [0, 1]. Expressions scaled as:
FAj
max TiM)
Structural Similarity Index Measure (SSIM) is caleulated as:
SSTM (2ū.u: + (2)((2cv((z) C2) (ug + u/ + ( + C2)
where cov(a,, X,) is the covariance, and C1 0.01, C2 0.03 are stability constants. A higher SSIM indicates better prediction accuracy.
RMSE. Root Mean Squared Error (RMSE) calculates the error between the z-scores of the ground truth and predicted spatial expression:
1 M
RMSE
Fig
3zz)²
j=1
where By and 3i are 2-scores. Lower RMSE indicates better prediction accuracy.
JS. Jensen-Shannow (JS) divergence measures differences in spatial distribution probabilities. The probability computed as:
Fzj Pg
Σ 2== Tij
The JS divergence is calculated as follows:
1 JS KL
P. + P.
1 KL
Pt
P:
P.
Pal
2
where the Kullback-Leibler divergence KL(aa(b is defined as:
M
KL(allb) Σ 1=1
ag log
aj
by
lower IS value indicates better prediction accuracy.
Aggregated metries. AS combines four metries: PCC, SSIM, RMSE, and JS evaluate overall performance, The methods are ranked as follows: ascending order for PCC and SSIM, where higher values indicate better performance, and descending order for RMSE and JS, where lower values indi- cate better performance. The method with the highest RMSE/IS value is assigned RANK = while the method with the lowest RMSE/JS value is assigned RANKRMSEлS with the highest SSIM/IS value is assigned RANKSSIM/JS = 1, while the method with the low- est SSIM/JS value assigned RANKSSIM/IS N. Finally, we calculated the average value of RANKPcC, RANKsSIM. RANKRMSE. and RANK15 to obtain the AS value of each integration method, as follows:
The method
AS = 7 (RANKPCC + RANKsSIM + RANK + RRAN1s)
A higher AS value indicates better overall performance.
23

---

Published as a conference paper at ICLR 2026
E SUPPLEMENTARY FIGURES
E.1 PERFORMANCE COMPARISON OF CELLAGENT WITH OTHER METHODS
Methods
CellAgent
GPT-4
Online Webserver
Single-cell FMS
Task-specific Tools
High-quality Analysis
No manual
Coding
Self-apimizing Execution
language
Interaction
Figure 6: Performance comparison of CellAgent with other methods. Comprehensive compari- son of CellAgent, GPT-4, online web servers, single-cell foundation models (single-cell FMss) and task-specific tools.
24

---

Published as a conference paper at ICLR 2026
E.2 PERFORMANCE COMPARISON OF CELLAGENT WITH OTHER LLM-DRIVEN AGENTS
Spatial Imputation
Domain identification
Celltgent
Agent (Qwend (L-2388)
Collgent (Qwenā Omni 308)
Trajectory inference
AutoBA
0.50
BIoMAAIA
Cell Type Annotation
0.85
0.0
0.2 0.4 0.6 0.5
1.0
Figure 7: Performance comparison of CellAgent with other single-cell analysis agents. We evaluated the performance across five downstream tasks: Spatial Imputation, Domain Identification, Trajectory Inference, Cell Type Annotation, and Batch Correction. The x-axis represents the per- formance metric score (see Appendix D.I). CellAgent, partieularly the version powered by GPT-4o (indicated by red diamonds), consistently outperforms baseline methods.
25

---

Published as a conference paper at ICLR 2026
E.3 MEMORY MECHANISM OPTIMIZATION ACROSS DIFFERENT BASE MODELS IN CELLAGENT
Colkgenn((PP-oo)
1ock
164 6
Figure 8: Memory Mechanism Optimization across Different Base Models CellAgent. We evaluate the performance gain in task exccution success rate provided by the memory optimization mechanism across eight analysis tasks (detailed in Table and three distinct base models. The gray dots represent the baseline success rate of the model without memory optimization, while the colored markers and annotated numerical values display the performance gain contributed by enabling the memory optimization mechanism.
26

---

Published as a conference paper at ICLR 2026
E.4
DETAILS OF THE PLANNER
Planner
CollAgent
Role
You are an expert the field biological analysis. You need to decompose a required task (such as debatch, cluster, into several sub steps. to consider the requested the final task.
System prompt
Expert Knowledge
The set of all possible sub-steps (specifii task components) contains: (Qualitty Control, Normelization, identification of Correction, Clustering, Type Annotation,
Highly
Dimensionality Reduction, Coll Communication Analysis, Trajectory inference).
The following attempts is reference:
Batch Effect correction only necessary for data with batch effects (there are batch-related labels such as "batch" "sampic" the dota). Note that Batch Effect Correction also known "debatch". Batch Effect Correction should be
Output
Use ordered lists in JSON format provide overall steps, along with appropriate explanations.
Instruction
Task Description: Using the Adata data cell annotation.
User Requirements: No need for steps of Quality Control and Normalization. Description: _t____ug___hhhadddppp
Data Format: AnnData object with n_obs n_vars 32472 32566
'location',
"patienttroup', 'percent.mite', 'protocol", 'donor'
var: igene ids'
layers:
Please provide sub-steps to complete the task. Note that there are user-provided requirements, they must be satisfied.
Sson
Planner
"step": 1, "nane"; "fdantiiication of Highly Variable Genes", "description": "Identify and salect which are often inforative for distinguishing cell types and states."
exhibst high variability colls,
"step": 2,
"name" : "Batch Effect Correction",
"description": "Corract for batch effects in the data to ensure that variations due to different batches confound the biological signal."
"stop":
"nane": "Dinassionaltty Reduction", "description": "Reduce cinensionality dataset capture most significant features of the data in Lover-dinensional space, often using nethods like PCA, t-SNE, or UPAP.
stepp" 4,
naae" "Clustering", 'description": "Group cells into clusters based their idantify distinct cell populations."
profiles
"step":
"naee" "Cell Type Armotation",
"descriation:: "Assign cell type labels to the identified clusters based known narker reference datasels."
Figure 9: Details of the Planner. The system prompt defines the Planner's role and output for- mat while integrating domain-specific knowledge of single-cell analysis, including key sub-steps and task-specific constraints, which helps mitigate hallucinations, ensuring accurate and profes- sional task decomposition. Structured instructions refine user input into itemized content, enhancing workflow efficiency. Additionally, the instructions specify parsed data format, enabling precise in- terpretation input data. Adhering to these guidelines, the Planner outputs decomposed sub-tasks in JSON format.
27

