SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Songhan Wang, Haoang Chi, He Li, Zhiheng Zhang, Jiayan Yuan, Cheems Wang, Hao Peng, Xinwang Liu, Wenjing Yang
• Venue: arXiv: cs.AI
• Published: 2026-07-08
• Collected: 2026-07-09
• URL: http://arxiv.org/abs/2607.07467v1
• PDF: https://arxiv.org/pdf/2607.07467v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers propose SpaCellAgent, an autonomous multi-agent framework that automates spatiotemporal analysis in single-cell transcriptomics. It improves efficiency by over 40% while maintaining expert-level performance. The tool is evaluated on six heterogeneous datasets and its code is available for public use.

What the Document Says

• The authors propose SpaCellAgent, a multi-agent framework that automates spatiotemporal analysis in single-cell transcriptomics.
• SpaCellAgent improves analytical efficiency by over 40% and maintains expert-level performance.
• It is evaluated on six heterogeneous datasets.
• The code for SpaCellAgent is available at https://github.com/LittleXH-shw/SpaCellAgent.

Key Concepts Mentioned

• Spatial and Single-cell transcriptomics
• Trajectory inference
• Autonomous multi-agent framework
• Large language model (LLM)
• Spatiotemporal analysis
• Narrative generation
• SpaCellAgent

Claims Made by the Document

• SpaCellAgent automates end-to-end spatiotemporal analysis and narrative generation.
• SpaCellAgent demonstrates over 40% improvement in analytical efficiency.

Evidence Used

• The text states that SpaCellAgent is proposed to automate spatiotemporal analysis and generate narratives. It also states that the tool consistently demonstrates over 40% improvement in analytical efficiency.

Methods / Approach

• Multi-agent architecture for strategic workflow planning
• Dynamic tool-orchestration engine for adaptive algorithm selection
• Self-evolution module

Limitations Stated by the Authors

The paper does not provide information on potential limitations according to the authors.

Limitations Noted by the Agent

Agent interpretation — not from the source.

Potential limitations may include the accuracy and robustness of the LLM in handling various complexities in spatiotemporal analysis, the ability to handle large datasets efficiently, and the generalizability of the results across different domains.

Open Questions

• How does SpaCellAgent handle various complexities in spatiotemporal analysis?
• Can SpaCellAgent handle large datasets efficiently?
• Is the performance of SpaCellAgent generalizable across different domains?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide detailed information about these potential limitations and questions. They can be explored further through experimentation, validation, and comparison with other tools.

Raw Abstract / Excerpt

Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell developmental paths, trajectory inference (TI) is critical. However, existing methods require extensive manual intervention and proficiency in heterogeneous tools, posing a significant barrier to efficient TI analysis. To bridge this gap, we propose SpaCellAgent, an autonomous large language model (LLM) multi-agent framework that automates end-to-end spatiotemporal analysis and narrative generation. SpaCellAgent utilizes a multi-agent architecture for strategic workflow planning, a dynamic tool-orchestration engine for adaptive algorithm selection, and a self-evolution module that iteratively refines performance through feedback. We evaluate SpaCellAgent on six heterogeneous datasets encompassing complex temporal developmental trajectories, diverse sequencing platforms, and spatially-resolved tissue architectures. SpaCellAgent consistently demonstrates over 40% improvement in analytical efficiency while maintaining expert-aligned performance. By converting natural language specifications into optimized analytical workflows and fully automating the pipeline, SpaCellAgent democratizes advanced spatiotemporal modeling and establishes a scalable, agent-driven paradigm for computational biology. The code and materials are available at https://github.com/LittleXH-shw/SpaCellAgent.