---

Published as a conference paper at ICLR 2026
E.5
DETAILS OF THE EXECUTOR
Executor (Code Prognommer)
Rale
System
You are an expert the field of single-cell analysis. Please help me generate code to handle with the specific task.
Input & output
give you overall tasks, description current step, as some
might
be used in the current step.
You need provide code to solve the current step task based on these tools (if provided).
You should
code together in one block, such
[Analysis about the code python
process]
[Code Description]
(code)
Expert Knowledge
Here are the rules you must follow:
generated code should
any placeholders,
able be directly.
Note that cfg has already been properly defined, with keys: (img, (img_seve, _eve_dir, output dir)
map debatch map, you should umap before running sc.pl.umap.
you are using sc.pl to plat, can not use the 'save' parameter. Instead, use plt.savefig)).
Instruction
stegs Quality Data Description: hum, lung_pp.hSad
Requirements:
Normalization.
Data Format: [AnnData object with n_obs n_ vars x 2000,
The code has been ran: [Code in Memory module]
Batch Effect Correction.
Here the docstring for some suggested tooks: [Dacument about suggosted tools] Generate complete
please.
python
Execufor
Effect Correctian Hamrony
Import scanpy
inport matpiotlih.ppple pyplat as pit from deols.debatcher import Debatcher
Instantiate the Debatcher class
debatcher Debatcher(atlata, batche 'batch", efg)
Apply Harnony batch correction adata debatcher. harmany()
Harrony corracted odata.obsx["X ("_pcc"""X"""") adata.obse["X cbssn'X_pcct ppc_aaaa__aapaaa 1___pc___hmoo'')
Updato
Corpute the neighborhand graph using the corrected PCA
app.reighberseadate (adata,, 1ueeigheers-19,
"Xpca')
Perfoom Leiden clustering
sc.tl. L.t..eedenedddta.. resolution=1)
Run UMP for visualization (aappaaa)))
Plat the UMAP to visunlize the batch effect corraction sc.pl..uaap (adata,, color- "hatte""aatch', "Ieiden'], show-False) Aat..svvFiggf"(eee si__save_ddrr______ sir')))/map_plos citrrpppooo_ccmacted.ooom coorratedd.pgg")
Figure 10: Details of the Executor. The system prompt explicitly defines the role, required in- put, and output format of the Executor while integrating essential expert knowledge. This expert knowledge includes various constraints on code generation and the use specific tools enhance the stability of the generated code. The instructions provide a structured description of the task and data. Additionally, the documentation of available tools for the current step is included, ensuring consistency with the tool list retrieved by the Executor. Following these instructions, the Executor generates and outputs the Python code.
28

---

Published as a conference paper at ICLR 2026
E.6 DETAILS OF AUTOMATIC CODE EXCEPTION CORRECTION BY THE EXECUTOR
Executor (Code Prognommer)
User Requirements: Do trajectory inference on this data directly, without any other steps. Description: eycle Buettner
Instruction
Data Format: AnnData object with Lobs vars 264 x 6812 obs: LUbraryName', callecycle_sst stage', 'celf,, 'groupingg The code been [Code Memory module]
The current step Trajectory inference
Here is the docstring for some suggested tools: (Document about suggested tools) complete
step, please.
python
Executor
Perfoms trajectory inference on the given AnrData abject using the spectfied tools.
Initialize TrajectoryInferecee
object configuration ensjectoor_iinfeene Trejjettor/infrrenceaddtt cfi)
trajectory inference executed sesjectory.infereee
'milestone id' colurn in adata.obs clusters' "rouupigg ')
Instruction
An exception was thrown while running the code you generated. Here are some descriptions of the exception:
Exception:
traceback:
ValueError
Traceback (most recent last) <modules
Initialize the TrajectaryInference class with the AnnData abject and configuration
--> trajectory_ inference Trajecttrynffrrennasdaht rfg)
ValueError: Did not find. layers/"counts"..
modify resalve exception.
python
Executor
trajectory inference given object specified
Ensure the raw counts are stored in separate layer adata layens" layyers("ccunts") adata copy()
Initialiie the Trajectarytnforrnce class with the AnnDota object and configuratiin trajectory inference Trajectooytnnfeennccsaatt cfg)
trajectory inference using 'grouping' column adata.obs excuuted_nethor wrajectoy_inferent _tt.eeeeeeeeuunnnts________ run(obs_clusterr' (untabb_ccustter-rrooinnggg
Figure 11:
Details of automatic code exception correction by the executor. The Executor first outputs code that can not be successfully executed. This exception is then prompted to the Executor. Based on the exception information, the Executor automatically adjusts parameters generate code that can be executed correctly.
29

---

Published as a conference paper at ICLR 2026
E.7
DETAILS OF THE EVALUATION PROCESS
Evaluater
CellAgent
Role
System prompt
helping
processing single-cell sequences.
Input output
will provide you with the current Additionally, will give you the code that has already been executed and the information "adata" variable after running code. You need to provide code evallaae whether the current reesonable. You just need evaluate based the code provideed.
Attention, you get your together, should included in block python
Expert Knowledge
Attention, the following code must be used for specific steps must not be applied other steps. To evaluste the result of batch correction, utils, dtib.ch evallute(inner, info iter, info,
(batch. informatian), cfg)
To evaluate trajectory inference,
dig):
Instruction
User Requirements: trajectory inference
directly, without any ather stops.
Data Description: grlig-hs--young_kae ko_akozwwkkkk
n, vars
abs: 'age', 'milestone id', 'rep', 'bulk, 'grouping""
layers:
The code has been ran: (Code Memory module)
The current Trajectory inference
This step has beed completed, please generate code evalunte the current step.
python
Evalustor
# inner info inno list and cfğ are already provided, you should use ther diractly. result, max_iter, iiner_info_liste cfg)
Sandhox (Output)
images of different trajectory inference mothods have been loaded.
result trajectory inference:
("rates's ( ("method_1's 7, 'rethod_2's 8, 'sethod33'; 2), 'best': 'method_2")
method_2, slingshot.
HBG
Une
Peasdeliie
Details of the Evaluation Process. The system prompt for the Evaluator specifies its role, required inputs and outputs, and the essential expert knowledge necessary to enable the effec- tive utilization of the self-reflective optimization mechanism. At each step, the Evaluator receives a structured description of the current task and data, which it subsequently analyzes to assess the corresponding results.
Figure 12:
30

---

Published as a conference paper at ICLR 2026
E.8 RESULTS ON CELL TYPE ANNOTATION THROUGH DIALOGUE
To meet requirements, visualized the specific expression the LILRA4 and CD79A genes different cell types.
LIRAA4
4.0
3.5
20
200
1.
1.5
1.0
0.5
000
UMSAP1
UMAPI
ULRM
CO79A
3.5
25
1,5
05
Dende Hi cale
NK cols
Nate cats
Navve CD4+ cets
Nelve oyctosee cwls
Ploereoooois Dendittc oats
Pabiinss
CDS- NKT io colls Dinni tc cets
Monorytes
NK cats
Nake cals
Natvv CD4 cats
Plos marylol Dendr cots
Figure 13: Results on cell type annotation through dialogue. After cell type annotation, CellA- gent provided a visualization of LILR4A gene (PDCs marker) and CD79A gene (B cells marker) expression across clusters using UMAP plots. The violin plot further illustrates the gene expression distribution within these cell clusters.
31

---

Published as a conference paper at ICLR 2026
E.9 RESULTS ON TRAJECTORY INFERENCE THROUGH DIALOGUE
trajectory differentially expressed genes previously platted clear biological significance. Pleese help me re-run the trajectory inference calculate the differential genes.
3 images of different trajectory inference methods have been loaded. The evaluation result for trajectory inforence: Trates:: (ratennce:trates: 'best:: 'melhad. 2jBest methodi mothod_2 slingshot.
('method 1: mmthhod 2: 2, 'method_ 3: 2).
cowery owd
this visualization preeents the Top 20 trajectory differentially expressed genes, identiffie by dynfeature, package that datects verious types of differential expression along the trajectory.
Mond
Qcea7
Mond
Mon7
Tubeib
Stort
Eth
Cd34
Pladd
Moo
satisfied
gradual upregulation validates
the HSC differentiation trajectory.
Figure 14: Results on trajectory inference through dialogue. In response to user dissatisfaction with the initial trajectory differential expression results, CellAgent automatically evaluated three distinct trajectory inference methods and selected the highest-scoring one (Slingshot). It then visu- alized the top 20 differentially expressed genes along the refined trajectory.
32

---

Published as a conference paper at ICLR 2026
E.10
THE PERFORMANCE DIFFERENT METHODS ON BATCH CORRECTION TASK
0.6
0.4
CollAgont
scV1
liger
Scenorame
Harmany
Combat
SCGPT
GaIPLM
b
Bio cur Seniiin
Baich comection
Augregals scure
Method
coloped
ARI
ARM
CLUS
compar
coroaiviiion Oveall
CellAgent
0.63
9.67
0.55
0.99
0.50
07
0
scVI
0.6
3.66
0.45
0.54
0.99
0.90
5:33
3,40
0.8
0.77
Lige
0.50
5.56
0.24
Scenorama
0.60
Harmony
CelIPMM
Figure 15:
The performance of different methods batch correction task. a, Violin plot shows the distribution overall score between CellAgent and other batch correction methods across all datasets. b, This figure presents the benchmarking results of CellAgent alongside Liger (Liu et al.
2020). Vc (Lopez et al. 2018), Scanorama (Hie et al. 2019), Harmony (Korsunsky et al. 2019), CelIPLM (Wen al. 2024), scGPT (Cui et al. 2024). and Combat (Johnson al. 2007 across five datasets, evaluating their average performance based on multiple metrics related to biological conservation and batch effect removal. The biological conservation assessment includes Isolated
Labels (Korsunsky et al.] 2019). NMI (Pedregosa et al. (01IT). ARI (Hubert & Arabie 1985), AW Soell (Büttner et al. 2019). and cLLSIceu (Korsunsky et al. 2019). which measure the ex- tent to which the biological structure is preserved after integration. The batch correction eval- uation comprises Graph Connectivity (Luecken et al. (022). PCR Comparison (Luecken et al,, 2022). KBET (Luecken et al., (2022), iLTSTmatch (Korsunsky et al., 2019), and AW Ahateeeh (Büttner al. 2019), reflecting the effectiveness of removing batch effects. The results are visualized us- ing heatmaps. The aggregate scores on the rightmost panel summarize the overall performance of each method, with batch correction and biological conservation scores weighted to compute the final overall score.
33

---

Published as a conference paper at ICLR 2026
E.11 THE UMAP VISUALIZATION OF CELLAGENT ON FIVE DIFFERENT DATASETS, COLORED BY BATCH LABELS
Immune Ihuman and mouse)
10x
MC1_P8_1
Lung
Dahlin_1
Dahlin 2
KCAJP4 P8_6
Dahlin_3
Octjon,
Dahling 4
Oetjen_P
Treyaag
Octjon U
MCA,
NCA_DM_
Sun, sample2 sc
MCA_sM_
sanpsame TB
A1
sammpls TC.
KCA_P9,2 2
viliaii
UMAP1
LIMA1
Immune (human)
Perirhinel Cortex
Preytag
Oeljen_P
Octjon U
10X222,1
Sun sample2, KC
3_
Sun jasmlsmmpp_r..
Villaii
UHAP1
UMAP1
colsog
celseqz
fuidigmet
hDropp
iDroo2
huropt
smarter
UIAP1
Figure 16: The UMAP visualization of CellAgent on five different datasets, colored by batch labels. Each point represents cell, and the colors correspond to different batch labels, indicating the source the samples each dataset. The visualization demonstrates the clustering of cells across different experimental conditions.
34

---

Published as a conference paper at ICLR 2026
E.12 THE UMAP VISUALIZATION OF CELLAGENT ON FIVE DIFFERENT DATASETS, COLORED BY CELL TYPE LABELS
Lung
calls
Heeccyle prppeTOO
Macrourage
Dox mouid proponiin
Coll Toids
Book
Greste
Men Mesimoe (L30
Hgy was ingreiters
Pierrrytuic coit
Ondiohal
"brsorant
Typm
Type
Pegulurypry
Immune thamani
Perthird Cotter
CDd+ Tcals
HSSCC
encendd cical
Plap SAPPOTT SIRAEROO
Hencoste-eries CRESITE oR cos
(accea scocites creath muade
Coll+ Porsoos
mocooo
Picara
FTy was zaggesous
syrem mormeeag
(M4F))
UWA
andobhll
Figure 17: The UMAP visualization of CellAgent on five different datasets, colored by cell type labels. Each point represents a cell, and the colors correspond to different cell types identified within each dataset. The visualization demonstrates the clustering of cell across diverse biological contexts.
35

---

Published as a conference paper at ICLR 2026
E.
THE PERFORMANCE DIFFERENT METHODS FOR CELL TYPE ANNOTATION ACROSS SIX DATASETS
Human Lung Dataset.
Dataset
0.5
00
Galhpne
Apr
Cestemer
hrpp
staee
Dalpel
D0
Galipel
MISHT
AB?
Coltose
3053
Galmeet
0.0
Gatterke
Scitt
ScsA
Hurren Pancress Dalasel
Human PBMC Dalaset
Human Iimune Datazel
1.0
0.8
0.6
0.5
0.5
0.4
0.3
0.4
02
0.2
0.
Gealert
Delges
Galtete
Coboent
RGT
scipe
4c34
Dupyin
Figure 18: The performance of different methods for cell type annotation across six datasets. The bar chart shows the average accuracy of CellAgent compared to other different cell type anno- tation methods across six different datasets.
36

---

Published as a conference paper at ICLR 2026
E.14 UMAP VISUALIZATION OF CELLAGENTINFERREED TRAJECTORIES COLORED BY PSEUDOTIME
cellbench-SC_ Juyitia
pseudotime
pseudotime
pseudotime 25 50500.5
0.00 0.25
3.0 0.4 0.8
12
1.6
germline-hmann-tohh.o
cell-cycla_busttree busttner
penceerii-aaphh--lllm-tttabooo z_angg
pseudotime
0.00 25 0.50
pseudotime
0.00 0.25 o 50 0.75
pseudotime
0.00 0.20 0.500.55
NKT-differentiation engel
hsc agina-hsc_kowalccyk
pseudotime
pseudotime
0.0 0.5 1.0 2.0
10
Figure 19: UMAP visualization of CellAgent-inferree trajectories, colored by pseudotime. Each subplot represents a distinct dataset, where dots correspond to individual cells, and colors indi- cate pseudotime progression, with darker colors denoting earlier states and lighter colors indicating later stages. The inferred trajectory paths capture the developmental or differentiation progression within each dataset.
37

---

Published as a conference paper at ICLR 2026
E.15
UMAP VISCALIZATION OF CELLAGENTINFEEREDD TRAJECTORIES COLORED BY CELL TYPE
cellbench-SC, kuyllinn
hew.iiieedddeeddiiiccccceeeeee ceapdittiiicccttPPPP. shalee
Embryonic day3
Embryonic day4
Embryanic days
PiCH1h
PICH2h
PiCrch
Embryonic days
Embryonic day7
Unsttmulated
germiine-huaan-oll..g
cell-cycle buellner
pancreatic- caaesalic-ilhh--eleeee hitp-ii---------lllllaaaattaeeae celllaaiilll zhang
NKTT-diferrnniatio_.og _engell
aging hsc. kowalezyk
NTKO
NTK2
ST-HSC
MPP
Figure 20: UMAP visualization of CellAgent-inferred trajectories, colored by cell type. Each subplot represents distinct dataset, where dots correspond to individual cells, and colors indicate cell type. The trajectory covered various biological processes and the inferred trajectories capture the developmental or diffcrentiation pathways within each dataset.
38

---

Published as a conference paper at ICLR 2026
E.16 VISUALIZATION OF DLPFC SLICES DATASETS
109t
11/033
Figure 21: Visualization of DLPFC slices datasets. The DLPFC layers were annotated by Maynard et al. (Maynard et al. 2021), where the ground truth spots in slices were mapped to their spatial positions and classified into six cortical layers (L1-L6) and white matter (WM), NA indicates spots that were not annotated.
39

---

Published as a conference paper at ICLR 2026
E.17 PREDICTED SPATIAL DOMAINS OF CELLAGENT ON DLPFC DATASETS
133870
01911
1997
13322
Figure 22: Predicted spatial domains of CellAgent DLPFC datasets. The figures present the predicted spatial domains of CellAgent across DI.PFC slices datasets, with spots colored according the inferred spatial domains.
40

---

Published as a conference paper at ICLR 2026
F SUPPLEMENTARY TABLES
F.1
THE OVERVIEW OF THE SC-OMNI TOOLKIT
Task Name
Task Category
Tools Involved
Removal of Low-Quality Cells and Genes
Preliminary Analysis
scanpy
Expression Normalization
Preliminary Analysis
scampy
Identification of Highly Variable Genes
Preliminary Analysis
scanpy
Dimensionality Reduction
Essential Analysis
PCA_scanpy, UMAP _ssappy,
L.SNE.canpy,
Neighbor Graph Computation
Essential Analysis
seappy, squidpy
Clustering
Essential Analysis
nouvain.scanpp leiden.scanpy. KMeans_scanpy.co
Cell Type Annotation
Essential Analysis
Celltypist, ScType, CellMarker2.00, etc.
Differentially Expressed Gene Identification
Advanced Analysis
wilcoxon scanpy, searp.. t-test stest_scanpy. ctc.
Batch Correction
Advanced Analysis
Harmony, scVI, Scanorama, etc.
Trajectory Inference
Advanced Analysis
PAGA Dyno, Slingshot Dyno, Scorpius Dyno, etc.
Imputation for Spatial Transcriptomics
Advanced Analysis
Tangram, SpaGE, novoSpaRe,
Spatial Trajectory Inference
Advanced Analysis
stl.carn
Identification of Spatially Variable Genes
Essential Analysis
squidpy
Compute Ripley's Statisties
Essential Analysis
squidpy
Spatial Domain Identification
Advanced Analysis
DeepST, SEDR, SpaGCN,
Co-occurence Probability
Essential Analysis
squidpy
Neighborhood Enrichment
Essential Analysis
squidpy
Table 2: The overview of the se-Omni toolkit. Each task is categorized into preliminary, essential, or advanced analysis, with representative tools or algorithms.
41

---

Published as a conference paper at ICLR 2026
F.2 OVERVIEW OF THE EIGHT MAJOR TASKS IN SCRNA- SEQ AND SPATIAL TRANSCRIPTOMICS ANALYSES
Task
Description
Purpose
Task
Batch correction
Remove batch effects while conserving biological variation.
Task
Preprocessing and clustering
Remove low-quality cells and genes, perform normalization, identify
highly variable genes, apply dimensionality reduction, and finally cluster cells with similar states.
Task
Cell type annotation
Identify the cell types of each cluster.
Task 4
Trajectory inference
Reconstruct the dynamic developmental trajectory of cells by ordering them along the pscudotime.
Task
Spatial neighborhood analysis
Reveal spatial relationships and interactions between cell clusters in the tissue organ.
Task
Spatially variable gene identification
Identify genes with distinct spatial expression patlerns in tissues or organs,
Task
Spatial transcriptomics imputation
Predict gene expression levels spatial transcriptomics data leveraging reference single-cell datasets.
Task
Spatial domain identification
Partition regions with similar gene expression patterns and tissue
structures into distinct spatial domains through spatial clustering.
Table 3: Overview of the eight major tasks in scRNA-seq and spatial transcriptomies analyses. These tasks include batch correction, preprocessing and clustering, cell type annotation, trajectory inference, spatial neighborhood analysis, spatially variable gene identification, spatial transcrip- tomics imputation, and spatial domain identification. The table also outlines the primary objectives of each task.
42

---

Published as a conference paper at ICLR 2026
F.3 OVERVIEW OF BATCH CORRECTION DATASETS
Batch Correction Task
Cell Number
Batches
Tested Features
Pancreas
batches
Widely used test data, protocols
Lung
42000
4 batches
Human variation, protocols. spatial locations. high resolution scoping. tissue types
Perirhinal Cortex
17353
2 batches
Brain region specificity, diverse gene expression profiles, precise cell-type annotations
Immune (human)
35206
batches
Tissues, laboratories, cell types
Immune (human and mouse)
97552
23 samples
Tissues, laboratories, similar cell types. species
Table 4: Overview of batch correction datasets. The table summarizes the key characteristics of the five datasets used for batch correction evaluation, including the number of cells and batches, as well as the tested features such as tissue types, experimental protocols, and laboratory sources.
43

---

Published as a conference paper at ICLR 2026
F.4
OVERVIEW OF CELL TYPE ANNOTATION DATASETS
Cell Type Annotation Task
Cell Number
Gene Number
Cell Type Count
PBMC
33694
Pancreas
2126
34290
10
Pancreas
8569
34290
Liver
64339
47951
27
Immune
33506
12303
Lung
32472
15148
17
Table 5: Overview of cell type annotation datasets. The table summarizes the key characteristics of the datasets used for cell type annotation evaluation, including the number of cells, the number of genes, and the number of cell types in each dataset.
44

---

Published as a conference paper at ICLR 2026
F.5 OVERVIEW OF TRAJECTORY INFERENCE DATASETS
Trajectory Inference Task
Cell Number
Gene Number
Trajectory Type
Aging-hsc Kowalczyk
493
2227
Linear
Human-embryos Petropoulos
1289
8772
Linear
Coll-cycle-hESCs Leng
1544
1770
Cycle
Hoanline-uuman-boie Guo
2202
8148
Bifurcation
Cell-cycle Buettner
264
6812
Cycle
NKT-ditfereeniation Miao
3999
7799
Multifurcation
Simulated-denne deliiiiiddddddiii---ell------ Shackel
4332
4158
Linear
sacrraafe-llyha---lllmmttttttt Zhang
322
6138
Linear
Table 6: Overview of trajectory inference datasets. The table summarizes the key characteristics of the datasets used for trajectory inference evaluation, including the number of cells, gene count, and the trajectory type (e.g., linear, bifurcation, cycle, or multifurcation) for each dataset.
45

---

Published as a conference paper at ICLR 2026
F.6 OVERVIEW OF SPATIAL DOMAIN IDENTIFICATION DATASETS
Spatial Domain Identification Task
Spot Number
Gene Number
Domain Number
Slice 151507
4226
33538
Slice 151508
4093
33538
Slice 151509
4784
33538
Slice 151510
4369
33538
Slice 151511
3861
33538
Slice 151512
3606
33538
Slice 151671
4221
33538
Slice 151672
3849
33538
Slice 151673
3692
33538
Slice 151674
3590
33538
Slice 151675
3632
33538
Table Overview of spatial domain identification datasets. The table summarizes the key char- acteristics of the DLPFC datasets used for spatial domain identification evaluation, including the number of spots, gene count, and the number of spatial domains for each slice.
46

---

Published as a conference paper at ICLR 2026
F.7 OVERVIEW OF SPATIAL IMPUTATION DATASETS
Spatial Imputation Task
Spot Number
Gene Number
Technology
Embryo
15476
33694
seqFISH (image-based)
Cortex
5240
10000
seqFISH (image-based)
Prefrontal cortex
8569
3429
STARmap (image-basod)
Embryo
6439
47951
seqFISH (image-hased)
Breast cancer
3326
16384
Visium (seq-based)
Kidney
3570
15148
Visium (seq-based)
Hippocampus
2044
3105
Slide-soqV2 (scq-based)
Table Overview of spatial imputation datasets. The table summarizes the key characteristics of the datasets used for spatial imputation evaluation, including the number of spots, gene count, and the technologies used (e.g., image-based seq-based)
47

---

Published as a conference paper at ICLR 2026
F.8 OVERVIEW OF DATASETS USED IN EXPERIMENTS
Dataset Number
Dataset Description
Dataset Source
Dataset
Human blood atlas
Xu al.
Xu
2023
Dataset
Human bone marrow utlas
Xu al.
Xu al
2023
Datasct
Human heart
Xu al.
Xu
2023
Datasct
Human intestine atlas
Xu al.
Xu al.
2023
Dataset
Human kidney atlas
Xu al.
Xu
2023
Datasct
Human liver atlas
Xu al.
Xu al.
2023
Datasct
Human lung atlas
Xu al.
Xu
2023
Dataset
Human pancreas arlas
Xu et al.
Xu al.
2023
Datasct
Human skelctal muscle atlas
Xu al.
Xt
2023
Datasct 10
Human spleen atlas
Xu et al.
Xu al.
2023
Dataset I1
Human pancreas
Luecken et al.
[necken et
2022
Dataset 12
Human lung atlas
Luecken et al.
Jnecken et
2022
Datasct 13
Immune cells (human)
Luecken et al.
[uecken ct al.
2022
Datasct 14
Immunc cells (human&mouse)
Luecken et al.
Luecken ct
2022
Dataset 15
Human perirhinal cortex
Siletti al.
Siletti ct
2023
Datasel 16
PBMC
Zheng Zheng al
201
Datasct 17
Human panereas
Baron al
Baron et al. 2016
Dataset 18
Human pancreas
Muram et al. Muraro 2016
Dataset 19
Human pancreas
Lawlor Lawlor al.
2017)
Dataset 20
Human panereas
Segerstolpe al.
(Segerstolpe al.
2016
Dataset 21
Human pancreas
xin et al (Xin et al.
2016
Dataset 22
Human liver
Guilliams al.
Guuiliams & Scoll
Datuset 23
Mouse retina
Macosko et
Macosko el al
2015
Datuset 24
Hypothalamic preoptic region
Moflitt al.
Moffitt
2018
Dataset 25
Mouse brain section (coronal)
Zhou et
Zhou et al 2020
Dataset 26
Mouse curlex
Tasic al.
Tasic et
2018
Datuset 27
Myeloid progenitors
Paul et al. (Paul et al
2015
Datuset 28
Human hindlimb muscle
McKellar (MeKellar al.
2020
Dataset 29
Human prostate
McCray et
(McCray el al
Datuset 30
Human
Zhou Zhou 2020
Datuset 31
Aging-hsc Kowalczyk
Saclens et al.
Saclens et al.
2019)
Dataset 32
Human-embryos Petropoulos
Saclens al.
Saclens
2019)
Datuset 33
Cellbench-SC luyitian
Saelens et al.
Saclens et al.
2019
Datuset 34
Cermline-human-bbee guo
Saclens al.
Saclens
2019)
Datusct 35
Cell-cycle Buettner
Saclens ct al.
Saclens ct al.
2019)
Dataset 36
NKT-differrnniationn engel
Saclens al.
Saclens et
2019)
Datuset 37
Siimuhaee-ddoatttiiccclll---- shalek
Saelens et al.
Saclens et al.
2019)
Pancreutio-ll Paccrutio-uphha--clll-mtutttttt zhang
Datuset 38
Saclens al.
Saclens
2019)
Datasct 39
DLPFC, slice 151507
Maynard ct al.
MMaynard ct al
202
Datuset 40
DLPFC, slice 151508
Maynard al.
Maynard et
2021
Datusct 41
DLPFC, slice 151509
Maynard et al.
MMaynard et al
2021
Datasct 42
DLPFC, slice 151510
Maynard Maynard
2021
Datasct 43
DLPFC, slice 151669
Maynard ct al.
(Maynard et al
202
48

---

Published as a conference paper at ICLR 2026
Datasct 44
DLPFC, slice 151670
Maynard (Maynard
2021
Datuset 45
DLPFC, slice 151671
Maynard et al.
Maynard et al
2021
Datuset 46
DLPFC, slice 151672
Maynard
Maynard
2021
Datasct 47
DLPEC, slice 151673
Maynard ct al.
Maynard ct al
2021
Datasct 48
DLPEC, slice 151674
Maynard
Maynard
202
Datusct 49
DLPFC, slice 151675
Maynard ct al.
Maynard ct a1
2021
Datasct 50
DLPFC, slice 151676
Maynard
Maynard et
2021
Datasct 51
Embryonic (scRNA-scq)
Li ct al.
(Li ct al
2022
Datasct 52
Cortex (scRNA-seg)
Li al.
2022
Datasct 53
Embryo (scRNA-scq)
Li ct al.
(Li ct al
2022
Datasct 54
Prefronnta cortex (scRNA-seq)
(Li
2022
Datasct 55
Breast cancer (scRNA-soq)
Li ct al.
(Li ct al.
2022
Dataset 56
Kidney (scRNA-seq)
Li (Li
2022
Datasct 57
Hippocampus (scRNA-scq)
Li ct al.
(Li ct al
2022
Datasct 58
Embryonic (scqFISH)
Li ct.
(Li
2022
Dataset 59
Cortex (SeqF)SH+)
Li et al.
(Li ct al.
2022
Datasct 60
Embryo (STARmap)
Li (Li
2022
Datasct 61
Prefrontal cortex (FISH)
Li ct al.
(Li ct al
2022
Datasct 62
Breast (Visium)
al.
(Li
2022
Dataset 63
Kidney (Visium)
Li di al.
(Li et al.
2022)
Datasct 64
Hippocampus (S)
Li (Li ct
2022)
Table 9: Summary of the datasets utilized in scRNA-seq and spatial transcriptomics analyses.
49


Reviews:
[{"review_id": "Um66ngQyx7", "reviewer": "Reviewer_szPk", "summary": "This paper introduces CellAgent, a multi-agent framework that uses LLMs organized into three biological Planner, Executor, and Evaluator to automate end-to-end scRNA-seq analysis, including preprocessing, batch correction, cell-type annotation, and trajectory inference. CellAgent combines a curated toolkit sc-Omni of domain methods, runs generated code in a notebook sandbox, and applies a self-reflective optimization loop.", "questions": "Could you release exact dataset lists (accession IDs), all preprocessing scripts or methods with random seeds?\n\nCould you specify the configuration or computational cost of the LLM components, including key parameters (e.g., temperature, tokens, API version, number of calls per task) and the estimation of the total inference cost and runtime for the experiments?\n\nWhat is the success rate of CellAgent? What will happen if it chooses the wrong tool? Have you analyzed the types of tasks or datasets where CellAgent fails or produces suboptimal pipelines?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. CellAgent automates single-cell and spatial analyses via natural language, which is a meaningful system that has clear practical value.\n\n2. The paper includes rich biological visualizations, enhances interpretability, and aligns with standard practices in single-cell and spatial omics.", "weaknesses": "1. CellAgent proposes an empirical engineering system by combining existing tools, but fails to elevate the framework into a principal scientific formulation. It does not clearly articulate why or under what principles the multi-agent coordination works.\n\n2. There is only one type of  LLM (GPT-4o) that both interprets results and decides which algorithm is best, which raises the risk of self-circularity. Masking algorithm IDs alone do not guarantee fairness. The absence of human-expert-LLM-related validation or multi-LLMs consensus undermines the robustness and objectivity of the reported performance claims.\n\n3. It’s unclear whether the framework ensures scientific validity, or simply produces metric scores and visual plots. Since it lacks expert-reviewed case studies or even a single example where the agent detects and corrects a biologically unreasonable output. In single-cell analysis, the former is not enough.\n\n4. The paper lacks ablation studies to justify the necessity of each component, or based on different LLM APIs apart from GPT-4o. Only plainly stating that it simulates the workflow of deep-thinking is not reasonable.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3CZLqGrtIh", "reviewer": "Reviewer_VQec", "summary": "The paper introduces CellAgent, a large language model–driven multi-agent framework for fully automated single-cell and spatial transcriptomics analysis through natural language interaction. The system integrates three agents, namely Planner, Executor, and Evaluator, operating under a self-reflective optimization loop that plans tasks, generates code, and evaluates results. A comprehensive benchmarking across over 60 datasets is also presented.", "questions": "I would ask the authors to address the weaknesses highlighted above:\n1) Please include biologically grounded evaluations, such as testing whether CellAgent recapitulates known spatial tissue structures in benchmark datasets. Walking the reader through one representative dataset out of the 60 would suffice\n2) Provide more details on the human expert evaluation, including participant expertise, evaluation protocol, and inter-rater variability.\n3) Please ensure that you exhaustively discuss the state-of-the-art.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1) The study presents a technically sophisticated and well-structured multi-agent framework t\n2) The inclusion of the sc-Omni toolkit provides a strong and practical foundation, consolidating many established analysis methods into a unified framework.\n3) The comprehensive benchmarking on multiple downstream tasks supports the robustness and practical relevance of the method.", "weaknesses": "1) The biological interpretation of the outputs is limited. The evaluation focuses mainly on computational metrics without assessing whether the inferred results align with known biological mechanisms.\n2) The evaluation of “human expert performance” lacks sufficient detail on the experimental design, number of participants, and reproducibility of human assessments.\n3) The author do not discuss the advantages / disadvantages of their framework against some other relevant systesm, most notably SpatialAgent:\nhttps://www.biorxiv.org/content/10.1101/2025.04.03.646459v1.abstract", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YQ4nkvY598", "reviewer": "Reviewer_FA5g", "summary": "The paper presents CellAgent, an LLM-based agent for the analysis of single-cell data. The system follows a three-step analytical pipeline comprising planning, execution (including tool selection and code generation), and evaluation. The framework is implemented as a natural-language chat interface through which users can upload single-cell or spatial data and perform interactive exploration. The paper also includes a benchmark comparing the agent against other computational utilities and a baseline LLM in terms of biological task accuracy and ease of use.", "questions": "Below I suggest modifications that can help improve the paper.\n\n1. Illustrate the novelty and efficiency of the proposed approach as compared to existing alternatives. How would **Cursor** on the one hand and **Biomni** perform the same task — are they worse than **CellAgent**?\n\n2. Illustrate the robustness of **CellAgent** to LLM hallucinations. Also, how would **CellAgent** behave when prompted with a complex task that includes multiple steps?\n\n3. The paper develops two code bases, **scOMNI** and **CellAgent**. While **scOMNI** is public, **CellAgent** is not. It is hard to evaluate the validity of the approach when the code for it is hidden. In **scOMNI**, I notice that many paths are hard-coded; this would be a major limitation to running the method locally.\n\n4. There are several unclear aspects in the paper. For example, how do you run other methods for the benchmark? How do you compute the *AS* score for spatial domain identification in embryo, breast cancer, and kidney datasets, which do not have ground-truth data?\n\n5. Cell-type identification is performed on a relatively easy dataset with coarse cell-type labels — extend it to more complex datasets with fine-grained labels and compare with other agentic approaches for cell typing, including a simple baseline where you give the top 50 marker genes to GPT-5 and ask it to reason over the data.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "Chat interface implemented at supported by the authors might be a helpful resource for people starting with single cell analysis.", "weaknesses": "This work is not the first attempt to automate single-cell analysis through agentic systems. Existing coding copilots such as Claude Code, OpenAI Codex, and Microsoft Copilot already support major single-cell analysis utilities, allowing users to analyze datasets locally and at lower cost. Moreover, domain-specific agents like Biomni offer broader functionality than CellAgent, while more specialized tools such as scAgent and Cell2Scale can perform key downstream analyses including cell-type annotation and exploration of cellular identity. The main distinction between Biomni and general-purpose copilots versus CellAgent lies in their flexibility—they are not constrained by a hardcoded set of tools, making them more adaptable to specific datasets and more easily extensible to new packages. \n\nCellAgent also does not implement any guardrails to control the LLM hallucinations or any procedures to inspect the quality of generated code that could potentially compromise the quality of the findings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ct6B6xNokZ", "reviewer": "Reviewer_ia61", "summary": "The manuscript proposes CellAgent, a large language model (LLM)–driven multi-agent framework designed to automate end-to-end single-cell RNA-seq (scRNA-seq) and spatial transcriptomics (ST) analyses through natural language interaction. The system includes three cooperating agents (Planner, Executor, and Evaluator) simulating a “deep-thinking” workflow.", "questions": "Here are several comments:\n1. While the proposed multi-agent structure (Planner–Executor–Evaluator) is conceptually neat, similar frameworks have already appeared in recent LLM-agent works such as AutoBA, AutoGen, and BioGPT-Agent. The manuscript does not sufficiently clarify what is fundamentally novel beyond combining these paradigms with existing scRNA-seq pipelines. The “self-reflective optimization” idea is interesting, but remains conceptually vague. It’s unclear whether this is an adaptive fine-tuning loop, reinforcement feedback, or a rule-based scoring system.\n2. The paper reports high success rates (96%) and better-than-human efficiency but provides limited methodological transparency. No details on how human expert baselines were defined or how evaluation bias was mitigated. Figures like Table 1 aggregate many metrics without error bars, variance, or reproducibility analysis.\n3. Although the system outputs biologically consistent results (e.g., trajectory inference of hematopoietic stem cells), it remains unclear how much biological reasoning actually comes from the model versus pre-existing toolkits (like Celltypist, Slingshot, Harmony, Tangram). CellAgent mainly orchestrates existing methods, rather than discovering new biology. Thus, the biological insights appear demonstrative rather than novel.\n4. The paper benchmarks CellAgent against individual tools such as scGPT, Tangram, SpaGE, etc., but not against integrated automated frameworks (e.g., ezSingleCell, scPipeline, or AutoBA). Moreover, it is not clear whether CellAgent used identical datasets and preprocessing steps. This could artificially favor its results due to built-in optimizations or curated defaults.\n5. The Related Work section is mostly descriptive and does not critically position CellAgent among existing AI agent frameworks in bioinformatics. The authors should explicitly discuss why prior LLM-based pipelines are insufficient and how CellAgent extends them.\n6. I have tried CellAgent online. Currently, it only allows the uploaded files that are less than 1GB. But lots of scRNA-seq data or single-cell-resolution ST data have larger sizes unless the analysis is based on the toy dataset. The authors should resolve this problem.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 2, "strengths": "The paper’s main strengths are its timely idea, an LLM-driven multi-agent framework for single-cell and spatial analyses, and its practical integration of many standard tools to automate end-to-end workflows via natural-language queries. The experiments are comprehensive, and the performance is better than the compared methods.", "weaknesses": "However, the novelty beyond tool orchestration is limited, and benchmarks lack rigor (unclear baselines, no variance/error bars). The system’s reliance on proprietary LLMs also weakens reproducibility, and the biological validation largely reaffirms known findings rather than yielding new insights.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)